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1 ALL SWANS ARE BLACK IN THE DARK HOW THE SHORT - TERM FOCUS OF FINANCIAL ANALYSIS D O E S N O T S H E D L I G H T O N L O N G T E R M R I S K S

2 TRAGEDY OF THE HORIZON - TERM FOCUS OF CAPITAL MARKET EXPLORING AND ADDRESSING THE SHORT ACTORS TERM TO SECURE A MORE SUSTAINABLE ALLOCATION OF CAPITAL FOR THE LONG - A 2 INVESTING INITIATIVE & GENERATION FOUNDATION PROJECT: ° Foundation : 2 ° Investing Initiative PROJECT The Generation The have formed a multi - year THE ABOUT & explore and address the ’Tragedy of the Horizon ' , describing the potential suboptimal allocation of partnership to - the - term due to the limited ability of the finance sector to capture long - term risks within short long capital for risk assessment frameworks . The project aims to - artificial and natural factors that compress the term assess of market horizons such that long - term risks — ’travelling’ from physical assets through to asset owners players, and — get mispriced . Such a mispricing of long - term risks creates a 'void' between the assets and managers of mismatch - term asset owners and can eventually amount to an asset - liability liabilities . long of - year project include : Stages the 3 for chain, 1. the across horizons time quantifying by debate the Informing investment of mandates owners, asset of liabilities the managers, respect with example, asset to 1 turnover, when of credit, equity portfolio maturity time periods analysed by analysts 8 4 horizons performing cash flow calculations, time discounted of risk models, backward - etc looking/forward - looking time horizons of data, and the lifetime . ) . of assets, industrial risk 2. unintended consequences of the management practices resulting Identifying - short to term frameworks, including barriers from the transmission of long - term risk ; signals and the implications for efficient and productive capital allocation financial 3. in partnership with the two key stakeholder groups, responses Developing of asset long policymakers for horizon, the and tragedy the overcome to owners, - term example, well as tools, and products practices, management risk reporting, addressing by frameworks policy . as other with engaging team, research Initiative Investing ABOUT 2 the by developed is project The : AUTHORS THE ° research rating researchers, equity consultants, investment including key and stakeholders, organisations agencies, etc . The authors of this report regulators, Mona Naqvi, Brendan Burke, Svenja Hector, Tricia are report from The inputs from Daniela Saltzman Stan and Jamison, . Initiative) Investing ° 2 benefited ( é Dupr ) (Generation Foundation . 2017 Published February For more information, please visit: We are interested in your views on this report and welcome collaboration. www.tragedyofthehorizon.com : contact Main Naqvi Mona | York New | Initiative Investing ° 2 [email protected] 2 degrees - . org investing nd 205 (USA) : Street, 10017 York New NY, USA E 42 London London (UK) : 40 Bermondsey Street, SE 1 3 UK UD, France Paris (France) : rue LaFayette , 75010 Paris, 97 a, 6 Kupfergraben Am Germany : (Germany) Berlin Berlin 10117 2

3 ABOUT THE AUTHORS to working tank think profit - for - not align is ) ii ° 2 ( Initiative Investing ° 2 a C ° 2 the with sector financial the goal and long - term investing climate Initiative . offices in Paris, needs With the York, New and Berlin London, network global a engages including members, and partners 40 over of financial investment researchers, asset managers, institutions, policymakers, research academics and NGOs . Our work institutions, focuses - three pillars of finance primarily metrics and tools, on the processes, investment regulation ; the Tragedy of and financial Horizon project informs all three . Foundation alongside Generation was established Generation The to strengthen order in Management Investment the case for Sustainable vision Capitalism . Our strategy in pursuit of this is to mobilize asset owners, in participants key other and companies managers, asset Sustainable of for case business the support in markets financial Capitalism All capital allocate to them accordingly persuade to and of . the activities of a the Foundation, a not - for - profit entity, are funded by annual . of Generation distribution IM's profitability This an benefits immensely from intense engagement with the : ENGAGEMENT financial industry . As INDUSTRY report well part our research, we conducted workshops and seminars as essential as interviews and a survey for equity research of the by expressed views the via consulted analysts organizations reflect necessarily not do report this of conclusions The . workshops final the in the in report feedback their integrate to possibility the them offered we However, . interviews and form of anonymized quotes . seminars 2016 (in workshops, and industry engagement Concluded : ) conferences, th City Equity New in Horizons Time on Workshop : 2016 August, York Research, 18 rd August, London 23 S&P, Analysis, Credit in Horizons Time on Workshop : 2016 th Equity August, 2016 : Workshop on Time Horizons in 24 Research, London (co - hosted with The Generation Foundation) st Credit August, 31 2016 Workshop on Time Horizons in Equity Research and s) Analysis, ’ Paris (co - hosted with Moody : rd st : 2 ° ii Seminar on 2 D Stress Testing & The Paris Agreement, Corsica 2016 September, 1 3 - th 15 September, 2016 : Conference on Time Horizons in Financial Analysis, Paris th in City Moody s, New 9 York Analysis, Credit ’ Horizons Time on Workshop : 2016 September, st New Analysis, Financial in Horizons Time York Conference : 2016 September, City 21 on analysts included Participants : organizations following the from Initiative Barclays Research, Bank of England, Blackrock, Carbon Tracker Equity , Citi Research , Comgest , Edmond de of Rothschild Group , Etho Capital, Exane BNP Paribas, French Society Financial Analysts , Generation IM, Grizzly Reponsible Cheuvreux Investment, Morgan Chase , Jupiter Asset Management , Kepler JP , Moody ’ s Investor Service, Métropole Gestion , The Pensions Trust, S&P Oddo Asset Management Meriten , Morgan Stanley, MN, MSCI, Natixis Global Asset Management, UBS , Management Asset Sycomore , Management Capital Insight Sustainable , Ratings Global . America Management Wealth other Disclaimer text, artwork, photographs, layouts and All content and associated intellectual property rights, : including copyright, and are owned by The Generation Foundation or used with permission . The content is provided to we you general, non - commercial purposes only . Although for endeavour to ensure that the content is accurate and up from to date, The Generation Foundation accepts no responsibility for information on reliance loss arising damages or is Unless this document . otherwise stated, all content in licensed to you under the Creative Commons contained Attribution - Non - Commercial 3 . 0 Unported licence (http : // creativecommons . org /licenses/by - nc / 3 . 0 /) . This means that distributing, you free to share the content by copying, are and transmitting it, and you may produce derivative works Generation to The it always must you and it of use commercial make not may you but content, the from attribute Creative Commons Attribution - Non - commercial . Foundation 0 Unported . You must always make reference to the 3 copyright licence with your use information licensing other or notices proprietary or of other any retain and Content the Foundation specified by The Generation . , “heavy fog in the foothills at night,” 2009 Cover Image: Jeff Ruane 3

4 TABLE OF CONTENTS PART III: HOW FINANCIAL ANALYSTS EQUIP 5 EXECUTIVE SUMMARY 37 INVESTORS WITH LOW BEAMS Equity and Credit Analysts in the Investment 3.1 38 Allocation Chain PART I: WHITE SWANS MAY LOOK BLACK IN THE 7 DARK 3.2 Financial Models are the Predominant Tool in 39 Equity Analysis 1.1 Analysts in the Investment Chain 8 40 3.3 Equity Research is Blind After 5 Years 8 1.2 The Role of Equity Research - 3.4 How Short termism Is Embedded in Valuation 1.3 Equity Analysts Swing Between Accuracy and 41 9 Models Inaccuracy 3.5 Analysts’ Explicit Cash Flow Forecasts Focus 10 1.4 The Role of Credit Rating Agencies 44 Only on the Next 5 Years 1.5 Ratings are Generally Accurate and Stable 10 44 - 3.6 Risk Sections Are Not Long term Accuracy and Stability Depend on an 1.6 Optimal 11 Risk Assessments Largely Focus on the 3.7 Credit Investor’s Time Horizon 45 - Next 3 5 years 11 term Risk Assessment - 1.7 Analysts and Long 3.8 Emerging Risks and Trends Beyond 5 Years Are 46 1.8 Not All Risks are Incorporated in Financial Unlikely to Impose Ratings Action Today 12 Models 3.9 No Evidence of a Clear Framework to 48 1.9 White Swans that Appear Black in the Dark Distinguish a ‘Kodak’ from a ‘GE’ 13 Often Get Missed by Financial Analysts Feedback from Industry Engagement Workshops: 50 1.10 White Swans in the Dark Are Common and How Analysts Equip Investors with Low Beams 14 Actionable 1.11 15 Our Taxonomy of White Swans in the Dark PART IV: THE DRIVERS BEHIND THE LOW BEAMS 51 Long Time 1.12 White Swans in the Dark May Have Face Multiple Obstacles to a Long - 4.1 Analysts 16 52 Horizons Term View 1.13 Examples of Megatrends That May B e on the Short - 53 4.2 Company Disclosures Focus term 17 Mispriced 4.3 ESG Factors Overlap With Long - term Risk 53 - Risks Affected term 1.14 Case Studies: How Long 18 55 4.4 Developing New Valuation Methods Capital Markets in the Past 4.5 Assessing Adaptive Capacity 56 - Risks could term 1.15 Future Risks: how Long 24 Place Limits On Long 4.6 Research Budgets - term Affect Capital Markets 57 Risk Assessment Feedback from Industry Engagement Workshops: 28 4.7 A Lack of Demand for Long - term Analysis Drives White Swans in the Dark 58 - termism in the Analysis Short Feedback from Industry Engagement Workshops: 59 II: PART TERM - THE RATIONALE FOR LONG The Drivers Behind The Low Beams : EXPOSING THE WINDOW OF ANALYSIS 29 MATERIALITY CONCLUSIONS AND DIRECTIONS FOR V: PART 61 2.1 Many Asset Owners Have Long Investment FURTHER RESEARCH 30 Horizons 62 onsequences C 5.1 Term - 2.2 Company Value Is Mostly Based on Long 31 63 5.2 Directions for Future Research Cash Flows 5.3 Mapping Potential Solutions 64 33 term Risks 2.3 Companies Are Exposed To Long - 66 5.4 Conclusions - 2.4 Company Value Relies on Long term Intangible 34 Assets ENDNOTES 67 35 2.5 The Bond Market Is Exposed to Long - term Risks GENERAL INDUSTRY FEEDBACK 69 4

5 EXECUTIVE SUMMARY Academic calls ‘Black swans’ financial risks that are unpredictable . Our research suggests that literature predicable non non - linear, long - term risks are actually cyclical, ‘white swans’ . They are left in the certain - by the ‘low beams’ of financial analysis that focus on the next 1 - 5 years . dark . a analysis is ‘calibrated’ on 1 specific time horizon . Financial analysts provide a target price Financial or Given the risk of default (bonds) . (equities) the variability of these metrics over time, analysts rate if to their recommendations over a specific timeframe adjust they want to maximize accuracy . need Equity research analysts usually provide a 1 year target . Credit rating analysts, on average, change 1 / 3 of their investment ratings over a 3 year period . Our analysis suggests that analysts currently grade that their on a 1 to 3 calibrate time horizon : they value the risks analysis are likely to impact the year cash flows of the issuers within this timeframe . 2 . Non - linear, non - cyclical, long - term risks likely to get missed . Risks that are unpredictable are examples categorized swans’ in academic literature . Past ‘black such as the subprime crisis or the as more recent VW emissions fraud suggest that some of these ‘black swans’ might actually be predictable non but by financial analysts due to their long - term, missed - linear, non - cyclical profile . In other words are these swans Current ‘white’ but ‘left in the dark’ due to the short term focus of financial analysis . examples of that are likely to get mispriced include energy transition risks and the disruptive such risks artificial transportation and automation for services and of . impact intelligence with Material on long . term investors’ returns . Investors impact long - term liabilities, such as pension 3 - and insurers, are supposed to optimize their return on a 15 - 30 year horizon . The net present funds - of is usually based on the ‘expected’ long portfolio term cash flows generated by the issuers their value stocks and bonds . Indeed, in many sectors, the value of companies is based on long - term assets such of infrastructure power plants, oil reserves and our . Based on as analysis, we estimate that about 80 % of . the value of a long - term investor’s portfolio is based on cash flows expected after 5 years present net - horizons in long Figure 1: Time term portfolio management investors’ 30 year horizon Long - term investors (are supposed to) optimize their returns over a 15 - Fund managers hold stocks for 1 3 years - Analysts provide a target price for 12 months 5 years - Analysts forecast issuers’ cash flows over 1 and then extrapolate , 2/3 of this portfolio’s Net Present Value is based on the (expected) long - term cash flows Discounted cyclical, linear non that - Non will risks only period forecast the after materialize and get likely to missed analysts by are cash markets therefore mispriced by FORECAST EXTRAPOLATION flows PERIOD PERIOD 2045 2030 2040 2035 ... 2016 2020 2025 Energy related equities (utilities, energy, transport, materials, real estate...) Energy - related bonds (same sectors) - equities Finance sector bonds Sovereign & other public sector bonds Other income Source : 2 ° ii 2017, representative institutional investor portfolio, 40% equity, 60% fixed - 5

6 SUMMARY CONTINUED EXECUTIVE RESEARCH APPROACH models 4 on cash flow forecasts for Analysts’ are . based The research is based on a mix of 5 In this report, the core part of the next the 3 years - . quantitative and qualitative analysis. focuses side equity used research - the by sell on models Most figures are based on third party analysis research analysts and credit rating agencies . Our research, both academic and non - rely analysts both in financial past on suggests that cases, data from academic as well as market rare cases and forecasts for the data 3 to 5 years . In next Bloomberg, S&P, Thompson Reuters, for sectors with very stable cash flow profiles this etc.. Our own quantitative analysis . extend up to period this After forecast years 11 - 7 can focuses on the breakdown of equity period, are the expected future cash flows of issuers , and the length of time period by NPV that risks extrapolated Therefore analysts only price the . the forecast periods based on them or likely to impact impacted issuers in the past are Morningstar DCF models and the period forecast during . - income data. We also Bloomberg fixed on equity quote the results of a study - analysis Long 5 methodological faces financial term . portfolio turnover, based on Mercer but obstacles . demand from of investors lack a also proprietary data and Morningstar the - short the behind term on interviewed drivers When funds data. The qualitative analysis is highlight analysts obstacles, methodological the focus, - side research based on a review of sell of forward - looking data reported by attributing the lack Agencies’ papers, Credit Rating justifying on short term by the and issuers, focus the methodologies, as well as to to any risk in companies long - ability the of adapt most engagement with practitioners (see and divestment through ( acquisitions, term innovation, and p 70). below, page 6 . closer look also reveals that etc the . ) However, a for analysis demand is heavily driven by short - financial term investors traders, and that even long - actually term FEEDBACK FROM PRACTITIONERS short relatively A assets their trade sister . horizons with Based on a discussion paper, our team developed in partnership with that shows study Mercer buy - side side and engaged with sell - long equity hold assets for term . 7 managers years on - 1 equity research analysts as well as . During the interviews, most analysts agreed that average credit analysts via a survey, interviews, of demand alone can explain the lack of long - this lack and workshops. The large majority of term . analysis feedback confirmed our findings. The caveats are: three main We analysis? . Developing 6 long - term ‘alternative’ Some equity analysts blamed us for • that can obstacles methodological the better conclude be much credit to DCF models, too giving sectors with long addressed assets like power, In . tem since in most cases analysts just use data avenues include the use of physical asset level to DCF to justify a price set based on the extension of - locked the assess better in effects, the peers’ estimates and market price. forecast more period, in a forward looking approach and • A strong minority of analysts also related - the calculation of the risk premium . The climate questions our optimism regarding the : risks are currently the main focus of attention the ability to overcome methodological Financial to Stability force task a established Board obstacles and uncertainty in general. a the EC finances and research project options, explore • Finally, one CRA explained that it 2 ) ° ii (led to develop an open source methodological by seeks to incorporate all risks into . However, moving forward, lack framework of the ratings, whether long term or short - remain demand from investors will a key obstacle . To looking term, with the most forward - measures address it, the report identifies both voluntary view that visibility based on the - . g . long (e term valuation and ratings alternative availability of data into these risks a by commissioned and or investors of regulators), pool permits. We however did not find actions (e . g . mandatory long - term - public policy risk enough evidence supporting this view disclosure) . Both dimensions are currently and analysis to modify our conclusions. . risks related - climate for experimented or/and discussed 6

7 PART I WHITE SWANS MAY LOOK BLACK IN THE DARK SECTION SPOTLIGHT they analysts very accurate when markets are • but are tend to miss their price targets Equity calm more than 50 % in volatile by . markets • - off Credit ratings have historically been very good signals of default; however, ratings must trade - accuracy and stability, and the trade off point chosen by credit analysts may not adequately transmit risk signals most relevant to different types of investors. Credit and equity analysts may • missing long - term, non - linear risks . be • Long - term risks, in particular those with non - linear and non - cyclical risk profiles, are likely to get focus missed analysis due to the short - term financial of current risk and valuation models . by • In light of these long - term future risks, long - term investors are potentially exposed to mispriced, financially threats . material • The subprime crisis was a case in point . Disruptive trends such as the transition to a low carbon financial economy raise the attention of currently regulators and intermediaries themselves .

8 of ANALYSTS their but services CHAIN range a THE IN offer analysts These 1 . 1 INVESTMENT make value the about judgments to is role principal a or financial assessing in role key risks play Analysts creditworthiness of investment securities, which assessment undertaking - risk fundamental comprises opportunities and for investors . . necessarily By fundamental and valuation analysis of Because risk offer a range of products and analysts individual securities, analysts performance companies services, their key and indicators may vary . However, their job their doing are analysts if provide recommendations and offer value judgments investors give the of sense accurate judgments that an help investors determine how to . risks function specific Their . capital their allocate relies economy real the by posed opportunities and how Precisely flow can analysts : model business their on provide the work of analysts facilitates this equity services act or companies, to investors, and debt between vary will information of This markets paper seeks to determine providers . This paper . whether party - third as independently and their role addresses analysts provide the investment community four key types of with analysts the investment chain (see Fig . 2 of prospects future the about information for more enough within securities information) . cover they the companies or : - : Research Equity Side Analysts side - Sell • Sell 1 RESEARCH EQUITY OF ROLE THE 2 . independent research analysts undertake equity research the value companies and estimate to analysts role is The of equity to accurately forecast 'fair value' of listed equities, ultimately of prices by assessing the fundamental value stock ‘buy, sell or hold’ recommendation . a prescribing . Equity analysts offer companies recommendations decisions investment stock on . buy - side and Both Analysts : - Buy • Buy - Side Equity Research side earnings produce analysts side - sell and estimates equity analysts undertake the same research . provide investment recommendations to investors directly behalf of type of research but fund on can frequently These recommendations change . managers should information so on be accurate based new in . term - near the analysts • Credit Rating Analysts : Credit rating and of the assess debt issuers creditworthiness nondisclosure The . side - sell the on focus Our of judgments about their ability to repay any make side buy the - by - sell makes side recommendations . debt outstanding obligations and recommendations accessible more much our over time and comparable is the focus of thus & • Environmental, Social, Research Governance revealed, however, analysts of . research survey Our Analysts the risks and cover analysts 'ESG’ : both that and - buy the both by used models valuation the opportunities associated with environmental, - . To that extent, our analysis of side sell do not differ social and governance be may which factors, applies to both groups, though the valuation models either short - or long - term . vary business the constraints model . - Source: Authors 2017 Fig. 2: Primary Investment Allocation Analyst Types and Roles Role Users Fee Structure Type Product Value companies and Research reports with Equity e Revenues based on stimate ‘target price’ side buy/sell/hold - Sell of equities vs. market investors transactions recommendations value Same as above but In - house trade In - house fund No fee since - internal Buy directly on behalf of side managers recommendations asset managers Assess ability of debt issuers (up to Debt Paid by Credit ratings issuers to repay debt Credit rating investors 21% of deal volume) obligations Cover risks and Sell for fixed rate or as opportunities w/ Investors a % of basis points of ratings ESG ESG ESG interest with ESG associated fund factors 8

9 3 ACCURACY EPS Estimate Standard Fig. 3 BETWEEN SWING 1 . : Ratio of Analyst EQUITY ANALYSTS 500 Earnings Per Share to S&P Deviation AND INACCURACY Analyst price targets exhibit a herding effect by Equity miss their targets analysts 10 % two - thirds by the of of the time, and third more than 20 % one - revisions estimates . With regular time of on based EPS Coefficient of Variation new information, analysts privilege accuracy over 4% stability but often miss their targets . A survey of targets from price approximately 170 , 000 sell - side are 2012 suggests that they to typically within 1999 3% 1 months of the actual price after 12 . (see Fig . 4 ) 20 % Aggregate price targets fell within 20 % of the actual 9 price in the out of the 14 years in the sample . In 2% years, however, analyst targets missed the five other more by . than 20 % mark Figure 4 reveals an inverse when relationship with accuracy and the S&P 500 – 1% the when and high, miss analysts the market plunges, market analysts miss low . This suggests that booms, some events are not anticipated by analyst models . 0% 2011 2006 2010 2008 2007 2005 2004 2003 2002 2001 2000 2009 pressure peer Sell - side analysts are subject to . earnings - Across the sell - side industry, analysts offer 2016 Ycharts 2012 and Papakroni Source: Authors from 6 12 These per months - - ahead forecasts (EPS) share . on based price share ‘rational’ the approximate the The frequent inaccuracy of targets implies often company fundamentals . Analysts of estimates may routinely be getting misleading and market other, each to close very are that estimates offer . analysts Since unstable signals tend to herd around industry the a exhibiting herding effect around periodically inaccurate estimates, investors has shown that analysts seek to average . Research sometimes - near the on guidance poor have term approximate the industry mean because of the risk of companies of prospects their Though . revise analysts 2 being an incorrect outlier . Anecdotally, in one 6 12 months, to cannot rely estimates every investors us the starting point for his workshop an analyst told on on are, Analysts . accurate be to them average, forecast . analysts other is prices target the model of likely and year 1 over prices actual from % 16 off estimates 1999 , the standard deviation of only Since farther over longer timeframes . This off means that once USD $ 0 . 20 exceeded in figure low a , 2009 not do analysts routinely to risks major anticipate considering S&P average EPS of an 500 stock has the yet their target prices and offer them anyways . Thus, . 3 ) been $ . 33 over 14 that same timeframe (see Fig . not do analysts fail to only a stable view of a give usually % 68 that suggests This within are analysts of fall - the in value company’s term, they short also long 1 . average industry the of % recommendations . accurate providing regularly of Providing with less views term - long stable more frequency serve to reduce this problem . might - side Price Targets and Actual Prices 12 Months Later Difference between Sell 4: Fig. optimistic often highly are Analyst price targets S&P 500 Returns Difference between Target Price and Actual Price Index Value S&P 500 between Target Price and Actual Price Difference 1,600 60% 40% 1,400 20% 1,200 0% 1,000 -20% 800 -40% 2003 2004 1999 2006 2007 2012 2011 2010 2009 2008 2000 2005 2001 2002 2016 Quandl & You 2015 and Dechow Source: Authors from 9

10 1 4 OF CREDIT RATING AGENCIES . THE ROLE Fig. 5: S&P Weighted Average One - Year Global Default Rate, 1981 - 2015 the creditworthiness Credit issuers analysts of judge Investment Grade corporate bonds rarely default are Credit . debts their ratings to ability their – repay of the relative credit risk of issuers or a measurement INVESTMENT GRADE SPECULATIVE GRADE rather within capital the universe, debt products 45% 3 an than default . probability the of assessment of agencies also disseminate Beyond ratings, ratings information about companies and securities but 30% rely ratings investors such, As are output main their . rating on downside relative the assess to agencies certain institutional risks to their capital . Banks and 15% investors supplement these ratings with in - house risk analysis . credit 0% GENERALLY 1 . 5 RATINGS ARE AND ACCURATE CCC/C B BB BBB A AA AAA STABLE Credit Rating Source: S&P Global Ratings 2015 to signal by ability Ratings are evaluated their default . The accuracy of credit ratings is traditionally A . defaults they well how by signal determined Fig. Year - Moody’s Historical Average Three 6: accuracy ratings absolute high with system has low Corporate Default Rates investment of default in the top rates (e . g . categories Investment Grade corporate bonds rarely default and high default rates in the lowest categories grade) grade) (e . g . below investment The . rating agencies INVESTMENT GRADE SPECULATIVE GRADE accuracy a also relative define as the ability of ratings 60% distinguish from non - system to defaulters 4 accuracy Perfect . defaulters relative implies a credit those perfect ranking of issuers by in risk only ; 45% . the lowest categories default 30% . been have ratings credit Historically, accurate between relationship strong There is a inverse 15% default as rates credit by and agencies the defined 5 default the rate Figs (see ratings S&P, For 5 . ) 6 and . 0% Moody’s default of probability . the as calculated is Aaa Aa Ca_C Caa B Ba Baa A a function of default rate is the expected credit loss, Credit Rating both the probability of default and the expected loss - triple Moody’s, and both S&P . occurs default if A For Source: Moody’s Comprehensive History of the corporates essentially never default, and default Performance of Moody’s Corporate Credit Ratings 2015 the scale . Measures of rates increase as down that most defaults accuracy relative demonstrate 6 matters investor’s Stability debt . From perspective, a . rated - lowest the among categories occur not the ratings is important . Most do stability hold of upon so their maturity, to bonds returns depend and - are generally stable . Ratings 1981 S&P , 2015 Over rating Credit . market secondary the in bonds selling categories kept 70 % of ratings across all unchanged . Credit riskiness relative in change a signal changes of investment grade time, the issuers % 85 ; year each prices will adjust spreads, and yields, therefore bond . 7 maintained year their rating , next 1 later (see Fig new investors compensate to for assuming this level Alternatively, . page) metric volatility Moody’s ratings, investment within this grade . risk of Even of gross number rating notches that the measures substantial rating Further, . be can spread credit credits year each roughly — moved average, on have, beyond bond can have financial changes ramifications for investment / grade and for speculative 1 1 / 3 2 to sell bonds prices . Asset managers may be required , 8 issuers (see Fig . grade next page) . Interpreted as a set go often that below investment regulators ; grade implies turnover grade investment that this rate, of riskiness the on based requirements capital an issuers years on average move one notch every 3 7 assets ultimately Both of these institution’s . impose while speculative grade issuers move one notch 8 . on costs investors . every 1 . 8 years 10

11 AND ACCURACY OPTIMAL 6 . 1 STABILITY ON DEPEND Percent S&P of Credit Ratings that Stay the 7: Fig. AN TIME HORIZON INVESTOR’S 2015 - Same over 1, 3, and 5 Years, 1981 Over 5 years most S&P ratings are likely to change - off is a trade There between accuracy and stability . 5 Year 1 Year 3 Year Prioritizing requires timeframe accuracy over a short implies frequent changes and less stability . Both INVESTMENT GRADE SPECULATIVE GRADE 100% Moody’s and S&P change their ratings to reflect new S&P, information . For ratings 1 / 3 of investment grade 75% change 3 years and ½ change after 5 after . years Unsurprisingly, speculative grade ratings change even 50% more frequently At the other extreme, if a rating . agency to prioritize stability over the maturity of were 25% long - term bond (e . g . 30 years ), it would be at the a of expense of new information ongoing integration therefore and . Rating agency reduce would accuracy 0% have noting that this publications tension, identified BBB B BB A AA AAA CCC/C Credit Rating ratings possible accuracy while be may to “it increase Source: S&P Annual Global Corporate Default Study and substantially, ratings reducing, and perhaps stability” Rating Transitions 2015 to worthwhile be even may it purposes some “For also 9 for greater stability” . trade Our away some accuracy - Moody’s Historical One 8: Year Corporate Rating Fig. that understanding is trade between off - the optimal Volatility the will accuracy and stability day at of end the Moody’s credit ratings are stable credit depend on the horizon of the investor . Since only term - ‘long rating a gencies single provide a rating’ SPECULATIVE OVERALL INVESTMENT time per issuer, we assume they select an implicit GROUPING GRADE (SG) GRADE (IG) . to horizon calibrate the review the During trade off - ating phase involving executives from credit r Average Gross of them with this framing , gencies a some agreed Notch Movement it . while others challenged ) (see page 46 0.75 . TERM RISK LONG AND ANALYSTS ASSESSMENT - 1 7 0.50 to which This study extent seeks the understand to aligned analyst . are While horizons time investor and ‘window of materiality’ inevitably varies the relevant 0.25 typical by investor, it’s worth examining whether the of timeframes of analysis and the exposure investors diverge . A natural follow - up question is whether of horizons make certain types misaligned risk to likely 0.00 B A to get missed? Failure capture these risks in analyst IG Ba All Aa SG Caa Baa Aaa Ca_C a could potentially assets, imply models mispricing of Credit Rating a suboptimal of for capital allocation amounting to Source: Moody’s Comprehensive History of the broadly, term in . the investors, and society more - long Performance of Moody’s Corporate Credit Ratings 2015 S&P 1 - year Default Rate, 1989 - 2015 9: Fig. Over the past 30 years, default rates have spiked every 10 years during times of crisis 12% 8% 4% 0% 1997 2011 2007 2005 2003 2001 2015 2013 2009 1995 1993 1991 1989 1999 Source: S&P Annual Global Corporate Default Study and Rating Transitions 2015 11

12 1.8 Gray and White Swan events may be incorporated NOT ALL RISKS ARE INCORPORATED IN FINANCIAL Like their financial models, albeit at some cost . into MODELS black counterparts, gray and white swan events are not all, events contributing to . but financial also financially material to investors Some, when they occur prior may be predicted and thus managed market losses that such likely, more much typically are they Yet, experience to it easier predict them and include before the fact . While some events that induce makes Swan to too are losses market financial events idiosyncratic White . models assessment - risk in are be can and analysts financial to visible clearly reasonably predict, there are cases where a longer time assessed Gray analysis by financial analysts depth - in more or horizon at fairly reasonable cost . Swans are also have better would predictable but equip a potential manage to investors higher level of uncertainty than wans events wans S hite W Gray S . thus requires disruptions . Taleb categorized unpredictable Spotting certainty in fall that the their result, a As . resources and time additional ‘unknown unknown’ realm of 10 of financial modeling is often justified on exclusion black a from notion Taleb’s on Building as ’ . swans ‘black event, swan cost - benefit grounds . been since have swans white and gray for Any failure to predict and plan contrary that, events specify to conceptualized on links Swans White road ahead, therefore, the often their to in black relatives, are predictable and thus than analysis the swan defect a to back of the cost rather 11 characteristics or analysis . 10 extent an to manageable ) . (see . itself event the Fig of The ahead predict to impossible nearly are events Swan Black road the on Swans White of visibility headlights . on the strength of the analyst’s depends and incorporate into financial models . Taleb defines driving like is research financial looking Forward - at Black Swans as low probability events potentially with A astronomical headlights Commonly cited examples their as far as see only can driver impacts . night . terrorist / 9 the or internet the of rise the include 11 shine . For an analyst, the analytical method is like a Black . attacks headlights car’s . analytical the ahead farther The risk unprecedented generally are Swans or analysts of events, typically defying of have combinations visibility more the looks, method potential Black Black . evidence empirical on based expectations Swan risks ahead . Swans can appear on the uncertain to reasonably the of brightness the so warning are no with events therefore too road ahead not that the to models, financial into incorporate headlights does extent increase their visibility (see Fig . 10 ) . to investors are largely inevitable when losses (or gains) But White Swans can be seen in advance with occurs . event an such sufficiently bright headlights . Taxonomy of Swan Events Fig. 10: term risks are probable and predictable - Some kinds of long 2010 2007 & Sikich Taleb Source: Authors based on Swan Event Implication for Analysts Characteristics • Impossible to predict • Very low probability NO CAN’T SEE THEM; BLACK SWANS Unlikely to get captured by risk and • RATIONALE FOR TRYING valuation models actionable Addressing • not is risk WHITE SWAS Predictable to an extent • • Highly predictable • Predictable to an extent • Impossible to predict • Can determine a • Can determine a probability and integrate CAN SEE THEM • Can determine a Very low probability • GRAY SWANS probability into models WITH HIGH EFFORT probability and integrate Highly unlikely to be • • Failure to integrate into models captured by models Cost • benefit may not be justified - traces back to defect in • Cost benefit may not be - Addressing risk is not • the analysis justified actionable Highly predictable • Can determine a probability • WHITE SWANS HARD TO MISS Failure to integrate into models traces • back to a defect in the analysis 12

13 and 1.9 WHITE SWANS THAT APPEAR BLACK IN THE DARK swans black were they though often get treated as models financial thus typically get excluded from OFTEN GET MISSED BY FINANCIAL ANALYSTS on cost - benefit grounds . if appear black might swans White analysis financial the expose risk that shows research The ‘low beams’ of financial analysis Our long - in them leaves . dark term set hazards ahead . year 5 - 3 a have typically models valuation and particular a to investors of forecast horizon, which short - term trends are are after the in black appear that events swan White dark . III) Part (see extrapolated characterized by a distinctive set of criteria (See Fig . with trends and Risks to be unlikely thus are years 5 beyond impacts material 11 ) : ( i ) value financial the affect adversely might They term, of ; (ii) Analysts build on existing assets (‘material’) - long such When . analysts financial by captured are risks linear - non often to knowledge likelihood their about expectations to contribute and form missed to (‘predictable’) attribute this (iii) The benefits to to tend analysts losses, market managing them ; black costs the that triggers with characteristics, event swan gray or justify of assessment (‘actionable worth and term assessing’), ; 5 (iv) They materialize in a long - and (> were either too unpredictable or costly to assess . Upon are criteria these in When . linear - non or year) closer inspection, however, some of these risks were fashion predictable fact met, extrapolating short - term trends in financial risk thus losses accompanying the and 12 As time term - short such, valuation . extent an to and preventable models is impact the capture to unlikely in of risks, thus leaving investors exposed to horizons swan white financial analysis mean that any white swan linear - non ahead term, - hazards (and potentially missed opportunities) way . events materializing in a long Fig. 11: White Swan in the Dark Decision Tree Four key criteria differentiate White Swans from Black and Gray Swans NOT A SWAN IS THE EVENT MATERIAL? No need to assess Could such an event adversely affect the financial value of assets? IS THE EVENT PREDICTABLE? SWAN: BLACK Is it a known concept with identifiable causes? No way to assess Can the risk be modeled in a cash flow forecast scenario? SWAN: GRAY IS IT ACTIONABLE AND EVEN WORTH ASSESSING? Not worth it: Can the risk be managed to some extent by changing the investment Benefit Cost > strategy? If so, is the benefit from assessing the risk > the cost of assessing and managing it? SWAN: WHITE IS IT LIKELY TO GET MISSED BY CURRENT MODELS? Already captured term? - Will the risk materialize over the long by models - Will the risk manifest in a non linear way? WHITE SWANS THAT APPEAR BLACK IN THE DARK term investors to hazards ahead. - Current models miss them, exposing long 13

14 actionable 1 . 10 WHITE SWANS IN THE DARK ARE COMMON AND Catastrophe risks are typically not for nuclear . When catastrophic events ACTIONABLE occur, like investors and war for example, assets across all industries horizon Many geographies impaired, be may implying are today’s on risks term - long that previous reallocations help a such For . wouldn’t portfolio predictable, be can and investors, to material risks, addressed risks of time . Several - in ahead benefit the reduces greatly agency of lack major from with Risks . assessment risk depth literature risk global the in identified considered be can impacts specific more of to particular unpredictable industry or sector, swan), a (not investors to immaterial oil a risk the . g . e risks (black swan), or too analyst’s an from of and spills in the Oil and Gas industry, or view point swan) costly to assess fall (gray . Yet, risks major many opportunities trends and technological specific from Black ‘White Appear that Swans of category our into in be managed can innovations, through targeted the . risk event of type this Importantly, ’ Dark is wants investments and divestments . An investor who risk decrease to an the offshore drilling to exposure actionable from an investor’s point of view . That is, of accident, oil example, for can manage investors risk exposure by adjusting to investments redirect can their influencing investee’s exercise or profiles the lesser with companies or strategy investment their risk foster materializes risk the before management risk shareholder rights to . tougher safety standards . Fig. 12: Classifying Major Risks on the Horizon Long - term risks can be grouped into three categories Risks that cannot be addressed from an investor’s perspective Source: Authors Swans in the Dark’: ‘White Material to Investors, Predictable, and Actionable Not actionable • • Stricter regulations (e.g. no Technological innovations Catastrophic • • An unfixable oil spill puts decommis - an oil company out of drive slow movers out of engineered or indemnity for ) business after reaching a natural sioning business, and potentially shift the profitability of nuclear power below tipping point (e.g. digital brings about regulation pandemics that hampers the entire efficiency. - operating cost camera effect on film sales). offshore industry. Illicit trade • • ‘Sharing Economy’ shifts • Payouts from lawsuits for adverse environmental or consumption patterns and • social Profound • A nuclear meltdown, e.g. instability due to a terrorist attack or shrinks market share (e.g. health effects dramatically natural disaster, alters the Airbnb effect on hotels). eat into the bottom line of Un(der)employ • - viability of nuclear power. hydraulic fracking firms. • Electric vehicle market ment Stricter rules on local In the aftermath of a • • growth combines with solar pollutants in city centers and severe global pandemic, • Ecosystem and battery price decline as well as improved software collapse the cost and constraints suppression of subsidies on diesel fuel reduce the related to pandemic to replace oil consumption control jeopardize the faster than projected by the Failure of • competitiveness of diesel geoengineering competitiveness of small cars in Europe. IEA and EIA airports and airlines. war • Nuclear Slow - build at first, - to - Unlikely in short run Viability of companies due to but almost certain to occur but accelerate after suddenly reduced reaching a tipping point at some point over removal of regulatory ‘anomalies’ the long - term 14

15 current transmission of risk signals OUR TAXONOMY OF WHITE SWANS IN THE DARK 1.11 to and potential the Thus, owners . over asset long - term, investors may swan due dark the in black appear that events White suffer unexpected losses, potentially failing to meet long short failure since Further, . liabilities term - to their are analysis financial of focus term - to a anchoring, - de building, - slow either by characterized long account - term risks and opportunities for implies point - sub allocated be will capital today, assets of mispricing or - in - time risks . ‘White swan in the dark’ types of This . optimally to lead can in underinvestment turn in risks typically exhibit one of three risk profiles that may in the long that projects benefit society make them unlikely to get captured within the 3 - 5 year - term or and burst, they as which, window of risk bubbles, valuation models . These profiles of induce the formation often our of long - term, non - . society on costs tremendous impose taxonomy provide the basis for linear . ) 13 . Fig (see risks swan’ ‘dark this How do define materiality? For the purpose of we we study, to long - term or non - incorporate linear risks least Failure at of impact an : as materiality define of financial see ( rating credit on notch one and price stock % 20 into models decreases the ability of investors on example and portfolios their manage efficiently to than might page 19 ) with a probability of more was view This . point some at happen to % 80 ultimately harm society . A short - term focus by equity the may rating research equity with workshops through credit and analysis research established prevent . analysts Fig. 13: Our Taxonomy: Classifying White Swans in the Dark by their Risk Profiles White Swans in the Dark exhibit common patterns of risk vs. time Source: Authors 2017 Definition Type of Risk Risk Profile Risk • Risks are slow to build at first but gain momentum over time so the expected impact of a n event risk grows at a greater - Building Slow - n tha - linear rate over time. projections neglect the Linear c ash flow • non - linear trajectory of the risk. Time • Status quo relies on artificial or regulatory Risk safeguards or barrier(s) to competition . If barriers are removed, the risk to the spikes future cash flows of incumbents dramatically. De - Anchoring - flow projections assume an • Linear cash artificial ‘risk anchor’, and thus do not account for the potential that it could be removed. Time impact event - Probability of a high • Risk occurring in the short - term is low, but almost certain to materialize at some - time over the long - in unforeseen point - Point Time - in - term. • Linear cash flow projections do not take - impact events with low such high immediate probability into account. Time 15

16 analysts risks likely believed and to visible are These WHITE SWANS IN THE DARK MAY HAVE LONG 1.12 next within not despite TIME HORIZONS decade the occur to necessarily being included in current models . Equity dark’ Disruption, and impactful probable Many ‘swan in which the analysts see Technological . - (slow time only over models business legacy undermines are likely risks event to occur in the long - term risks happen certain are that events to pertain to Some building), the Global Energy Transition to Renewable a natural disaster . removing at the some point in the future, like Sources, involving changes in policies is material to investors might - (de companies However, established of anchors market a disaster that time) every 100 years . To as map the in - (point Meltdown Nuclear and anchoring) - only occur once profiles, highly and impactful risks that only likely probable risk and horizon time between relationship are both the buy - to future in years 6 than more occur we asked the Given . equity research analysts the on and side and sell - side to profile risk current of horizon constrained long - term risks we found in valuation are impactful probable how asked We . literature our and models, these results support the view that there the that horizon the on analysts to risks visible material are risks might be and over what time horizons they . materialize likely were risks these perceived . models valuation to most by not captured Fig. 14: - term Time Horizon of White Swans The Long nergy e Analysts view the isruption as risks to investors over a long time horizon d technological and ransition t HIGH MODELS BY POTENTIALLY CAPTURED LIKELY TO GET MISSED BY MODELS PROBABILITY Nuclear Meltdown Sharing Economy Safety Litigation Antitrust Litigation Global Energy Transition to Renewable Sources Emissions Regulation Equity Bubble Burst (Decline of >20%) Technological Terrorist Attack Disruption Natural Disaster Size of Bubble refers to Relative Sovereign Debt Default impact of risk LOW PROBABILITY 0 4 14 6 8 10 2 12 Time Horizon (Years) Perceived Likely 2016 ii Equity Research Analyst Survey  Source: Authors from 2 16

17 1.13 EXAMPLES OF MEGATRENDS THAT MAY BE Artificial Intelligence (AI) . The rise of artificial MISPRICED - based technologies could disrupt intelligence numerous If sectors and aggregate demand . growing Energy of Transition body a is There . (ET) to information process ability the with computers the that to evidence a low - carbon transition humans implemented are like decisions make and may impact rise to could risk that economy give economy, they could have far - throughout the financial markets as . Such risk, alternatively known reaching and existing employment on effects carbon more asset risk, risk, and now carbon This could business model (see Fig . 16 ) . transition transition agenda the on commonly is the risk, of 14 participation force labor in decline a . cause rates 13 on . 20 G Reporting and Board Stability Financial the on based models business Entire resources human now is risk transition mandatory for institutional eliminated be could . AI to adaptation without investors France, in are and many other investors on it own within the broader context their examining output economic increase and generally will AI . risks financial related - climate of that suggests This 15 aggregate could sectors specific But be . GDP of mispricing of form some assume regulators ET substitution the by disrupted human workforce a of - carbon alter fundamentally may risks These . risks machines with . Specific sectors impacted could be intensive sectors . resources services, human trucking, brokerage services, these consumer discretionary, due to and scale The Energy Transition the in change of may sectors’ high and labor human on aggregate reliance have alter many sectors . Many governments active Without . demand management, investors risk emissions policies decarbonization adopted including losses be could susceptible to . and standards Risk Transition (Energy taxes carbon consortium) and Opportunity . These policies often will AI technology is new as time over grow align to Contributions Determined Nationally with implemented The and developed Group . Analysis emissions as agreed under the Paris reduction market reach maturity by 2024 will predicts that the addition consumer Agreement In changes, policy to . 16 support continued on based . capital venture from lead could decarbonization for preference to a 10 time horizon the year This means that without reduced demand for fossil fuel - based products and . today priced effects likely be not may AI of (see fuel ) 15 . Fig . users fossil to costs reputational autonomous like cars may technologies Particular may be targeted at high emitters . also Litigation 17 exceed the Given . 2040 until models existing not costs of - carbon technologies Most low importantly, technologies, some proposed efficiency of AI could will risks unconventional These . plummet look analysts but inevitable, is replacement need to - non develop not may and term - long the over linearly to when ascertain costs switching and plans R&D at by financial models be captured adequately . . occur might it Fig. 16: Artificial Intelligence Megatrend Factors and Fig. 15: Energy Transition Megatrend Factors and their Cash Flow Impacts their Cash Flow Impacts Cash Megatrend Factor Flow Impact Megatrend Cash Flow Impact Factor Technology prices Commodity Price/Costs Market Market Labor Market Participation Technology Costs Regulatory Costs Policy Universal Basic Income Policy High Research and Regulatory Constraints Development Costs Production Volumes Intellectual Property of Existing Obsolescence Output Technologies Fuel/Technology Volumes Unconventional Ethical backlash Legal Costs Unconventional Reputational Costs GDP/Inflation Other macro trends GDP/Inflation Other macro trends Aggregate Demand Other disruptive shocks Source: Authors Source: Authors 17

18 1.14 CASE STUDIES: HOW LONG TERM RISKS - SUMMARY : CASE STUDIES AFFECTED CAPITAL MARKETS IN THE PAST - De car Anchoring Risk : Volkswagen’s diesel Many resulted borne losses financial by past investors business of enforcement lax the by anchored was have from long - term risks that could predicted been risk them exposing regulations, the emissions to and thus managed . Nonetheless, risks were these could of enforcement effective more . Analysts often and reflected in equity credit research only learned in have about emissions fraud advance We ratings the risks had already materialized . once warning signs . but likely ignored the of Energy, the examine Peabody Volkswagen, cases Peabody slow Slow - a building Risk : Energy - faced to Crisis Mortgage Subprime the and how showcase gas building challenge from declining natural - time risks de point building, - and in - slow - anchoring, to failed Analysts . of prices severity the recognize resulted in capital market losses in the past . In the . trend which these to extent the examine we particular, risks Mortgage Crisis The : Risk Time - in - Point Subprime strong either by preceded signals that weak or were began . rising stopped prices house Credit once analysts’ could have been incorporated into financial to failed of reversal the recognize analysts have have signals such might . models Incorporating not did and growth price housing downgrade steep equity lessened the harm to investors following - . time in securities backed mortgage of de - valuations or unexpected adjustments credit . events the after only occurred ratings Case Study 1: Volkswagen De - Anchoring Risk: Exposure of Vehicle Emissions Fraud Summary : Since 2006 , Volkswagen (VW) was aware its diesel engines could not meet U . S . emission standards in 18 real conditions . - From 2010 - 2015 , VW sold 11 million diesel vehicles worldwide with “defeat world driving that in emissions controls only software laboratory test conditions in order to meet device” activated enforcement During time, the anchor of lax regulatory . allowed VW to avoid the costly requirements this of their diesel engines that would be necessary to comply with regulations . On September 18 , 2015 the redesign 19 . EPA announced that VW had admitted emissions fraud . . In the 10 days that followed the announcement of S U fraud, VW shares lost 34 % of their value ; 10 months later, they were still 20 % below pre - scandal value . the There were signals of VW’s emissions fraud that may have been ignored by analysts. Automakers had attempted to manipulate lab Mounting Evidence of Emissions Gap Fig. 17: Begins to Dislodge the Anchor of Lax Enforcement emissions results before: Ford, 1972: $7 million fine for manipulating test • cars NGO/ Date Key Message/Action Chrysler, 1974: Recalled over 800,000 cars with • Organization defeat devices Recalled almost 500,000 1995: General Motors, • A International verage gap between lab and 20 Cadillacs with defeat devices real Clean Council on world emissions growing: - May 2013 23 Transport <10% in 2001, 25% in 2011 . Vehicle emission standards were tightening since US EPA; California 2007: Begin investigation of Air Resources EU 2009: announcement of mandatory CO2 • May Volkswagen diesel emissions . 2014 Board emissions standards for 2015 2014: Announcement of tougher fuel - • US 2011 International Average emissions gap in 21 efficiency standards Council on Clean 2013: 31%. New VW Passat 2014 Sept. 24 model is a major offender. Transport growing After . was fraud emissions of Evidence 2013 , r esearch multiple NGOs documented the by European laboratory - world gap emissions between and real Diesel vehicles consistently for Federation exposed, was fraud the After . ) stated . Fig 17 VW (see emit much more NOx than July 2015 Transport & device usage was an open secret in the that defeat 25 regulations permit. 22 Environment and that regulators were aware of it . industry 18

19 VW’s high risk exposure might have been worth it and the consequent drop in share prices suggest it to incorporate the risk of tougher enforcement into risk and valuation models . Volkswagen Share Price Drop 18: Fig. was business to the ’s VW highly exposed stock lost nearly half its value due to emissions Volkswagen’s emission and tougher of risk standards fraud 2015 11 , their enforcement : During 2010 - Euros (€) multiple million cars were sold in 300 - 43% IN 2 WEEKS POST - that jurisdictions set vehicle standards for ANNOUNCEMENT emissions that penalties financial total The . regulatory multiple by assessed be could was for entities significant Further, fraud . 200 would the announcement of fraud likely significant cause reputational damage . exact violation However, the a of timing be not . predicted could 100 dropped did Share prices substantially and Close Price YEAR LATER 1 27% - recover not of after exposure VW’s days that followed emission fraud : In the 10 0 announcement the of the fraud, VW lost before One . out bottoming 43 value its of % 1/2/16 9/2/16 1/2/14 5/2/14 9/2/14 1/2/15 5/2/15 9/2/15 5/2/16 year later, it was still 27 % below pre - scandal ) value (see Fig . 18 . Quandl Source: Inquiry Financial/ The sharp reaction in target prices and credit ratings after the announcement of VW’s emission could fraud considered the event to be probable or impactful indicate that financial analysts had not enough to adjust recommendations or ratings in advance. Fig. 20: Volkswagen Credit Rating Changes see not did . growing risk the Equity research analysts Upgraded Volkswagen’s Credit analysts Credit 2014 year one , research equity Morningstar’s late from report Rating just months before the fraud before announcement, cited short the term risks like “stiff - announcement “increasing global excess capacity,” consumer competition,” No switching, and the auto business cycle . mention of was made emissions targets potential costs of the violations . Thus, price January 2015 costs known were . ) 19 . dropped dramatically once these Fig (see S&P rates VW as stable, citing its ability to meet “stringent Fig. - Average Volkswagen Price Targets, 2014 19: 2016 environmental standards” Equity Research Analysts did not lower their Volkswagen price targets until after the announcement of fraud March 2015 €) Euros ( Moody’s upgrades VW, citing - ANNOUNCEMENT - POST 34% 300 its ability to meet “stringent regulatory requirements” October 2015 200 S&P downgrades VW, citing “tarnished reputation” and financial penalties 100 Close Price November 2015 - 55% SIX WEEKS LATER Mean Price Target Moody’s downgrades VW, 0 citing the cost of regulation 9/2/15 1/2/16 5/2/16 9/2/16 1/2/14 5/2/14 9/2/14 1/2/15 5/2/15 Quandl Source: Inquiry Financial/ 19

20 Case Study 2: Peabody Slow - Building Risk: Fuel Substitution In U.S. Electricity Generation world, In Peabody Energy was the largest private coal producer in the , with 82 % of its sales by : 2011 Summary 26 . S . electricity generators . volume to steady improvements in drilling technology, U . S . shale gas U After 27 2005 , making natural gas an increasingly after fuel for electricity production . boomed production economical 28 . S . environmental regulations continued to dampen the relative competitiveness of coal . Meanwhile, After U April of demand globally, Peabody filed for bankruptcy in coal 2016 . Loss of equity value was weak years : the market cap of Peabody dropped to US $ 38 m in 2016 , down from US $ 18 bn in 2011 . Peabody’s immense US price US $ 72 in 2011 to from $ 2 in 2016 . fell stock - moving decrease in the competitiveness of coal relative to gas was predictable. The slow 31 reductions . gas natural in Steady decline in the market cost spurred techniques drilling Evolving : Fracturing Hydraulic • share of coal relative to gas: ease to shale into pressure high a at fluid Injecting since extraction of gas, largely developed After steady improvements in . Energy Mitchell by 1980 of technique A : drilling technology, shale gas Horizontal • drilling underground horizontally wells turning . pioneered by Elf Aquitaine in 1980 production began a sharp and shale of imaging Improved : Imaging Seismic D - 3 • s 2000 in formations steady increase since 2005, 29 rising 40% by 2015. improved discovery . commercialization By costs, lowered and enabling - 2009, natural gas was price Fig. 21: Slow building Risk to the Coal Industry - competitive with certain The price of natural gas price converged with coal while the share of coal in US electricity types of coal. During 2012, generation decreased gas was price competitive Coal % of Generation with all but the lowest - cost Weighted Avg. Cost of Coal U.S. coal, thus altering the Natural Gas % of Generation Henry Hub Spot Natural Gas Price market share of fuel sources 60% in U.S. electricity generation. 12 Tightening environmental 9 40% regulation: After 2004, the 6 U.S. Environmental Protection mmBtu 20% Agency (EPA) proposed and in $/ 3 some cases finalized regulations such as the Clean 0 0% 2005 2010 2015 Power Plan (2014) that would 2015 2010 2005 increase the operating costs Source: EIA 2015 and capital expenditures of Fired Power Plants Regulations US - Impacting Coal in many existing coal plants order to comply . Further, the EPA’s New Source National Quality Ambient Standards, Cross - State Air Air Pollution Rule, Performance Standards : Residuals Coal and Rule, Structures Rule Cooling Water Intake Combustion would (proposed 2011) these regulations specified higher standards for the air emissions, From 2004 new coal essentially make additional fired plants, power - coal of products - by and water, cooling adding 32 plants uneconomical given significant . generators costs a compliance to portion of US coal existing technology. Though aspects of these rules were Toxics required Standards : Finalized in (MATS) Air and Mercury 2011 , MATS successfully challenged in power Control Allowable Maximum install to Technology fired to coal - plants court, the trend towards Carbon Tracker estimated that 40 % of US limit specified pollutants by 2015 . more stringent regulation to plants needed to upgrade controls in order coal be compliant . increased the risk around coal relative to that around Standards 2 maximum Carbon New Source Performance Proposed . CO (NSPS) cleaner fuels such as natural 30 coal would emissions levels for new power plants such that new plants gas. of effectively require some form storage carbon capture and . 20

21 Fig 22: Proposed Coal Additions 23: Fig Planned Coal Retirements Trends in generation capacity 2011 Few coal additions were planned in Many coal retirements were planned in 2011 implied a smaller role for coal Coal Natural Gas Other in the U.S. electricity grid of . As of EIA 2011 data, the future W G W G 92% of proposed generation 25 40 capacity in the US used a fuel RETIREMENTS: 77% COAL ADDITIONS: 92% NOT COAL 22), other than coal (see Fig. 20 30 while 77% of the capacity planned for retirement was 15 fired (see Fig. - coal 23). 20 Further, the US coal fleet was aging. As of EIA 2011 data, the 10 average age of a currently 10 operating coal generator was 33 5 years, while historically the average retirement age of a coal generator was 38. 2012 2013 2019 2022 2021 2020 2014 2018 2017 2016 2015 < 2 yrs. 2-5 yrs. > 5 yrs. Source: EIA Form 2011 860, decline of US Peabody’s coal, to and relied on vulnerability - discounted term Equity analysts the long much . expectations accurate ultimately and skeptical more instead were analysts Credit . optimistic Equity Analyst Percent Buy/Sell Recommendations Fig. 24: research Equity Peabody believed that analysts believed Peabody would recover until Most equity the slump in . outlast coal U . S could 2015 essentially sales would demand and that Analyst Recommendations % of - - as : business to return usual 100% as • Analysts Peabody the regarded player dominant felt their US coal and 75% low cost, - sulfur coal could survive low - abundant natural . alongside gas adapt • Analysts felt that Peabody could demand to declining U . S . thermal coal 50% Buy . by exporting U . S . coal felt that Peabody’s Australian • Analysts Sell sales 14 % volume of ( by coal in 2013 ) 25% revenue to provide outlast the would U . S . slump . EIA data on fuel demand generator fuel and 0% in available on a yearly and was usage 2016 2015 2014 2013 2012 2011 However, some cases monthly basis . equity reports from 2011 - 2014 focused research much on weather, inventories, and more guidance while rarely mentioning coal % Buy begins final drop in July 2014 and natural gas additions . This retirements % Buy > % Sell until October 2015 suggests have could analysts that before the risk of a coal collapse integrated 2014 . did not (see Fig . 24 ) but Source: Zacks / Quandl 33 Credit Rating Agencies were very cautious on Peabody’s prospects Peabody rated Moody’s nor . Neither S&P . 2014 investment grade during 2011 - Moody’s had a non - investment grade rating on Peabody beginning in as began S&P May 1998 when it Peabody’s downgraded steadily coverage , 2013 in Beginning . company the on the that corporate credit rating . . credit analysts interpreted This signals of weakness in the coal sector suggests 21

22 Case Study 3: Subprime Mortgage Crisis Point - Time Risk: Subprime Bubble Burst - In conflicts The crisis is partly attributed to fraud and mortgage of interest . However, it was : subprime Summary rooted in a point - in - time risk also The ‘sudden’ realization that subprime adjustable - rate fundamentally : cannot repaid in the long - term . be t he development of origination and securitization mortgages When for subprime mortgages accelerated in 2003 , along with the residential housing bubble, the risk of techniques a bubble to build up . Over the short - term, started collapse of this bubble might have seemed very bursting the . Yet, if longer time horizons had been employed, a housing bubble burst could have been seen as unlikely . inevitable The risk of a housing bubble burst was predictable and subprime mortgage markets were extremely exposed to it, thus suggesting that considering this risk would have been “worth it.” 2007 Fig. 25: US Home Prices Declined Beginning in in - house prices was a point The fall in - time risk burst of the subprime The mortgage bubble was inevitable. S&P/Case-Shiller Home Price Index ‘Subprime’ mortgages targeted HOUSE PRICES - income - and at times even low BEGIN TO DROP - individuals. Since unemployed 200 these borrowers often lacked the ability to meet mortgage 150 payments, repayment of subprime loans was largely based on the 100 resale of properties. If a borrower sold the home for more than the 50 purchase price then they could 0 repay the mortgage. With adjustable mortgage rates often 2014 1990 1987 1984 1981 1978 1975 2005 2002 1999 1996 1993 2008 2011 exceeding the growth of home Source: S&P/Case Shiller Home Price Index , the risk of default was high. prices These mortgages were ticking time Fig. 26: Market Share and Dollar Amount of Subprime Loans bombs, ready to explode when burst a nearly trillion dollar bubble The fall in house prices housing market prices eventually This eventually occurred declined. Subprime Mortgage Market Share in 2007 (see 25). Fig. Dollar Amount of Subprime Mortgages Market Share Subprime Mortgages ($ ) Bn - at The value - risk from a bubble 25% 750 burst was immense. Between 2001 and 2006, the share of 20% 600 subprime mortgages jumped from 7.6% to 23.5% of the total U.S. 15% 450 26) mortgage market (see Fig. . - Securitized and re packaged with 80% of these other assets, 70 - 300 10% loans were rated AAA until the beginning of the crisis, creating a 150 5% pool of $3.2 trillion of high - risk assets labeled as investment 0% 0 34 grade. 1999 2001 2000 2007 1998 1997 2005 2004 2003 2008 2002 2006 1996 Source: Federal Reserve Bank of St. Louis 22

23 The collapse in housing prices could have been predicted ... 2008 - Monthly Supply of Houses, 2005 27: Fig. supply collapses price occur because Housing of - in - There was a weak signal of a point time risk before the crisis and demand . If the housing market is oversupplied, prices are housing then collapse . Typically, likely to by rates vacancy increased to respond builders Monthly Ratio of Unsold Homes to Sold Homes in up to homes fewer building the . However, lead mortgage the subprime homebuilders crisis, 12 add to continued inventory . This trend, combined median house with its above % 67 price the 9 created the initial conditions for historical average 35 As number the a of months . bubble a result, to sell unsold inventory steadily increased required 6 that Fig . 27 ) . Given (see average U . S . the monthly the supply unsold homes is 6 months, once of 3 July rate crossed 7 in monthly of 2006 , analysts been to alerted an have should oversupplied were homes new as continued trend The . market 0 Early added until housing prices collapsed in 2007 . house warning signs for a collapse existed prices in 1/1/05 9/1/08 5/1/08 1/1/08 9/1/05 5/1/05 5/1/06 9/1/07 5/1/07 1/1/07 9/1/06 1/1/06 - in time could prior to 2007 and the point risk thus have . factored into analyst models been Source: Federal Reserve Bank of St. Louis, 2008 y credit rating analysts due to the non ... but was missed b - linear risk profile. Fig Backed Security 28: - Credit Rating Agency Mortgage late from of combination The low - payments Downgrades & Subprime Mortgage Default Rates prices house and borrowers income made falling Rating agencies did not downgrade securities until it was too late on mortgage subprime - backed defaults securities not did rating credit though inevitable analysts Mortgage-Backed Security Downgrades crisis securities downgrade any of these until the delinquency was inevitable . The m ortgage rate, refers to the fraction of mortgages with at which Ratings Downgrades Late Payment Rate three months of outstanding payments, least 26% 1,000 from the end of 2006 (see Fig increased linearly . 28 correlates directly with the increased ) This of subprime mortgage issuances . market share housing prices collapsed in 2007 , When 800 22% rates skyrocketed, borrowers delinquency since homes could no longer sell or their to refinance cover delinquent . payments 600 slow were agencies rating credit Nonetheless, to 18% securities enact downgrades of mortgage backed - 400 made of downgrades No . by (MBS) were MBS credit agencies late 2007 and not at until rating until Q 1 2008 (see Fig . 28 ) scale explanation An . 14% of lack a was this . for performance MBS on data 200 on agencies did not have enough data Rating 2006 the MBS vintages to make downgrades second until 36 forward emphasis - quarter of 2007 . on Greater 0 10% looking analysis earlier to led have might Q3 Q1 Q2 Q3 Q2 Q1 Q4 Q3 Q4 downgrades on these securities smaller losses and 06 08 07 07 07 07 08 06 07 for in 2008 . investors Chuanshu Ji, 2008 Source: 23

24 1.15 FUTURE RISKS: HOW LONG TERM RISKS COULD - SUMMARY: EXAMPLES OF FUTURE RISKS CAPITAL MARKETS AFFECT Slow could building Risk : Autonomous car fleets - risks Dark’ the in Swan ‘White New going emerge will disrupt . manufacturers automobile legacy explore pages, we three Over . forward the next right) potential White Swans (see that may not be De insurance of elimination - The : Risk Anchoring valuation financial term - short addressed adequately by pools operators . power nuclear anchor - de could examine and risk assessment models . First, we whether our these risks satisfy criteria of ‘predictability’, : - in - Time Risk Point An offshore oil accident rig - linear risk benefit’, ‘justified cost and ’long - term - or non could set an oil major’s a on downward profits we Second, . ) 29 . Fig (see profile’ hypothetical conduct spiral . how illustrate to experiments thought could risks these missed by financial models that rely on get potentially linear extrapolation of cash flows . Fig. 29: Three Long - term Risks that Fall into the Category of White Swans that Appear Black in the Dark headlights There are numerous White Swans that may fall outside the range of analysts’ Source: Authors Autonomous Cars Insurance Offshore Oil Drilling Accident Nuclear YES: - driving ise in crude oil prices from The r YES: Cars with self insurance Nuclear YES: 2000 made increasingly legislation is renewed regularly. features are already on the road complex today. A future legislative body may E xperts suggest that 10 drilling conditions profitable, and require nuclear operators to buy driving - accidents more likely. After BP’s cars with full self million abilities could be on roads by accident insurance rather than Deepwater Horizon disaster, well 37 2020. receiving insurance through control risks are widely understood Event Predictable? regulated insurance pools. within the industry. The prevalence of YES: YES: The cost of accident YES: A n incapacitated rig could cause substantial losses in an oil firm’s autonomous cars is highly insurance would be immense drilling revenue, cleanup costs, legal (particularly in view of probable, potential disruptive liabilities, and damages to reputation: Fukushima), and would thereby effects on the auto industry are significantly undermine the As of 2015, BP faced costs of US$ S$ U large: Estimates suggest a Incorporating? 87bn market for driverless cars by viability of power producers 53.8bn. After Deepwater Horizon, the with large amounts of nuclear 29% of market volume with 2030, probability of well control accidents known within the have been well thus threatening for software, production. - 38 industry and not too costly to conservative auto makers . Risk Worth determine. YES: YES: opposition to F ully autonomous cars A l YES: are a I ncreasing ack of large accidents over a the erode to period of time is likely way from commercial nuclear plants could deter long 39 linear? reality. The need for advanced emphasis on standards and governments from passing costs - technology , and questions around of nuclear insurance onto monitoring, as well as the ability to breakdowns. Multiple infrastructure, regulation, and respond to low taxpayers. Further, since acceptance, slow the prevalence regulation such as the US Price probability and manageable events and Non of autonomous cars over the next Anderson Act is not up for can combine to produce a sudden term push a , and will likely renewal until 2025, the risk may years 10 high impact event. - not manifest for 10+ years. beyond fully autonomous fleet 40 Long 2040. Anchoring - De Time Risk : low rates of S Risk: Building - Slow Future cash flows Point - in - In the US, Risk: operators have a maximum of substitution due the long lifetime are at risk from a rig accident that at of cars suggests incremental a low probability $350 million in accident any one moment has 43 41 rather than sudden change. A coverage. of occurring, Fukushima cleanup but across the industry risk tipping point could be reached as cost $50 billion. If operators as a whole is extremely likely in the run. - long needed to cover that amount, fully autonomous cars are widely Swan premiums adopted, and as households could increase by a reduce their fleet. Eventually, car of 1,000. This could make factor ownership may be diminished by nuclear power business models 42 43%. unviable. 24

25 Future Risk 1: Prevalence of Autonomous Cars - Building Risk to Long - Term Investors Slow Car Manufacturers and adoption Given rise of self - driving vehicles, early current and development of shared autonomous : the Summary will likely be a key determinant of auto companies’ driving - term success . Companies that lag systems long such advances are prone to lose market share technological may even get displaced from the market behind and . entirely could be the case as new competitors develop superior products (i . e . Google), or as households This abandon ownership due to car sharing technology . Assuming that 50 % of cars are shared by 2040 , Barclays car 44 25 40 % decrease in car sales over the next a years . Research A uto manufacturers currently make up estimates 1 . 74 % of MSCI ( MSCI World Index as of Feb 28 , 2016 ) . The evolution of autonomous vehicles along with the sharing economy will likely induce a significant disrupting automobile manufacturers reorganization of the automobile industry, potentially term scenario: The Short - long development cycle of autonomous driving - Driving Technology has Present: Self , regulation, and slow adoption technology Minor Market Penetration market penetration of shared inhibits the . Car - mobility solutions sharing on a broad RISK NEGLIGIBLE scale is limited due to slow moving - changes in consumer preferences. to - - Medium long - term scenario: M arket Long - Term: penetration first accelerates and then Autonomous Cars Induce Significant sales as more causes a significant drop in Industry Reorganization anufacturers that do cars are shared. M RISK BUILDS SLOWLY not keep up with the pace of the are likely to get left behind. technology start diverging : Analysts’ current analysis financial implications Hypothetical cash - flow projections slowly for flows . The divergence becomes more drastic from the reality of a non - innovative car manufacturer’s actual cash shared, displaced market . as more vehicles are the implying that non - innovative manufacturers are from - Fig. 30: Hypothetical Effect of a Transition to Autonomous Cars on a Non Innovative Car Manufacturer innovative car manufacturer - Autonomous cars could slowly erode the new car sales of a non Mainstream Self-Driving Cars Scenario Analyst Projected Cash Flows USD) Millions, Cash Flows ( 20,000 10,000 0 31 36 6 1 11 26 21 16 41 46 Forecast Period (Years) example (dummy data) Source: Authors, illustrative 25

26 Future Risk 2: Nuclear Operators Must Buy Insurance Coverage De - Term Investors - Anchoring Risk to Nuclear Power Operators and Long Nuclear Summary presently protected from accident insurance : r egulated insurance pools and operators : are . coverage artificial cap on nuclear operators’ costs an Future legislation could, however, liability maximum put operators to buy costly require insurance in the private market . A non - renewal of the U . S . nuclear accident Anderson in 2025 , for example, would have Act effects on nuclear power generators’ cost Price detrimental : Insurance premiums could increase by a factor of 1000 , thus de - anchoring operators from the cost structure 45 of business is grounded . in While the risk their such a severe change in legislation is small which environment the short - term, i ncreasing opposition to nuclear power could deter governments from passing nuclear in nuclear onto over the longer term . Producers with taxpayers operations comprise over 1 % of the insurance 46 capitalization of the S&P 500 and have a weight of about 0 . 9 % in MSCI World . market A non renewal of nuclear legislation (e.g. - Price Anderson Act) would oblige nuclear operators to buy accident insurance, potentially imposing major and permanent drops in some power producer’s cash flows. - Short scenario: Protection from term Present accident insurance, such as the U.S. Artificial Cap on Nuclear Operators’ puts an artificial Price Anderson act, Insurance Costs cap on nuclear operators’ costs . RISK ANCHOR - - term scenario: to long A Medium renewal of the Price Anderson - on n - Long Term Act in 2025 increases insurance Nuclear Operators Obliged to Buy Costly premiums by a factor of 1000. Accident Insurance ANCHOR REMOVED the Hypothetical Implications for financial analysis : Analysts’ current cash flow projections may neglect on premiums removed . For a insurance operators’ nuclear is on cap the if flows cash decreased possibility of operational, nuclear insurance premiums nuclear operator with 5 - 10 operational plants, if the plants remain flows fig ) . could reduce free cash 31 by nearly 50 % each year (see . Fig. 31: Hypothetical Effect of Removal of Insurance Maximum on Cash Flows for a Nuclear Operator Non operator - renewal of Price Anderson Act could massively increase operating costs for nuclear Cash Flows under Price Anderson Nonrenewal Scenario Analyst Forecasted Cash Flows Millions, USD) Cash Flows ( 15,000 10,000 5,000 0 46 1 41 36 31 26 21 16 11 6 Forecast Period (Years) example (dummy data) Source: Authors, illustrative 26

27 Future Risk 3: Offshore Oil Rig Accident Point in - Time Risk to an Oil Major and Investors - : The in crude oil prices from 2000 made increasingly unconventional extraction methods Summary rise accidents oil more likely . While the occurrence of an offshore and rig accident has a small profitable much accident attached over the short - term, such an it becomes more likely when longer time horizons probability to considered . An oil rig accident leading to an incapacitated rig could impose a downward spiral on an oil are cash flows, thus leading to deteriorations beyond those faced by BP in the aftermath of the Deepwater major’s disaster p Deepwater Horizon cost $ 53 . 8 bn in 2015 including $ 1 . 1 bn damages Horizon . a . for 18 years and (BP’s rig $ 1 bn for expenses such as actual cleanup) . Companies with offshore . exposure comprise over 3 % of S&P 35 500 market capitalization . An oil major who faces a severe oil rig accident would forego immense amounts of revenues and face high clean up costs. Short scenario : Stricter - term Present safety controls standards and Low Probability for Major Oil Rig Accident after Horizon BP’s Deepwater NO EVENT probability support disaster low a accident . for a major oil rig Long - Term to long scenario Medium term : - - A n oil rig accident worse than Major Oil Rig Accident Occurs Horizon Deepwater occurs and EVENT HAPPENS to cap takes more . 3 than months : losses astronomical clean up costs, Accumulated revenues, analysis flow cash for Implications Hypothetical in gap between the oil company’s realized cash flows and the and reputational damage could lead to an increasing projections no rig accident . In a worst case scenario, a major spill major of analysts who assume there will be oil zero flows cash free major to reduce could . Hypothetical Effect of an Oil Rig Accident on Cash Flows for an Oil Major 32: Fig A major accident could cripple the cash flows of an oil major in perpetuity given high litigation Analyst Forecasted Cash Flows Cash Flows Under Large-Scale Accident Scenario Cash Flows ( Millions, USD) 30,000 20,000 10,000 0 1 21 23 25 3 5 7 9 11 13 15 17 19 Forecast Period (Years) Source: Authors, illustrative example (dummy data) 27

28 FEEDBACK FROM INDUSTRY ENGAGEMENT WORKSHOPS: WHITE SWANS IN THE DARK Feedback Summary Supporting Quotes Section While analysts and research department “These risks should be considered but their - uncertainty makes them difficult to consider.” - managers acknowledged the relevance of non term asset term risks to long - ESG Analyst linear long - WHITE owners, they pointed to uncertainty about the SWANS IN - “Spending time assessing very long future as a constraint to long - term risks term risk THE DARK assessment. This confirmed our view that long - might be difficult to justify to clients if the risks are not very likely to materialize.” term risks are not currently analyzed in equity - Senior sell - side equity research analyst and credit research. Analysts pointed out that the risk profiles in “This taxonomy is too academic. Tell me what our taxonomy typically overlap. For example, the risk is and when it will happen.” Former - Bracket slow - building risks could evolve into de - Head of Equities Research at a Bulge Institution anchoring risks over time, and many if not all TAXONOMY risks have a point - in - time aspect to them once OF LONG - - they crystallize. While our taxonomy of long TERM RISKS term risks was seen as a helpful broad categorization, analysts pointed out that risks by - case basis in practice are spotted on a case - rather than trough a holistic framework. “Energy Storage could be a game changer for the - Equity analysts offered many examples of long term risks that may be missed by financial Former Head of Equities - utilities industry” Research at a Bulge Bracket analysts due to short term time horizons. Institution - RISK Examples included the evolution of energy EXAMPLES storage, cybercrime, groundwater depletion, renewable energy, declining Chinese demand, and US Clean Water Act enforcement. “Nuclear power policy risk is already priced into There was consensus that capital markets may German utility share prices given the recent not be adequately pricing the risk of nuclear out of nuclear power. Yet, such risks might - phase policy change into security prices for nuclear operators. Yet, there was some disagreement not be factored into the valuation of utilities in as to whether oil spills will pose major risks to other countries.” - ESG analyst cash flows in the future, and whether FUTURE “Large scale oil spills may already be captured by autonomous cars will supplant current models. EXAMPLES Some analysts disagreed with the example of ESG analysis; the importance of such a risk depends on location and the quality of autonomous cars due to the uncertainties that surround the trajectory of technological - Buy side analyst companies’ risk management” - - innovation over the long term. “There are a million possible scenarios in the Senior - future. It’s impossible to pick just one.” Credit Analyst “The rules for Volkswagen were in place, they The past events considered in our case studies Managing Director of - just weren’t enforced.” (VW emissions fraud, Peabody’s decline, subprime mortgage crisis) were potentially ESG Research missed by financial analysts though they could have been anticipated with more foresight. “Peabody’s downturn was preceded by clear Equity analysts agreed with our Volkswagen signals in the U.S., e.g. the rise of hydraulic PAST fracturing and clear signals indicating that coal - example because the rules and fines were EXAMPLES already on the books but were not enforced fired power was under pressure even before the yet. They disagreed partly with the Peabody Clean Air Act.” - SRI advisor example because Peabody was trying to diversify as a company, but acknowledged that “Housing prices empirically had always gone up Peabody’s downturn was predictable to an so it was reasonable to assume that they would extent. Asset Manager keep going up.” - 28

29 PART II TERM ANALYSIS: - FOR LONG THE RATIONALE EXPOSING THE ‘WINDOW OF MATERIALITY’ SECTION SPOTLIGHT under assets owned by long - term investors with an average of is share large A management • . years 15 - 10 horizon exceeding exceeds the 3 - 5 year time horizon of financial analysts in The relevant ‘Window of • Materiality’ . decades by often industries, most

30 2.1 MANY ASSET OWNERS HAVE LONG INVESTMENT Fig. Average Liability Lengths of Leading Asset 33: HORIZONS Owners Long Liabilities Asset Owners Have many Analysts long term - the research because should Years asset have long time horizons . The major owners 60 investor time horizons is demonstrated by the length of . vehicles investment common of horizon time sovereign Typically, wealth funds and endowments are 50 horizons year over beneficiaries 50 time liable to their pension while and 20 to commit funds insurance funds Fig horizons horizons long year (see are . 33 ) . These the term liabilities - of the funds . by driven long 40 pension funds, wealth sovereign Endowments, funds, companies insurance term and - maximize to seek long to return because of their long - term responsibilities 30 owners their investors . Similarly, high net worth asset for invest generally or term - long retirements their mutual management . While hedge funds and wealth 20 funds have comparatively smaller time horizons, they market do not comprise a large portion of equity . ownership 10 Indeed, funds with nearly - term liabilities own long 48 . markets equity of equity domestic . S half . U the of % with 10 owned by Investors is liabilities of over market 0 investors, International . ) . Fig (see Years 34 including funds, sovereign wealth and other investor classes, also endowments, of portions significant own including Pensions Insurance the market equity mutual and funds Hedge . funds, Hedge Funds Endowments horizons, investor classes own short time only with Mutual Funds types, % of the equity market . Thus, among the fund 25 than comes term investors investment long - from more implies there is that . demand investors term - short This Private High Net Worth Sovereign Wealth Funds investment for . research long term - Source: MFS 2016 Fig. Ownership Share of U.S. Domestic Equity Market, 2015 34: term Investment Horizons A Majority of Asset Owners Have Long - Typical 8% Asset Owner Type Liability 4% Lengths ( ) yrs 4% Households 10 33% Mutual Funds 1 20 Pension Funds 16% Mixed International Investors 0.5 Hedge Funds 1 ETFs 15% Mixed Other 21% Source: Authors based on Goldman Sachs and Federal Reserve Board Data 30

31 2.2 COMPANY VALUE IS MOSTLY BASED ON LONG - - long exposed most term that companies The to are TERM CASH FLOWS major a derive risks value from the long - their of part derive term % than less utilities and estate real Both . 13 equity company of % 82 In over valuation, This . value 5 next the from value present net their of years of risk low the to relates comes from flows more than 5 years in the sectors these in cash investments future . Figure 35 shows low Morningstar discount rate corresponding the and net present value future cash for 500 the of Each . period time by calculations S&P assets physical the flows as just But real and . of utilities 32 % sectors represented in this figure derives at least estate to last for long time periods, they are exposed beyond flows from estimate value fair its of 20 cash Over of present . risks term - long net value the of % 65 years future . Although analysts use discount the in more stocks derived from cash flows occurring Utilities the reduce to rates cash future of value present net This term years the future . in means that long - than 20 present the the flows, still is flows cash future of value utility risks to the could sector, if accounted for, term risks should high . As a consequence, long - be constituent strongly affect the net present value of its when valuing shares . accounted for industries many of values The . companies skewed are on the long toward term, as . page next the shown - Stock Value By Future Time Period of DCF Models for Sample of S&P 500 Stocks Fig. 35: Risks Stock Value is Based Mostly on Cash Flows that Are Exposed to Long - term 51 and beyond From Years Percent of Enterprise Value 50 6 - 10 11 - 21 - 20 5 - 1 100% 80% 60% 40% 20% 0% Technology Energy Financial Healthcare Real Industrials Utilities Basic Consumer Consumer Cyclical Defensive Materials Services Estate Source: Authors, from Morningstar DCF Models 2016 ( n=107) 31

32 Fig. Company Net Present Value by Forecast Time Period 36: Industrials and Utilities stocks are more Stocks: Cash flows from to Long - term risks than Technology exposed beyond 20 years generate the largest percentage of net present value in capital intensive sectors Beyond 20 Years 11-20 Years 21-51 Years 6-10 Years 1-5 Years Industrials 45% 30% 15% 0% ADP CarMax Paychex Fastenal ABB, Ltd. Fiserv, Inc. Equifax, Inc. PACCAR Inc. Fortune Brands Fidelity National Dun & Bradstreet Rockwell Collins, Inc. Technology 52% 39% 26% 13% 0% Garmin IBM Corp. Linear Tech Corning Inc. VeriSign Inc. NetApp, Inc. HP Enterprise Analog Devices Omnicom Group EMC Corporation Interpublic Group TE Connectivity Ltd Skyworks Solutions Amphenol Corporation Motorola Solutions, Inc. Utilities 100% 75% 50% 25% 0% Duke SCANA Entergy Con Edison Xcel Energy FirstEnergy CMS Energy Corporation NiSource Inc. Alliant Energy Pinnacle West Eversource Energy CenterPoint Energy Edison International Dominion Resources Ameren Corporation Pacific Gas & Electric from Morningstar DCF Models 2016 Source: Authors 32

33 TERM RISKS 37: Average Lifespan of Physical Assets Fig. 2.3 COMPANIES ARE EXPOSED TO LONG - lifespan can last 100+ years Real asset companies cash physical Many from derive their flows Years . Assets assets with lifespans such long as 120 years as 140 by companies as buildings and infrastructure are built . years more or for flows cash generate to if 80 Even 120 resale assets are sold the their useful life, their during the sector, is based on the long - term . In power value 100 assets generation 30 are typically designed to last for 37 Buildings more (see Fig . or ) . years and urban 80 infrastructure, developed in sectors such as Real Estate long last as 120 years can . The cash and as Industrials, 60 affected be can these from flows projects term - long by . risks 40 of capital intensive sectors In the S&P 500 , assets are amortized . ) 38 . Fig (see years 6 of minimum a over 20 recovery of life useful the Depreciation period refers to physical all encompasses and in assets used a assets 0 conduct to assets build companies When . sector business, intensive - asset physical as such power transportation, natural resource extraction, or Pipelines in lives useful generation, they invest assets with long, . Cement Plant Wind Turbine asset from owners is This means that capital raised Building Stock Solar PV Array Passenger cars Coal Power Plant asset As . projects - long fund to term result, a used Large Hydropower should from value realize to owners and be able Nuclear Power Plant Urban Infrastructure Offshore Oil Platform Consumer Electronics the they . in understand the risks of investing assets are Natural Gas Power Plant Manufacturing Equipment - term investors should understand Long risks to the physical assets . based on IEA Data 2012 Authors Source: 38: Fig. Median Depreciation Recovery Period period can depreciation Median last up to 18 recovery years Years 18 12 6 0 Mining Marine Utilities Aviation Automobile Engineering Paper Products Oil & Gas Other Consumable Fuels Integrated Oil & Gas Electrical Equipment Oil & Gas Exploration Highways & Railroads Construction Materials Source: Authors based on Bloomberg Data 2015 33

34 TERM 2.4 COMPANY VALUE RELIES ON LONG - Fig. 39: Typical Lifespan of Intangible Assets INTANGIBLE ASSETS lived - Intangible Assets are long Years from % of the S&P 84 ’s value derives About 500 60 assets . Intangible assets refer to intangible - non physical assets like brand names, patents, regulatory licenses, corporate strategy, intellectual goodwill, capital, and reputation . Studies have shown that the 50 majority of intangible asset value derives from patents, on which are not balance accounted for clearly 47 . patents Based on Ocean Tomo’s valuation of sheets in S&P 500 companies, the share of intangible assets 40 has increased from 17 % in 1975 to 84 % in 2015 (see that Fig ) . This implies 40 the value of companies is . increasingly derived from non - physical assets . 30 Intangible assets are largely long - term investments . Patents have 20 - year terms and trademarks are 39 . Fig (see Copyrights, . ) renewable every 10 years 20 further, 50 years . Furthermore, intellectual capital last incurs - run paybacks . Research and development long projects have, on average, 15 periods year payback . - . all intangible assets are long - term, though Not 10 Goodwill is a common intangible asset derived from excess acquisition prices that is essentially just paper Aside . money and can be written off in any given year 0 that, investment in intangible assets exposes asset from owners Over long - term risks . the lifespan of these to Copyright Patent Trademark R&D - risks can lead term impairments or long assets, to Duration Payback Duration Duration of assets . As, the value of these write - downs these today necessarily they will not assets mean does carry based on Source: 1999 and Wiley 2001 Dabbah Authors . the same value tomorrow Breakdown of S&P 500 Company Valuation by Asset Type Fig. 40: The value of S&P 500 stocks is increasingly based on intangible assets Intangible Assets Tangible Assets 100% 80% 60% 40% 20% 0% 2015 2005 1995 1985 1975 2015 Tomo : Ocean Source 34

35 2.5 THE BOND MARKET IS EXPOSED TO LONG - TERM Maturities Breakdown of S&P 500 Debt 41: Fig. RISKS Most corporate bonds have maturities of 5 or more years focused is bonds The net present value of corporate Years term corporate of on the long - yields . Given the low 25 to 30 20 to 25 Greater than 30.5 current the especially debt, in rate interest low 15 to 20 10 to 15 5 to 10 environment, the present value of corporate debt net <=1 1 to 5 . 2015 In is mostly based on payments beyond 5 years , Materials debt issuers in all S&P 500 sectors except had (Millions, USD Debt Issuance ) average maturities of 10 years or more . When the that accounting for current bond yields, this means 1,000 majority present value of the bond payments of of the average bond comes from years 5 to 15 in most the sectors (see . 41 ) Fig Consumer Staples and Financial . Services have over 30 % of their present value from 11 . to years in the future 15 Further, since principal is not 750 paid maturity, they are exposed to loss of until principal and the risk of the debt being refinanced to a lower yield . The longer the average maturity, the the longer into the future analysts must look to assess of default sector . risk in each 500 S&P bonds issued in corporate the 500 of Most, 2015 will at least 5 years from now not reach maturity until 42 ) . Hence , credit analysts should focus on at (see Fig . covering of outstanding maturity period least the the of the long window of materiality, Because debt . this issuers repay their debt depends, in part, of ability to 250 risks ability to long - term respond . to issuer’s the on debt - term The on the market in 2015 presence of long in part to the low - interest rate environment . is due in 2005 , half of corporate debt But even and 2010 5 years or more . This maturities carried issuances of 0 in high yield environments, there is a - that even means 2005 2010 2015 . This demand meets market for long - term debt companies’ term . debt - long issue needs to Source: Authors from Bloomberg 2016 42: Net Present Value of Bond Payments for Sector Average Bond by Time Period Fig. term cash flows Bond NPV is primarily based on long - % of NPV 11 to 15 Years 6 to 10 Years 1 to 5 Years 100% 75% 50% 25% 0% Energy Utilities Materials Industrials Healthcare Technology Consumer Discretionary Financial Services Consumer Staples Telecommunications 2016 Authors from Source: Eikon Bloomberg 2015 and Thomson 35

36 S&P 500 Sector Average Bond Maturity By Time Period 43: Fig. often extend beyond 10 years sectors Maturities for Consumer Staples and Industrials Consumer Staples Basic Materials % of Bonds % of Bonds 33% 60% 22% 40% 20% 11% 0% 0% 25 to 30 20 to 25 15 to 20 10 to 15 5 to 10 0 to 5 > than 30 10 to 15 5 to 10 0 to 5 15 to 20 20 to 25 25 to 30 > than 30 Bond Maturity (Years) Bond Maturity (Years) Industrials Energy % of Bonds % of Bonds 33% 36% 27% 22% 18% 11% 9% 0% 0% 5 to 10 10 to 15 15 to 20 20 to 25 25 to 30 > than 30 0 to 5 20 to 25 10 to 15 5 to 10 0 to 5 > than 30 15 to 20 25 to 30 Bond Maturity (Years) Bond Maturity (Years) Utilities Technology % of Bonds % of Bonds 36% 33% 27% 22% 18% 11% 9% 0% 0% > than 30 15 to 20 10 to 15 25 to 30 20 to 25 5 to 10 0 to 5 15 to 20 10 to 15 > than 30 25 to 30 0 to 5 5 to 10 20 to 25 Bond Maturity (Years) Bond Maturity (Years) Authors Source: from Bloomberg Data 2015 36

37 PART III HOW FINANCIAL ANALYSTS EQUIP WITH BEAMS INVESTORS LOW SECTION SPOTLIGHT Valuation models used in equity research typically have a time horizon of • more than 3 - 5 years no and recommendations focus on the next 12 months . • Although credit risk assessments can extend beyond 5 years, rating action today is unlikely to 3 follow likely to materialize beyond risks - 5 years . from • Financial analysis often relies on companies’ capacity to adapt over the long - term, yet a comprehensive framework to assess adaptive capacity is missing .

38 3.1 EQUITY AND CREDIT ANALYSTS IN THE buy, Finally , fund managers • hold or sell securities on INVESTMENT ALLOCATION CHAIN a daily basis based on the recommendation of external internal - side) and (buy (sell - side) analysts . Asset rely a chain of intermediaries to owners on of - The in depth analysis company risks specific - Capital allocation make investment decisions . . place at takes this stage asset owners to asset players of chain a involves from investee : companies to managers a hunt and Equity credit analysts key role in the play swans for departments credit and research Equity . chain • first step in this The is the asset owner that examine the potential performance of agencies rating decides strategic asset allocation based on the on securities, disseminate information about companies structure of and liabilities its regulatory and securities, and make buy/sell/hold is informed by investment . constraints This process recommendations for assess or analysts) (equity stocks - performing forward - macro looking consultants the potential risk associated fixed with income interest rates, global growth, analysis economic of products addition analysts) to . the (credit In asset etc . The allocation strategy is generally investors, information that financial analysts deliver to 5 reviewed . years 7 to every a have also recommendations their assessments and of second order, - “feedback” effect : The selection • Then, equity and bond portfolios managed day are securities and recommendations analysts’ on (based to fund by dedicated internal teams or external day assessments) shapes the benchmark and changes the managers definition the involves process of . This ) 44 The . Fig (see funds index of . allocation mandates, including performance indicators and of recommendations equity and and assessments risk usually level are indicators These maximum . of flow impact fundamentally thereby analysts credit the defined bond relation to a benchmark (stock or in capital in financial markets . analysts play a key Thus, of . review usually takes place on index) Performance role of, assessment overall markets’ capital in and Performance an ongoing basis (e g . weekly) . . of types all with, . risk alignment targets bonuses rarely exceed . years related and 5 The Role of Analysts in the Investment Allocation Chain Fig. 44: Benchmark selection Structure of liabilities Security selection Asset Class Allocation Selection of Liabilities prescribe Investors select Macroeconomic benchmark and the time horizon of equities or bonds analysis leads to a investment universe the overall portfolio driving ultimate calculation of the drives to a large and the risk budget portfolio construction optimal allocation by extent sector and asset class country allocation Securities Research Investment Consultants Informs stock + bond picks; Undertake macro analysis to assessment - fundamental risk determine the risk profile of for companies occurs here each asset class Feedback Effect: Analyst recommendations also influence allocation of the stock market and indices on the  over or undervalued securities (due to mispriced risks)  past performance drives the selection of the whole a collective mistake compounded in overall portfolio construction.  benchmark Benchmark Study, 2014 for more info.) ii ° 2 (See Source: Authors 38

39 from . The 10 % of time spent company management 2 FINANCIAL MODELS ARE THE PREDOMINANT 3 . shows periods beyond 5 years discussing there is TOOL ANALYSIS EQUITY IN in - term outlook the of interest company . But the long does conversations of out coming information the these analysts research equity of role The intermediate to is complement To . needs data analysts’ meet - not short between company management and capital markets . term term longer with management from information the and earnings company of analysis Through analysts projections, quantitative conduct quality, equity research analysts management of short compare and results term - extrapolations business nuanced translate and communicate common company prospects using indicators . These the down information investment chain and further core of financial tasks analysis are primarily thus the . While investment concrete facilitate decisions . accomplished through quantitative valuation models to not is analysts of role future, the predict exactly earnings forecasts and communicate analysts must models of part only are research equity Valuation the outputs core These . recommendations investment of a function but are the primary means for reflecting are typically : on equity research analysts based modeling is company’s long - term prospects . Financial of of one a suite only tools that analysts have available a of ability The • to company’s management team to them when evaluating a company and its business operate change to adapt and effectively environment Qualitative risk disclosure and . • strength of a company’s past earnings The . management quality evaluations are others However, of prospects • The returns deliver to investment an nearly all analysts build financial models to assess going forward companies’ prospects . This is because valuation models direct and companies between comparison a for allow the analysts role key evaluate Thus, is to future of into enable the translation of earnings forecasts performance in Yet . are horizons time their so, doing investment recommendations . In fact, 96 % of . constrained Analysts do not treat time future all used form of to respondents some analyst our survey matter horizons time their fact, of a As periods equally . model of model, although the primary type valuation of artificially constrained due to the availability are varied (DCF) and users Discounted Cash . Flow between information, sophisticated for demand client limited employed Multiples commonly most the are Methods conflicts analysis, term - long of of lack the and interest, 75 % of the sample with respondents, models by survey future performance technical tools needed to forecast Cash on mainly relying Flow models (Figure Discounted part (see of these discussion depth in an for IV models 46 Each of these . uses different equations to ) the all receive could analysts Ideally, . obstacles) company translate analyst earnings forecasts and from need performance future on they answers that can be values market into financial statements however, practice, In . analysts company management compared models these of Each . companies between over corporate with conversations their of % 90 spend analysis, as carries embedded time horizons of also management teams discussing short - term prospects page next the on . detailed the (see . 45 ) . This in part relates to Fig lack of insight Fig. 46: The Most Common Valuation Methods in Breakdown of Analyst Conversations with 45: Fig. Equity Research Management by Time Period Analysts discuss the next 5 years nearly exclusively DCF and Multiples are the most common analyst models Analyst Usage % of Time Spent Discussing 100% Future Time Periods 30% 75% 25% 50% 20% 25% 15% 0% 10% 5% Analysis method method 0% Expected Value 3 50+ 21-50 11-20 1/4 1 6-10 5 Sum of the parts Sum of the parts Market Valuation Multiples Method Years Discounted Cash Flow Source: 2 ° ii Equity Research Analyst Survey 2016, n=10 Equity Research Analyst Survey 2016, n=6 ii ° Source: 2 39

40 3 . RESEARCH IS BLIND AFTER 5 YEARS 3 EQUITY year Valuation methods have a - 4 to 3 Comparable time horizons . Comparable valuation methods look at common The models used by equity valuation most recent at year one most, and, performance earnings The are . term two research - short inherently analysts . securities of value relative the determine to estimates models in equity research are dominant valuation The most common method comparable valuation is a of Flow (see models Cash Fig . 46 on and Multiples previous multiples on based a common metric like approach flow explicit make only models These . page) cash Earnings to Price . Earnings to Price on rely models on for . ) 47 . Fig (see average forecasts up to 1 to 5 years earnings trailing indicators or one year forward either each assessed the time horizons embedded in We companies compare to estimates based on price . The research model, showing that equity is rarely based on proxy for time of is ratio Earnings thought to Price as a more of are There . two or years 5 principal forecasts the by divided value enterprise gives it because horizon valuation methods : equity of relying on, a year of earnings . However, by typically, ratio current earnings or one year forecasts, this year 5 to 1 - have methods Valuation time Absolute not implicitly assumes linear earnings growth and does methods analyze the intrinsic value Absolute . horizons - are models Comparable . risks term long impute value of companies through forward - looking models of - are often and term short inherently a rough but term - Flow, Cash Residual group, this In . near estimates expedient way to value . Earnings companies and Economic Value models Income, rely on build outs price/volume momentum and stock models look at estimates earnings of with 5 - years, 1 over most recent performance, up to 4 years in the past, to . future the in Hence, years 3 - 2 made estimates intrinsic determine trends related to specific securities . value calculations exclude long - term risks . Fig. 47: Time Horizons of Common Equity Valuation Models term explicit cash flow forecasts, rarely extending more than 5 years ahead - Equity Valuation models rely on short Explicit Forecast Period (Years) 5 4 3 2 1 0 -1 -2 -3 -4 -5 Stock Price and Dividend Earnings Multiples - Expected Discounted Economic Residual Income Model Value Analysis Discount Cash Flow Momentum Price to Value Added Volume Model Model Earnings Patterns Model Model Damodaran 2010 and CFA Institute, 2016 Sources: Authors 2017, from 2  ii Equity Research Analyst Survey 2016, on was based on Figure review of authoritative sources compiled valuation methodologies and survey of practicing : Note a Specific mentions of years in academic material and survey responses were used to generate the box for each method analysts . to whiskers realistic interpretations of each method . Negative Forecast Period means the method looks backward reflect and the - and Forecast Period refers to forward positive looking estimates . results past 40

41 3.4 HOW SHORT - TERMISM IS EMBEDDED IN Stage constitutes period forecast in . ) page 45 1 This VALUATION MODELS 5 beyond that means This . ) 48 . (see models DCF Fig that years, affect analysts cannot assess specific risks Comparables do - cyclical anticipate methods not non . statements financial risks - or assessing By only . trailing the term long be methods leading 12 months, Price to may Earnings To this, analysts rely on perpetual address economic cycles . According to the U . S distorted by . extrapolation estimates flow . cash Typically, their of average the Research, Economic of National Bureau the are three key variables in cash flow models, the years business cycle lasts for 69 months, or . nearly 6 flow cash and rate, growth perpetuity forecast, explicit short the on only By comparable term, - focusing usually rate discount . The last cash flow forecast, methods value a company based on cyclical may growth the with conjunction made 3 to 5 years out, in inverse the ignoring factors, trends that might emerge stock’s rate and discount rate form the basis of the when another cycle begins . Beyond this cyclicality, flows value variables, these on Based . terminal cash comparables - long evaluate not do typically methods perpetuity . 48 ) . grow to extrapolated are (see in Fig term similar risks . Since companies are compared to is rate growth perpetuity This aggregate on based their in comparable companies approaches sectors, economic and growth applies across industries . This assume that all things are equal between companies or means growth rate is rarely company - the that and except their market the run - long the in that prices, While may sector perpetuity the growth . specific - rate 48 however, like treat should conceals, This . alike stocks the by mitigated is not accurate, be of effect its effect that companies face differentiated risks based on their . the discount rate business products . and models a extend their forecast period, analysts use To Even of value intrinsic the evaluate analysts when discount on . results DCF models, based In past rate cash explicit out build they companies, forecasts flow usually of the on based cost is rate discount the equity years . than more no usually for value Absolute 5 In cost . capital - - average - weighted or of the cases, both - limited calculations are forward by looking but are - 50 based on historic are equity returns . rates generally analysts’ forecasts of earnings . Analysts forecast future that premium the to refers equity of cost The equity cash flows by making exact financial statement replicas the over - free rate . This is based risk require investors looking process This . period - forward defined a for in on historical of risk averages and relates to the level creates a set of specific for company expectations time capture not does rate This . the at economy the performance including line item budgets and costs . A not non cyclical forward - - looking risks and is typically study of sell - equity analyst models revealed side of value term - long the Thus, . specific - company financial statement forecast periods of 5 years or generalized companies is based (see metrics on Figure 49 less . Our study revealed that some outliers forecast of Many . next page) , flows cash forecasted 49 the but as many as 11 years depending on the the sector a to from eliminated are business specific attributed (see at years 5 to out goes most model DCF typical only valuation . rates discount applicable broadly through 48: Cash Flows Before and After Discounting in Typical DCF Models Fig. Extrapolation Equity NPV Relies on Perpetual Growth Millions, USD) $ ( 7000 : Stage 2 : High Stage 1 Full 6000 statement financial Cash Growth Flows forecasts are each for 5000 term - and long year approximated with risk a formula assessment 4000 3000 2000 1000 0 27 25 47 45 43 41 39 37 35 33 31 29 49 1 23 21 19 17 15 13 11 9 7 5 3 Source: Morningstar DCF Models Years 41

42 Fig. 49: Effect of Discount Rate and Risk Premium by Industry Sector Looking and Miss Future Risks Risk Premiums are Backward - - free rates and risk premiums exceeds the net present value in 8 out of 10 Note: The sum of the cash flows discounted by risk term risk assessment and may expose investors to long - term sectors. This discounting of the future limits the value of long - risks Risk-Free Rate Reduction of Forecasted Cash Flows Risk Premium Reduction of Forecasted Cash Flows Net Present Value Millions, Total Cash Flow ( USD) 500,000 450,000 400,000 350,000 300,000 250,000 200,000 150,000 100,000 50,000 0 Energy Utilities Industrials Healthcare Real Estate Technology Basic Materials Financial Services Consumer Cyclical Consumer Defensive from Morningstar DCF Models 2016 Authors Source: 42

43 Fig. 50: Enterprise Value Attributable to Time Period and Explicit Cash Flow Forecasts Value is primarily based on the growth and discount rate assumptions used to calculate terminal value -- not on Explicit Forecasts. 20 6 50 21 - - 10 - 11 5 - 1 From Years 51 and beyond From Explicit (as opposed to extrapolation) Forecasts Percent of Enterprise Value 100% 80% 60% 40% 20% 0% Technology Energy Utilities Industrials Financial Real Healthcare Basic Consumer Consumer Cyclical Defensive Materials Services Estate n=107) from Morningstar DCF Models 2016 ( Authors 2017, Source: 43

44 3.5 ANALYSTS’ EXPLICIT CASH FLOW FORECASTS next 5 years, although, as previously shown, only - 1 FOCUS ONLY ON THE NEXT 5 YEARS from of the stock comes value this 15 % around of the timeframe . between leaves a large gap the This time analyst and stocks for period materiality horizons - term . Analysts focus their forecasts on the short To a issues Bloomberg consensus, industry quantify future consequence The of this gap is that most of to earnings estimates on survey research equity extrapolated, not explicitly forecasted . cash flows are their listed in responses The . analysts off tail database Based on Morningstar’s models from 2016 , around made in analyst % 74 forecasts with years, 5 after of 74 % of cash flows in models extrapolation, from are in the first 5 years . Less the first three years and 94 % even discounting (see Figure 50 , previous page) . after than 1 % of forecasts or 10 made are sample this in majority the that means This equity valuations do of Care, IT, and more years ahead, mainly in the Health not come but analysis looking - forward analysts’ from demonstrates Telecom sectors . This analysts issue standardized assumptions . backward - looking and cash explicit the their of all nearly forecasts for flow Fig. 51: Analyst Cash Flow Forecast Entries in Bloomberg Terminal by Forecast Year Analysts’ Explicit Cash Flow Forecasts Focus Only on the Next 5 Years Percentage of Responses 30% 25% 20% 15% 10% 5% 0% 6 1 3 4 5 2 7 8 9 10 11 Consumer Discretionary Energy Consumer Staples Health Care Industrials Information Technology Telecommunication Services Materials Utilities Source: Authors 2017, from Bloomberg Data 2015 3.6 RISK SECTIONS ARE NOT LONG TERM - Even the most forward - looking analysis on the sell - side does not look beyond 5 years . Some equity research sections firms qualitatively discuss long - term risks in long - term outlook sections . These incorporate increasingly Even next at look only though, sections, these the . concerns Governance and Social, Environmental, . years five growth They rely on market research analyzing new technology commonly projections over 5 years . There may be currently an opportunity to discuss long - term risks in these qualitative disclosure sections but the risk sections is . valuation may targets price from analysis risk Decoupling increase models the of horizons time the by constrained of scope the long assessment - term risk . 44

45 (See next drivers the on more of less or years 2 the on ON 3 . 7 CREDIT RISK ASSESSMENTS LARGELY FOCUS short term metrics on page 54 ) . - - THE 3 NEXT 5 YEARS risk financial of S&P generally determines profile the The of a debt issuer’s financial cushion visibility an issuer from 5 year averages of cash flow ratios, a imposes ratings credit of horizon time the on limit . years including of historical data 2 2 - 3 years of and Credit ratings) ratings (long - not term corporate do have financial (S&P’s Corporate Rating forecasts Ideally, . ratings to attached limit time formal a them forecast 2013 Slightly . ) Methodology can periods longer represent an issuer’s general creditworthiness (issuer particularly be for issuers that employed operate in or rating) credit his ability service debt until maturity to markets, stable the utilities industry . Similarly, as such however, ratings practice, In . rating) credit (issue are last focuses on EBITDA margins over the and 2 S&P assumptions, the based on quantitative and qualitative coming 3 years when 2 - assessing an issuer’s as validity of which are horizons longer declines — a key indicator for profitability the sustainability of importantly, assumptions Most considered . these the . cushion financial the ‘financial of sustainability and size the concern financial issuer’s an reflects and cushion’, health which time horizons does not Moody’s communicate explicit . thus to repay debt in periods of stress his ability as such metrics financial of assessment the for but only states that both coverage and leverage ratios, plays The cushion an issuer financial a key role in of in used historical are results financial projected and the indicates for health financial but assessment credit risk Moody’s industry (see specific corporate process rating - factors that indicate the Credit no more than 3 . 5 years financial methodologies) rating . However, the metrics cushion financial issuer’s an of size role decisive a play similar process that go into Moody’s rating are to very corporate for Moody’s, . assessments risk credit in restricted are They . S&P by employed those data the by weights example, places heavy on leverage and that and uncertainty companies provide around long - coverage ‘grid issuer’s an determining when ratios . In view of the business term risks and strategy indicated rating’ (Box 2 next . Similarly, when page) horizons forecast restricted financial issuer’s an of examining corporate credit risk, S&P emphasizes the cushion, implicit horizon of corporate the credit time of role an issuer’s risk profile, which is derived financial for years 5 - ratings be to regularly stated is 3 Funds such ratios debt as flow cash various from and issuer’s grade stable a in investment that operate . below) 1 (Box Debt/EBITDA or Operations/Debt from 51 S&P, . g . (e For environment . business Fitch) a from assessed typically are metrics flow cash Such speculative issuers, financial grade visibility the of projected are that results and data historical of mixture thus and credit of metrics, horizon time implicit the term constrained metrics These . are - short the over by ratings, shorter even typically . is focused typically which guidance, earnings company is Corporate Ratings Methodology S&P’s Generalized Box 1: Financial Risk Profile Modifications and 4 6 2 3 1 5 Cash Flow adjustments for - aaa / bb a+/ / aa bbb - a Capacity 1 a aa+ bb+ DIVERSIFICATION - a+/ a / aa/ bb+ bbb bb 2 bbb + a aa - ISSUER MANAGEMENT RATING bbb bbb a/ - / CREDIT bbb + bb b+ Competitiveness/ 3 ANCHOR bbb - /bb+ a - CAPITAL RATING Profitability / bbb STRUCTURE b bbb - bb+ bb Bb - 4 - bbb FINANCIAL POLICY Country & - b/b b+ bb+ bb+ - bb bb 5 Business Risk Profile - bb / Industry Risk bb - b+ b b - bb - LIQUIDITY 6 b+ Cash Flow Capacity: e.g. FFO/Debt, Debt/EBITDA: 2 years historical data, 2 - 3 year forecasts Competitiveness/Profitability: e.g. Competitive advantage, scale, EBITDA margins 3 year forecasts, margin volatility: 7 year historical data - Margin levels: 2 years historical data, 2 e.g. Economic & legal risk, industry growth trends, risk of secular change: Country & Industry Risk: - Forward looking, focus on the next 3 5 years Corporate Rating Methodology 2013 Source : Authors from S&P’s 45

46 Box 2: Moody’s Generalized Corporate Ratings Methodology Rating Factors that Determine Fundamental Qualitative/Subjective Credit Strength (illustrative generalization) Rating Factors (examples ) Weights across Management strategy 10 key industries* Leverage – 60 % 25 ISSUER and Coverage Corporate governance CREDIT & RATING Event risk – 46 % 15 Business Profile Regulatory risk 8 – 25 % Scale ... – 10 20 % Financial Policy ‘GRID INDICATED’ RATING 5 – 20 % Profitability Source: Authors from Moody’s Corporate Rating Methodologies 3.8 EMERGING RISKS AND TRENDS BEYOND 5 YEARS transition along go intelligence artificial and energy IMPOSE RATING ACTION TODAY ARE UNLIKELY TO with limited confidence in the risk itself and with a restricted predictability on impacts their of 53 case Corporate credit ratings are derived from a base metrics . fundamental Moody’s asserts that credit the most likely developments that reflects the scenario is harm of many “incremental, episodic” risks derives S&P . years - 3 next 5 the to input core the over delayed can credit on impacts The . uncertain be and flow cash an from forecasts, its ratings, namely likely to be “curbed are offset” by countervailing or 54 case which base expected scenario incorporates regulatory for timing specific Also, . over forces time industry “current and near - term economic conditions, . initiatives is required to change ratings Long - term policies” and financial assumptions, Corporate (S&P risks may be ignored because of their uncertainty . This . ) 2013 Methodology Rating scenario case base on most likely the reflects S&P’s current expectation - Qualitative the of assessment well a not is term long - When . years 5 3 next the over developments analysts defined process . In our workshops, credit ‘the what on constitutes expectations likely most not do methodologies ratings with stressed that stop years 5 - ratings 3 change, next the over scenario’ credit may ratings the Instead, grid ratings indicated - grid . adjusted are be may adjustments Such . dynamically point - . discussion for Grid starting the be just indicated preceded outlook’, ‘rating a by a that indicates which on that committee ratings a to go ratings deliberates next 6 - 24 months due to in changed the rating may be qualitative factors including governance and adaptive changes fundamental business or economic in potential specify not do methodologies The . ) 2 Box (see capacity the conditions . Thus, the rating outlook indicates . factors the and questions discussed on this topic The next potential direction of a credit rating over the 2 rating interview with agencies did not point towards but years, expectations not about the long - does reveal additional factors the on documents specific and term trajectory of credit risk . and lack a suggesting addressed, questions specific of assess sophisticated frameworks to and governance assessed sometimes and trends risks term - Long in are when capacity term - long adaptive face risks companies credit risk analysis but are to induce rating unlikely Intelligence . as such Transition Energy the and Artificial action . today most are trends and risks Exogenous rating induce to likely the affect they if today action the may assessment risk Environmental time extend to likely highly are they if . e . i scenario, base current horizon developing are S&P and Moody’s . 5 low a with Risks . years - materialize in the next 3 change methodologies to assess the risk of climate medium the effect - or in chance of coming into short , 2015 Heatmap Risk Environmental Moody’s . . (e g term, 5 beyond materialization of likelihood high a but Analyze Moody’s Carbon Transition Risk 2016 ) . As to will but analysis, risk years in assessed be may credit of part analysts may assess the risk of this, credit believed ratings be reflected in current only if they are 20 . over the next decarbonization - 30 years However, 52 or to opportunity to an pose . a material threat issuer risk or analysis credit in evaluated are risks such if even strong are beliefs enough to However, such seldom have will they that likelihood the publications, related such the as Exogenous . today action induce rating risks small effect credit ratings is current . decisive a on 46

47 Actions Effect of Moody’s Environmental Risk Heat Map on Rating Figure 52: Credit Ratings Reflect Environmental Risk Only if the Risk Already Has Material Implications of Risk Exposure Assigned Materiality Characteristics Rating Action Examples Implied? of Risk Risk Category and Materiality Direct exposure to market impacts Coal Mining, YES of environmental regulation; Already Immediate Unregulated Already occurred or material elevated risk implications for cash flows, likely within 3 yrs. Power Generation revenues and margins already felt Clear exposure to environmental Oil and NO ; material impacts unlikely next 3 - 5 Gas Refining, Emerging risk But possible in the next 3 years; years elevated risk Automobile beyond 3 years. Manufacturers flexibility to adapt Clear exposure to environmental Integrated Oil and risk; material impacts unlikely in NO Emerging Gas, Regulated 5 or more the next 5 years; uncertainty But possible about moderate risk Electric and Gas years beyond 5 years. the implications for credit quality; Utilities high flexibility to adapt No sector wide exposure to NO environmental risk or Limited Mass Transit, Low risk Unlikely in the materiality consequences not likely to be Retail and Apparel next 7 years. material to credit quality Source: Authors based on Moody’s Environmental Risk Heat Map 2015 47

48 suggests 3 NO EVIDENCE OF A CLEAR FRAMEWORK TO are resources limited that 9 analysis Our . assessing - innovation and effects in locked . mobilized DISTINGUISH A ‘KODAK’ FROM A ‘GE’ on largely adaptive of capacity relies The assessment adaptation the The second dimension of is ability to generic analysts credit and equity When . assumptions away business’ core ‘doomed a via from diversify prospects and ‘extrapolate’ near - term ( 3 - 5 year) and risk emerging the before acquisitions divestitures ( horizons they ratios cash/debt long 10 - 30 years), over gets priced . market the by in the often term - long . over adapt will companies assume acquisitions . Near Ability - term financial finance to business This adaptation process can occur at the metrics ratios indicate debt to flow cash as such the switch from ICE g . car manufacturers segment level (e . evolutions if such finance to companies the of ability functions electric engines and integrate auto - pilot to to they decide . Both credit and equity research through or business) core their changing without put analysts emphasis on comes it when dimension this away . . g diversification utilities from the old business (e . to the adaptive capacity of a assessing company develop energy and capacity renewable distributed on based However these financial ratios are usually - ) capacity fired : coal their efficiency programs and sell looking - backward term 3 near prospects ( - 5 years) or therefore can revenues and costs regarding and with involving industries many assets term - long • For For . changes structure this if deteriorate dramatically emissions, in - be dimension first the can locked S&P the of our ratings, credit and understanding of fixed assets, CapEx evaluated via an analysis is actions rating the that methodologies Moody’s expenditures . contracts term - R&D long and plans, to take place when these ratios start primarily - 3 a suggesting deteriorate, year 5 horizon for the to The • second dimension is more difficult evaluate . analysis of this dimension on the it of since the mostly depends ability to management buy and sell business segments at make the decision at the right time . The to Ability the the right time : before they get discounted by management the whether is question key will other companies for race Some . market the lose might or . at the right time decision too late the make new some technologies like Kodak and adaptation to Moody’s, for example, emphasizes that management . GE might reinvent themselves constantly like strategy constitutes a when consideration key a company’s exposure to carbon transition assessing Limited analysis of disruptive and effects in - locked 56 . highlights Similarly , S&P management's risk innovation assess commonly Analysts . potential and strategic with dealing when role important of flexibility the product diversification of the portfolio, to risks, such related those as operating factors ESG contracts the level of R&D investment . However, and (see : S&P Risks in Corporate Credit Ratings - ) 2015 ( ESG the of analysis depth - in of evidence find not did we . Overview) An - inertia and investment plans . A to fixed assets related is power sector : this of illustration good the term - In short the company which by of focus view • to technology exposed highly is sector The and management and shareholder - investee dialogues are energy transition and the to related risks policy it ), 39 page (see however characterized, often seems to subject term a locked - in effect due to the long - ability to management of assume to reasonable the (e . of the fixed assets nature . g power plants) . ‘GE’ be (to term - long the over company the reinvent a rather ‘good of dimension than only is ‘Kodak’) a one • Databases technology, (including plants power on the management . ’ Rating methodologies and existed planned years, age, additions, etc . ) have for credit by workshops during us to provided information expected of estimation allowing cash flows from the dimension analysts do not provide details on how this type - term . of asset over each the energy long it that suggesting addressed, specifically is a not is a major the analysis and mostly based on of focus only have recently agencies rating Credit • The same subjective judgment call of the analyst . acknowledged for the value of asset - level data where research, equity applies conclusion did we to environmental risk in infrastructure assessment find approach the to sophisticated of evidence not 55 ratings expanded be can practice This . . credit further topic . This topic would, however, require companies Similarly, the to R&D budget of • exposed research on both existing and emerging practices on changes disruptive terms analyzed of in rarely is the topic . It will certainly be a key focus of the future investment no almost : technologies breakthrough in developments regarding Energy Transition risk ask about it . . companies report this and few analysts assessment (see page 55 ) 48

49 perspective Adaptive Capacity from the Investor’s Fig. 53: There are three levels by which investors can assess susceptibility of term risks - portfolios to long DIVERSIFICATION • PORTFOLIO Turnover HORIZON LEVEL • EXISTING COMPANY DIVERSIFICATION Acquisitions LEVEL CASH AVAILABLE • MANAGEMENT MINDSET • • R&D Emerging business segment INVESTMENT NEW • REAL INVESTMENTS ASSET • LOCKED - IN LEVEL EFFECT Declining business segment 30 40 25 35 20 0 15 10 5 Source: Authors 49

50 FEEDBACK FROM INDUSTRY ENGAGEMENT WORKSHOPS: HOW ANALYSTS EQUIP INVESTORS WITH LOW BEAMS Feedback Summary Supporting Quotes Section Analysts generally agreed with our assessment “It’s fair to say that analysts make short term - that forecast horizons in equity research assumptions and extrapolate long - term - - seldom go beyond 3 5 years, where cash flows side Analyst growth.” Buy - outside of this focus are merely extrapolated. “Analysts integrate long - term risks are thus not genuinely - Long term risks by adjusting term growth rate by 1 the long - - Senior - 2%.” accounted for in valuation models, but are at side Equity Research Analyst - Sell best approximated by a tweaking of assumptions such as growth rates. Several TIME analysts highlighted that time horizons and “ I base my valuation on the current stock price” - HORIZONS side Equity Research Analyst valuation methods employed vary across - IN EQUITY Sell sectors and across research firms. That is, RESEARCH there is considerable variation in actual forecast horizon between 0 and 5 years. Further, an equity research Managing Director mentioned that his department employs forecasts of up to 7 years, yet this appears to be an outlier. As a result, the short term focus - of equity research was validated. “Valuation models are one of a suite of tools for Analysts challenged our assumption that analysts. They are not a summation of the whole equity analysts’ time horizons are primarily investment process.” - reflected in valuation models. Equity valuation Buy - side Analyst THE models and price targets are one important PREVALENCE term risks can - “The integration of long - term risks into channel through which long OF enter investment decisions. Yet, assessment investment decisions may occur at a sector VALUATION of management quality and qualitative risk allocation rather than individual security level.” – MODELS side Analyst disclosures also inform equity research - Buy products, and should thus be examined when studying the time horizons of equity analyst. 50

51 PART IV THE DRIVERS BEHIND THE LOW BEAMS SECTION SPOTLIGHT analysis obstacles to longer time horizons in • Key relate to the availability of data from financial issuers, the lack of framework for long - term risk assessment, the limited demand from investors, to and - benefit attached cost more sophisticated analyses . the • . for long - term risk analysis are emerging but remain scattered Frameworks

52 - 4.1 ANALYSTS FACE MULTIPLE OBSTACLES TO A LONG in of be affordable environment the current economic TERM VIEW least, research equity . Last but not our analysis no that suggests there is currently simply demand for restricted by Financial analysts’ time horizons are - that the average Given . analysis financial term long the to obstacle important One . factors multiple - long even for periods only holding portfolio equity - long is research securities in risks term integration of it just ), (see is seems page managers 58 months 21 of relevant data from issuers . Company the shortage and ‘long - questionable whether investors in general – disclosures only cover or looking backward often are – investment term’ investors in particular follow the of - thereby analysts future, term depriving the near long - consideration the strategies that necessitate of - long term the on expectations build to needed metrics term risks, thus kicking the can even down the further generally are analysts prospects Similarly, of a security . road Furthermore, . interviews with sell - side equity well not with frameworks that enable equipped research the that suggest agencies rating credit and . over financial long - term the projections - short are analysis financial term clients primary of practices best the as such — innovation Methodological traders deliver to pressure face analysts Internally, . 3 broader on required is — a . 4 section in showcased side - short term results . In a 2014 of 365 sell - survey term risks . Yet, even and scale, for a variety of long - equity generating that reported % 44 analysts, increased with data term - long of availability investment their to important was fees banking 56 and additional the frameworks, assessment - risk cost powerful on compensation . pressures Analysts face term analysis may not attached to sophisticated long - time . horizons their Figure 54: term Risk Analysis - Obstacles to Long term risk assessment Analysts face four key obstacles to long - Source: Authors - BENEFIT SHORTAGE OF DATA FROM NEGATIVE COST TOOLS ISSUERS ANALYSIS High costs for sophisticated analysis: looking disclosure: Introducing Companies primarily - Backward report backward looking financial data; some more sophisticated forward - looking analysis will provide cash flow forecasts, yet usually limited to 5 imply additional costs, potentially offsetting the years. benefits from better long term risk management. No standard: The existing guidance and regulation Restricted research departments: Declining disclosure don’t specify the applicable time budgets for equity research and understaffed on risk - research departments call the viability of more horizon and provide no incentive to cover long sophisticated analysis into question. term risks. SUPPLY DEMAND LACK OF LONG - TERM RISK NO DEMAND FOR LONG - STOP ASSESSMENT FRAMEWORKS TERM ANALYSIS Need for methodological innovation: Integrating Limited demand from investors : The fee term risks in existing models requires - long side equity research is based on structure of sell - methodological innovation (e.g. extending forecast volume and thus heavily tilted towards high periods, developing scenario analysis, etc .). volume traders . Even ‘long - term’ investors trade - term research. frequently and don’t demand long cenario analysis could Need for standardization : S supplement existing models, but regulatory or Limited demand from companies at risk: industry wide efforts may be needed to allow Potential self - selection bias due to issuer - pay comparison between issuers. model, where high - carbon issuers (e.g. Exxon) are unlikely to pay for enhanced 2 ° C sensitivity - test based ratings in voluntary system. FRAMEWORKS 52

53 4.2 COMPANY DISCLOSURES FOCUS ON THE SHORT - How Company Level Risks are Transmitted 55: Fig. TERM to Investment Decisions disclosures The horizons of limited corporate time Corporate Disclosure - to transmit term ability analysts’ restrict long risk Companies disclose the signals Financial analysts owners asset and, to – . risks and opportunities disclosure corporate ultimately, asset owners rely - on affecting them companies and assess risks around their credit to value communicated is information Such . right) (see profile including annual or quarterly through various channels, Securities Analysis analysts reports, direct conversation between and Analysts assess these risks some financial management, and . To press or releases and opportunities and extent, these channels cover forward - looking make investment : can They company’s disclosures contain expectations a recommendations presentation a guidance), (earnings earnings on future of may harm the company (e . g . potential risks that of section ‘Risk Factors’ in discussion K), and a 10 Investment Decisions risks and trends . deal management’s strategy to with Analysis drives the forward - looking information is such even However, investment decisions of . Earnings guidance short - mostly is term on focused asset owners, ultimately estimated capital or margins, earnings, revenues, affecting corporate cost of seldom spending looks and quarter, next the for capital and liquidity year . Similarly, discussions of risks and beyond 1 term - short around centered largely strategies are 57 Source: Authors Source: Authors and expectations . With such restricted developments information long on - term company metrics, risks and risks long term strategy, analysts’ ability to - integrate ancillary usually is analysis ESG research, equity In to models – thus informing long - term into their ESG not the is analysis core . research fundamental to . is - making - decision investment limited In research equity . analysts equity of work ESG departments, serves three typically research - TERM RISK 4. 3 ESG FACTORS OVERLAP WITH LONG principal purposes : • Risk characterization : ESG analysis encourages across A taxonomy of ESG Risks has been developed where they risks of taxonomy a make to analysts of research . the late 1990 s, a new type Since sectors might of impact the capture To . otherwise not assessment has experienced exponential growth : the of uncertain risks and opportunities outside the realm Governance Environmental, Social and (ESG) factors . of risks traditional analysis, analysts must categorize multiple on companies score primarily analysts ESG time like factors severity, using and horizon, by of set a complimented sometimes factors, with associated be not may that factors probability, data like consolidated looking - backward qualitative 60 other risk of kinds . field’s The . emissions carbon driven has development and companies by reporting ESG of development the Scoring • ESG on performance Tracking : ratings and related standards . development the of factors can help analysts produce ratings for socially Certain responsible investors . socially responsible analysis is primarily ESG used by socially responsible investors in invest only may ESG high with securities . investors ‘ESG integration’ is presented by often ratings . a as agencies and investors way to integrate long - term risks into investment decisions given that most analysis can : analysis fundamental Tweaking • ESG and not often are issues social covered environmental drivers help analysts model specific commonly frameworks . In practice, by term short risk assessment - affected growth terminal include risks ESG by rates of ESG scoring primarily serves the need socially the three rates discount and are . two out of These to investors responsible scores use these who and data in the affect not do but models DCF variables key used companies or reweight portfolios . It is also screen Further, . last 42 page (see estimate flow cash ) 'mainstream' to a lesser extent by some to investors analysts can construct scenario and sensitivity their marginally on report portfolio of profile ESG the or around risks . These processes are not analyses ESG g . (e portfolios into the analysis integrate . results of the however valuation, fundamental to core . exclude . companies that make cluster bombs) 53

54 More ESG is not sufficient to generally, analysis alone Fig . 56: Some ESG Factors are Material to of address long types risk we might consider all - term , but Not All Investors be . in As black appear that swans ‘white dark’ to the such, term - long from risk may assessment ESG differ FINANCIAL RISKS ESG FACTORS the following ways : assessment in risks to 1. The scope is usually not . limited material all Most scoring systems and social major capture with companies' environmental issues associated Material Material that Externalities materiality . activities financial their of regardless risk ESG Non - ESG are risk issues, material Some prioritize frameworks to not material see auto industry example in though . (e g . SASB, investors Long - term risks not 57 ) . Fig . captured by standard models usually limited scope environmental, 2. The is to - related issues, leaving social and governance term risks, e - long many . technological . g autonomous as such developments of out cars, Source: Authors . scope have of impact on asset value and cost - cling no is analysis ESG of output The not 3. usually . 57 ) . Further, this factor does not capital (see Fig translated metrics that can be into quantitative to present high magnitude risks nor externalities factored in - regarding long easily assumptions society . Other factors, such as Materials Sourcing, risk premiums or term cash flows of companies, earnings, bear direct and value, asset relationships to rating credit in factors risk . with cost of high capital while also presenting risks magnitude and . probability 4. Finally, ESG integration does not induce changes in target prices or rating action in standard practice Risks cases some in overlap Financial and Risks ESG some but have line items models for ESG factors . to produce Material ESG risks . Many risks in our being advances made in this area but are Some ESG externalities with overlap not might taxonomy equity are not mainstream in credit ratings or Further, risks material all not are . above) (see . research In percentage of cases, term - long necessarily . small a long ESG with overlap risks financial term however, - Empirically, connected tangentially are factors ESG to to . Thus, ESG not analysis is bullet silver a factors For factors looking - forward and financial example, . - a but risks, term it long measure and categorize is taxonomy SASB’s sector Automobile in risks ESG of show analysis ESG practices best Several . step first in - bears a limited relationship to financial and forward extended this - long the to term be can practice . how - risks . Factors like Materials Efficiency & Recy looking SASB’s Materiality Map: Sustainability Topics Likely to be Material for the Auto Industry Figure 57: High Cost of Issues likely to be material for Probability/ Externalities Value Asset Earnings Capital the Automobiles Industry Magnitude Materials Efficiency & X Recycling Labor Relations X X - Fuel Economy, Use phase X X X X Emissions Product Safety X X X Materials Sourcing X X X X Source: SASB 2016 54

55 The agencies - term risk assessment framework under long Proposed ESG frameworks from credit rating to 5 development for energy transition risks may apply could extend the focus of risk assessment beyond S&P’s proposal more sectors . The first findings of these two work the defines ratings ESG years . for need to as term - long the and years 5 2 as term medium streams revealed can that scenarios risk for the on of used by analysts as inputs to build assumptions be the ESG risk profiles beyond 5 years . To determine flows . - long companies’ Models term margins - long and - medium the assess to plan they issuers, term and cash the environmental energy and carbon of impact regulation calculate and social risk exposure with the the on at specific companies of margin profit net medium - term weighted more highly than the long - 60 - time, which can be used to forecast term points - . in While this type of analysis could factor into is operating ET The . ) rating credit a itself by not Risk it assessment, risk credit 58 . Fig (see flow - cash It overall the extend not may developing such a scenario . thus and consortium currently is of horizon time . 40 often regional parameters, - ratings 30 of set a includes and sector specific . equity research firms have the quantified Some - : Experimental Approaches to Long 58 Term Fig. ESG taxonomy via valuation effects of an financial Risk Integration model inputs . One example is framework the described in Morgan new report “Embedding Stanley’s into Valuation : Global Framework Sustainability 60 Analyzing for ESG risks and Opportunities . The ” factors framework identifies the most material ESG for Profit Impact on Net each sector individual the and applies these factors to Margin from Probable models inputs of valuation including volumes, pricing, Decarbonization revenue, capex, asset lifespan, etc . As such, the Policies framework applies ESG factors and translates them into valuation impacts that can apply to quantitative Free Cash Flow valuation model . This approach expands the types any risk that of and models valuation in included be can capture might consider we risks linear - non thus some to ‘white swans that appear black in the dark’ . be . DEVELOPING NEW VALUATION METHODS 4 4 Transition risk analysis opens the door to long - Energy valuation More . term specifically, in the techniques 2016 2017 2018 2020 2025 of climate context the negotiations, international of transition risks' has become more question 'energy ii ET Risk Project Briefing 2015  Source: 2 of prominent on the agenda the finance sector : develop To . Asset - level data further such approaches, an convened - Stability Financial The • for industry methodological research discovered the need Board necessity and specific - country Financial Related - Climate on Force analysis, Task led therefore the level assess mobilize real asset power . g . (e databases to Disclosures (TCFD) to the numerous climate fields) disclosure and come up with and looking - forward aggregate to regimes plants, oil specific assess disclose to how on recommendations country and data on corporate long - term assets, - climate annual largely are missing from corporate that concludes report first The . risks related which related are Such a the in development under is database most climate initiatives disclosure . - reports largely from financial risk . disconnected project ET Risk 61 assessment . Advanced quantitative techniques . Advanced the such of utility enhance may methods statistical Carlo data probability Monte . models a create European Commission finances a The • consortium thousands on based outcomes possible distribution of S&P involving Kepler Risk) Transition (Energy Global, multivariate of of simulations valuations under , Oxford, of University the Carbon Cheuvreux 63 This kind of model assumptions enable analysis . may Tracker to ii ° 2 and CE (lead) Initiative, CO - Firm, I 4 sufficient data exists . multiple of - long term variables if of develop a methodological framework and a suite could analysts equity on provide a this analysis, Based long - term energy risks . assess to tools transition future of estimates probabilistic of . range flows cash . 2017 in expected are results first The 55

56 Fig. DCF . Using a simplified set of mostly global : Bloomberg Carbon Risk Valuation Tool 59 Impacts Bloomberg tool Risk Valuation he Scenarios’ Effect on Exxon Mobil Share Price parameters, t Carbon on , in 2015 and available ) the 59 . (Fig launched Valuations are sensitive to long - term scenarios the to scenarios five offers Terminal, describe Share Price (USD) future . pathways oil companies in a low carbon for 100 in for flows cash DCF oil alter scenarios The companies prices models . Bloomberg’s scenarios include lower oil The analysts and policies tool allows decarbonization . 75 assumptions on, for example, oil prices, gas to adjust extraction and reserves, production, gas oil prices, & suggest a revised stock price . costs and 50 ADAPTIVE 4 . 5 ASSESSING CAPACITY 25 the context of obstacles in One of the main identified these methodological developments relates to the made the adaptive assumptions to regarding be 0 long capacity term - risk Most . companies of the focuses on the risks affecting the analysis to date revenues derived from long - term physical assets like oil the fields, putting emphasis on power plants and Current Price their . - in locked lifetime This with associated effect $50/bbl Oil Price $25/bbl Oil Price the potential magnitude write - approach informs of 5% Oil Price Decline but faced by the be owner offs cannot 1 : 1 translated at related stock - In - value a into certain . risk for the at assets physical the retrofit can companies cases risk, price before sell them to competitors at a reasonable Prompt Decarbonization Policies Sudden Decarbonization Policies other gets in, acquire the companies to priced risk Source: Bloomberg New Energy Finance 2013 from risky innovate diversify away activities, or simply As to gradually re - orient their business lines indicated . that on page 49 , our research suggests analysts 60 Fuel Risk Premium Fig. : The Fossil capture capacity' as a qualitative heuristic, 'adaptive the sometimes reflect it 'informally' by adjusting and framework to use formal risk premium, but do not a systematically assess and value this dimension . BAU Scenario: Oil demand high the As proxy for adaptive capacity, analysts tweak a reflect of the directly premium risk to uncertainty Approval of more high cost are that premiums Risk . sustainability business core projects to meet demand to are used company a of value present net the assess past based share volatility prices, of on thus typically company ignoring company a of to a of ability the High costs/low margins in BAU scenario . Emerging ) 9 . 3 section (see risks future to adapt such as Carbon Tracker’s (CTI’s) analysis of approaches in tackle sensitivity to this volatility NPV oil prices Higher sensitivity to oil prices 62 compares present net the . CTI the issue of value scenarios upstream oil industry in two A : different Increased volatility in BAU scenario usual scenario and a 2 ° C aligned scenario . business as relative to 2 ° C scenario that the BAU scenario is more sensitive oil CTI to shows implies a higher risk premium to which volatility, price risk discount future cash flows (“Fossil Fuel Premium”, a NPV ), and thus potentially 60 higher . for oil and Fig C ° RISK PREMIUM 2 RISK PREMIUM in gas scenario ° majors C the 2 in cost high which > SCENARIO BAU SCENARIO the are projects approved risk . not adjusting By risk future the integrates CTI way, premium this in Markup = Fossil Fuel Risk Premium profile oil . techniques valuation into majors gas and of Source: Authors based on Carbon Tracker 2016 56

57 4.6 RESEARCH BUDGETS PLACE LIMITS ON LONG - Decrease Equity Trading Commissions 61: Fig. TERM RISK ASSESSMENT Trading commissions have decreased since 2009 to long - term risk assessment is likely Sophisticated Given nature, costs nascent their . increase research Equity Trading US Europe Bn Commissions ($ ) lot the is still a there of uncertainty on additional cost - the related to long However, term risk assessment . 16 of forecast periods, the use of multiple extension scenarios and level the access to physical asset data 14 imply are likely to . additional costs, even if marginal 12 research contraction of budgets has forced equity The departments to focus on their most profitable 10 activities . The demand for sell - side equity research has decreased since Global Financial Crisis . Increasing the 8 to and decreasing access flow into asset information funds since 2008 have limited actively managed 6 for research and lowered equity investors’ demand As (see 61 and 62 ) . . a result, revenues research Figs 64 have been forced to cut costs . heads of research 4 - measures, such as reductions in cost Recent cutting analyst teams, further decrease the viability the size of 2 approaches - term risk integration of for innovative long Long - term risk assessment departments research in . 0 cost may analyst present an opportunity for lean 2009 2013 for may Thus, . teams additional be research unfeasible Source: Greenwich Associates, 2013 20 - 15 cover . already who analysts overstretched stocks European Commission’s proposal The an update of for 62 : Budget of Sell Fig. - Side Equity Research Firms the Markets and Financial Instrument Directive, MiFID 2018 brokers require will ), to Jan effective (likely II - Sell side research budgets have decreased since 2007 charge fees for their research, creating clearer demand among and triggering differentiation strategies Bn Estimated Aggregate Budget ($ USD) research encourage firms . Regulations like MiFID may 9 research innovation equity in . the Long - term risk assessment is likely to require The publication of multiple ratings and valuations . 8 horizons of extension involves time uncertainty more suggest and the use of scenarios . First experiments by risk that these long - used be will assessments term 7 to investors sensitivity than inform basic ' tests' rather require will asset valuation . They therefore the on publication of multiple results based different 6 scenarios, thus adding complexity to analyst reports . is used, if a unique scenario Even the 'long - term' credit different rating or a target price is the likely from to be ones, standard which are primarily designed for short - 5 . term next page) (see Indeed even if an investors analyst anticipates a sharp drop in value in 5 to 7 years, would sense make still it to recommend buying and 4 the security the for clients next few months to holding portfolio over turn who regularly This increase . in their related - communication drive to likely is complexity on It costs is . costs research additional the of top up, 3 hurdles likely to the face regulatory also that given 2013 2009 2008 2007 2006 2005 2004 2012 2011 2010 has regulations of emphasis recent - user the on been Source: Frost Consulting, 2013 information friendliness of the . 57

58 - Mean Dollar 63: Fig. Weighted Holding A LACK OF DEMAND FOR LONG 4.7 - TERM ANALYSIS Period of Institutional Investors DRIVES SHORT - TERMISM IN THE ANALYSIS Institutional investors hold stocks for 1.5 years on average - Demand is heavily tilted towards short first term . Our credit with sell - side equity research and workshops Years rating analysts suggest that both categories primarily 1.8 sell serve investors with short term horizons . - side For revenue correlates with transaction volume, research, giving hedge funds and other investors with high 1.2 trading volume a lion's share of their total revenues despite their relatively lower weight in assets under management According to our interviews with credit . 0.6 one rating agency perceived bond traders as analysts, their though even audience, main their business not depend on audience interest . model does 0 agency viewed their ratings as equally Another applicable empirically to all types of investors but few 2003 2005 2001 2007 1985 1987 1989 1991 1993 1995 1997 1999 2009 . exist investors term - long truly risk assessment There is no demand for long - term - investor Institutional . investors term even long from : 64 Fig. Average Corporate Bond Holding Period stock holding periods have remained flat at around 1 . 4 Average corporate bond holding period is 1.5 years 65 . 2 /Mercer A ° . ) 63 ii Fig (see s 1980 the since years Years ) research on turnover rates of long - only ( 2016 equity 1.8 managers revealed average annual turnover fund implying rates of period holding average 21 , % 58 of Fig . . In the bond market, corporate months (see ) 64 1.5 64 (see average on years ) 5 . 1 for held only is debt . Fig . The offers therefore investors of horizon time short - term long produce to analysts for incentive little 1.2 . research It is unclear whether high portfolio turnover drives 0.9 risk the lack of demand for long - term assessment or investor . demand it from results With and annual for fund returns, quarterly even not might have managers 0.6 any appetite for investments spanning multiple years . may hand, But, on the other short - research term 2012 2006 2015 2014 2013 2007 2011 2010 2009 2008 holding decrease the would that funds of period Source: BlackRock 2016 long otherwise - be term more . only Mutual Fund Managers 65 : Portfolio Turnover of Long - Fig. The average equity fund manager portfolio holding period is only 1.7 years Turnover Assets Under Management % of Total # of Funds % of AUM 40% 300 30% 200 20% 100 10% 0 0% 1.25 0.67 0.59 0.56 0.53 0.50 10.00 5.00 0.71 0.77 0.83 0.91 1.00 1.11 0.63 1.43 2.00 2.50 3.33 1.67 ) Turnover Period (Years ° ii, Mercer & The Generation Foundation 2017 Source: 2 58

59 FEEDBACK FROM INDUSTRY ENGAGEMENT WORKSHOPS: THE DRIVERS BEHIND THE LOW BEAMS Section Feedback Summary Supporting Quotes Analysts completely agreed that “A thorough discussion of a small number of - corporate disclosure is insufficient to key risks would be preferable to a very high - - assess long term risks to their business level presentation of all kinds of risk.” models. Existing risk disclosures were Equity analyst CORPORATE criticized for being too high level. DISCLOSURE “Spending time assessing very long term Clearly, companies could provide better - data on the risk facing their businesses. risks might be difficult to justify to clients if the risks are not very likely to materialize.” - - side equity research analyst Senior sell Analysts partially disagreed with our “Given the limited capacities of sell - side analysts, a development of a term risk hypothesis that long - term assessment is too costly to pursue. On methodological framework for long - risk integration may be more likely on the the buy - side and in credit risk analysis, Managing Director, Sustainable buy it would be pursued if it could be done - side.” - HIGH COST OF Finance feasibly. There, the issues are more ANALYSIS side research, technical. In sell - however, costs are an issue and centralized long term risk providers - could lower costs across the industry. “Long term risk analysis requires the use of - - term risk Analysts agreed that long assessment may not work within sophisticated methods such as Bayesian current methodologies. Valuations or statistics, this may be too challenging for analysts; further investors may not be credit risk assessments might require LACK OF TOOLS FOR advanced computation that analysts do prepared to evaluate results based on such - TERM ANALYSIS LONG - not currently perform. Further, side analyst Buy analyses.” - calculating the probability of a range of scenarios may be outside the scope of “Our analytics team could analyze scenarios current analyst methodologies. but we don’t do that.” - Credit Risk Officer side The lack of demand for long - term risk “The highest volume customers of sell - side - equity research are hedge funds. assessment was controversial. Sell term This prevents a stronger focus on long - analysts indicated that their biggest and Vice President of ESG Research fastest growing client base is the hedge - risks.” fund industry, which pushes analysts to - side - term. Further, several analysts be short “Asset managers will pay for sell research if asset owners ask for it” confirmed our view that the lack of - Senior demand for long - term analysis is a key Side Equity Research Analyst - Sell obstacle, and highlighted that a push towards longer time horizons needs to DEMAND FOR LONG - come from investors. Yet, managing TERM ANALYSIS directors of credit agencies and research firms pushed back on the lack of demand, saying their mandate is to produce the most accurate research. Hence, even if client demand focuses - term information, analysts on short term analysis. This should perform long - contributes to our view that analysts - term should lead the dialogue on long risk assessment. 59

60 Section Feedback Summary Supporting Quotes “Governance is a gateway to assessing Analysts stressed that assessment of environmental and social risks” - Vice adaptability is critical to understanding President of ESG Research term. They pointed out that - the long this topic was understudied in our “Soft indicators such as management quality research. A framework for understanding adaptability seems to be or risk management practice are sometimes missing, though. ESG analysts said that employed to approximate a company’s advanced governance metrics, which ADAPTIVE CAPACITY flexibility or ability to detect impending can be used to assess adaptive capacity risks, yet such factors are difficult to to an extent, have been developed but incorporate into financial models in a side ESG - side - are not widely used. A Buy Sell standardized manner.” - analyst Investment Strategist indicated that analysts commonly assess management quality but that this is only a subset of governance analysis. ”ESG analysis can act as a safe space for Analysts thought that ESG analysis could help close the gap on long - term term risk factors - analysts to point to long VP of ESG - without changing price targets.” risk assessment. But there was Research controversy on whether these assessments could contribute to price ESG targets or credit ratings. ESG sections typically bear no relation to the recommendation so do not solve the term equation. - long 60

61 PART V CONCLUSIONS & DIRECTIONS FOR FURTHER RESEARCH

62 5.1 CONSEQUENCES Artificial This mispricing of securities can lead volatility . to formation bubbles, but also contribute asset of the Short - term bias in corporate investment decisions . regular to the 'artificially basis inflated' more a on The most obvious of this report’s findings consequence of stock markets : certain volatility that can be risks to managers corporate by faced pressure the to relates years, for ignored are principle in priced and anticipated term short focus on - of expense the at creation value are priced suddenly - re be can then and they when on long place Analysts . creation value term - pressure captured eventually financial beams low the by of on through their emphasis managers short - term analysts . term short is consequence direct The . performance capital bias in corporate expenditure decisions and risk The term of long - investment strategies . Lack other As management . on Capital Focusing a by evidenced coin the term investment of side is the lack of long - the make executives corporate survey, term - Long . have with long - term strategy liabilities, who Investors over strategic plans they than horizon time shorter a in and buy term - long adopting interest an hold . leads would mismatch This ideally (see ) 66 . Fig employ to encouraged are strategies, over portfolios their turn - non facing sectors in misallocation capital to linear of frequently and align their horizon with the horizon those transition to the long - term risks, such as related of a markets financial financial analysis . This deprives . to a low carbon economy stability . potential factor of Our analysis securities that . of Mispricing 70 suggests for system monitoring of Lack - but Last . risks term long calculated by 80 % of the NPV of listed companies as to not least, the short - term focus of financial analysis on based is analysts very involving extrapolations and leaves governments financial authorities largely term, - long non analysis limited cyclical , - linear - non of unequipped to These . risks linear - non term, - long assess Since a recommendations and analysis their risks . play authorities macroeconomic analysis to inform perform stock is key role in the situation this prices, formation of and public stability financial manage . policymaking . stocks of mispricing a to lead to likely precisely, More extent rely they the on However, large very a to - meant to decline due - non term businesses long to (including infrastructure of private financial analysis overpriced risks This linear are likely to be . deliver rating credit to agencies) and research equity phenomenon by the rise of passive reinforced is microeconomic of the analysis . This lack term - long risk which investors indexing', investment and 'closet in financial analysis has recently highlighted as it been replicate composition the closely of cap - very track or relates by climate - related to risks the of Governor the indexes stock a enjoy that weighted . Businesses and Carney, Mark England, of Bank to FSB the led has past significant market capitalization due to and success create related Climate on Force Task sector - private the but short of light in overpriced are prospects term - good long Financial (TCFD) . - However, the gap in Disclosures long - term risks automatically benefit from a strong term . a be might broader analysis financial issue base of 'blind' owners of their shares . : Share of Passive Management in Global 67 Fig Fig. 66: Strategic Planning Time Horizons of Assets under Management Corporate Management Teams Assets are increasingly managed passively CEOs focus on the next 4 years at most Active Alternative Passive Time Horizon Currently Used Ideal Time Horizon $ of AUM % of CEOs 100% 40% 75% 30% 50% 20% 10% 25% 0% 0% <1 1 to 2 3 to 4 5 to 6 7+ 2020 2012 term 2016 - Source: Focusing Capital on the Long Source: PwC 2014 62

63 5. 2 DIRECTIONS FOR FUTURE RESEARCH own research, we will develop partnerships Beyond our with academic researchers to further explore certain this used as will This . be report an of Extension report notably report, this case studies and aspects the of in input workshops involving process consultation a quantitative . survey with equity research, research, credit rating ESG quantitative The report a online survey and agencies . Exploring of demand side . A Tragedy the Horizon the several of research the through extended be will key Winding and Long “The entitled report, How : Road research financial with partnership in topics the Their Over Turn Managers Equity only - Long Portfolios industry : 1 has Years,” 7 . written Every partnership been with in the . The aim of this investment consultancy Mercer Mapping : • Long - term Analyst Best Practice Guide report to further study the practices of equity is the further practices and avenues for best related the and turnover portfolio regarding managers improvement related to long - term financial analysis of terms in implications . analysis term long for need as with associated cost additional the as well the The turn investors term - long even that finds report extension of time the of analysis . This horizon Best . no over their portfolios frequently Thus, there may be be will concert Guide Practice with produced in . demand for long - term analysis groups analyst . industry - corporate for implications the Exploring decision the Corporate Disclosure Report : Identifying • of Tragedy Another . making will report Horizon the requirements corporate and data to related decision deal with the - making process within with analysts sufficient provide would that disclosure - companies will to long exposed term risks . The research financial models long for inputs - term . focus focus risk frameworks, management risk of the on . processes making - decision investment and disclosure Adaptive Report : Examining the drivers of • Capacity influence the explore specifically more will report The analysts capacity and developing tools for adaptive of managers’ top of shortening the in analysis financial basis systematic more a on topic this assess to . . horizons Fig. Analysis - : Mapping Potential Solutions For Longer Term Risk Assessment in Financial 68 New research methods can extend the time horizon of analysis Lengthening the Time Horizon of orizons : 3 - 5 years T H Financial Analyst ime company disclosure/corporate decision making? Lack of LT Lack of demand • Disclosure Guidelines? information from from Investors • Guidelines for scenario analysis? Companies long Aligning - term investors’ demand for Benefit Issues Cost - Lack of Framework research with their horizon? - • Tax and regulatory incentives for more long term investment strategies? term investors to commission - Pooling long • New approaches to Long - Term Financial Analysis? long term research? - • Development of scenario analysis? Communication of alternative NPVs and credit ratings in sectors facing high uncertainty • Extension of cash flow forecast? Use of real asset level data in sectors with high locked - in effect like aircraft manufacturing, etc. power generation, More sophisticated assessment of adaptive • capacity? Source: Authors 63

64 proof concept POTENTIAL MAPPING 3 . 5 Climate risk assessment as a SOLUTIONS of . The topic of climate - related risk management is currently mapping - Pre this While . therapy acupuncture for gaining traction among investors, financial regulators the and focuses on the work of financial financial service providers in the wake of Paris paper analysts Agreement the launch of the Financial Stability and broader the of purpose the specifically, research Disclosures Related - Climate on Force Task Board’s the is project connect to of practices the between dots . the different we see the developments on this forward, Moving players across the investment chain to (new methodological frameworks, data and topic identify ‘acupuncture points : ’ simple actions in frameworks, disclosure policies, offers, rating etc . ) as different parts of the chain that can trigger bigger programs are changes implemented in a coordinated way . pilot if : the solutions that will be developed on they At this stage of the research, the following ‘potential the climate long other to applied be - topic are likely to term those in Part I . should be interpreted as as such material risks identified for further solutions’ then debate recommendations . rather Figure 69: - term Risk Assessment in Securities Research Potential Solutions for Long Authors Source: Target Solution Mechanism Ongoing Developments, Problem Addressed Potential Research Needs Solution Analysts Long - Stanley ESG is writing ESG Qualitative risk sections Morgan can write risk term sections for 1,000 reports in 2016. sections with longer Outlook address risks to the - Section in price target, which time horizons than term Centralized research on long their price targets Research risks beyond ESG might be needed to carries a constrained expand this practice. Reports embedded time horizon term explicit - Equity analysts’ certain sectors with high inertia For Longer - term Long Targets cash flow analysis using recommendations level data can - (e.g. utilities) real asset Price term economic (5+ years) focus on price term price targets - be mobilized. Long long - - are set on the buy movements in the next roadmaps and longer side but further - research is needed to determine the - 12 term discussions with 18 months, thus not providing assessments management extent of this practice. of long - term performance Some analysts currently construct Equity analysts’ price distributions Probability Sensitivity recommendations are analysis cases for stocks in of long - term risk Bull, and Bear Base, Equity Research their coverage. On an experimental scenarios would inform based on usually based on a basis, Bloomberg introduced an alternative markets about the single scenario scenarios interactive DCF with different climate sensitivity of price scenarios. Scenarios with multiple targets and recommendations to variables could produce a probability scenarios that may distribution through a Monte Carlo increase in importance model. Such statistical methods over the long remain limited in equity research and - term credit ratings and deserve further research. Moody’s examination of credit Alternative Ratings that are based ratings reflect Credit the most likely implications in various carbon Ratings Based on various long - term transition scenarios yields insights on developments over the on Multiple scenarios could inform next 3 Term - 5 years but do - scenario specific credit implications on Long investors about the Scenarios sensitivity of their not inform about a sector level; currently these investments to diverse scenarios are not translated into potential credit risk Ratings term risk alternative rating scenarios for long - developments over the sector level only). longer trajectories - companies ( sub - term Credit Such alternative ratings might be useful in the context of ' climate stress currently being developed by tests ' . (France, UK, NL, etc.) central banks 64

65 Potential Ongoing Developments, Solution Mechanism Problem Addressed Research Needs Solution Guidelines Company reports (e.g. such as the TCFD Initiatives Reporting Standards lack a useful - or SASB will likely foster for (voluntary or mandatory) that 10Ks) Long Term long - term disclosure, but reporting on long term risks and - - term focus on long risks and strategies, thus Corporate solutions focus on strategies could enhance the time horizon of company making it difficult for Disclosure climate/sustainability related analysts to assess disclosure and enhance the risks rather than long - term - usability of long term metrics - companies’ long risks in general , and are term to date. mostly voluntary by analysts. prospects. The TCFD currently explores Practice Companies are - Best Guidelines or Guidelines Handbooks could help increasingly employing the use of scenarios by for scenario analyses to Company companies to conduct - companies and will likely assess long - Level term risk scenario analyses that are not make suggestions for Disclosure from issuers Scenario improvement; further exposure only useful for their own , but scenarios - term decision making long of limited are Analysis research is needed to but are also usable by expand the scope beyond informational value for analysts climate related risks financial analysts Fee In Europe in 2018, MiFID 2 Sell - side research reports fee for Charging an explicit are currently free of research reports could shift Structure likely introduce a fee will structure for research his incentivizes T charge. analysts’ attention to clients For Equity - side analysts to focus with a lower trading volume reports. More research is Research sell - time horizons, needed to explore the pros on clients that trade Reports and longer frequently and create and cons of various business a niche market for . term risk assessment - long models for financial analysts pays model, Investor - pay The emerging demand from Both S&P and Moody’s are - issuer In an or regulators and a niche of there is no incentive for currently exploring the investors the issuer to ask for might create an government development of new emerging demand for such change risk related - pay model ratings based on climate for credit alternative ratings products which may be alternative adverse an based on scenarios investor - pays rating Business Model of financial analysis model. can offer long term Stock Exchange Long - term value is not - Companies - - Long Long term term shareholders a higher adequately represented Dividends (LTSE) is exploring incentives dividend than short - term by returns term shareholders for long - 66 shareholders. The large number and Pooling A potential solution could be Long - term asset owners Budget increasingly express their for heavy weight of investors to pool budgets to finance term liabilities concerns regarding the Long Term with long - the development of research o t - create explicit Research capacity on selected long does n mispricing of climate change risks by financial markets, but term risks to inform buy - side term demand for long - external analysis, due to research, in line with what they do not pay for proper financial analysis on the topic happened with ESG research the short term focus of most portfolio strategies about 10 - 15 years ago. or other long term risks. ax This issue has not been Even ‘long - term’ countries, t In several Tax investors frequently incentives schemes attempt to explicitly studied comparatively across Boost demand from investors - turnover their portfolios for long incentivize long and the term - countries. 2 ° ii investing including lower 3 every 1 - years creating term French government plan to term a lack of demand for how the investing - release a report on capital gains tax for long - strategies tax scheme shareholders. long French term risk term influences long - assessment investment in 2017. 65

66 4 5 . CONCLUSIONS Fig. 70: Share of Net Present Value beyond 5 years in Morningstar DCF Models by Sector materializing ignores Securities research largely risks Long term cash flows constitute the majority of NPV - beyond 3 - 5 years . Valuation models used in equity than research typically have a ' time horizon ' of no more Share of NPV the on focus recommendations and 12 next 3 - 5 years 100% months . Credit risk assessments can extend beyond that follow horizon, but ratings actions are unlikely to 80% materialize 3 from risks likely to - beyond - 5 years . Non the long - term risks (White Swans in linear, Dark) 60% materializing outside of this focus are, therefore, valuations in and equity unlikely to be reflected current 40% long development The . ratings credit of analysis term - of seems technically feasible but faces a number 20% . methodological, data - related, and commercial hurdles 0% financial of from disconnected is analysis The focus investors' the horizon associated with liabilities . A is by of owned assets large share management under Energy Utilities term exceeding horizon average long - an investors with Industrials Healthcare Real Estate Technology suggest financial current findings that 10 - 15 years . Our Basic Materials designed not analysis for stocks is corporate bonds and Financial Services Consumer Cyclical such to help these investors optimize their returns over Consumer Defensive portfolio term horizons without high - turnover . long from Morningstar DCF Models 2016 Authors Source: analysis is disconnected from of focus financial The 71: Fig. Time Horizon Gap for Credit Analysts the . In most the 'window of materiality' of securities based on Maturity of Debt % the present value of a of 70 than more sectors net Credit analysts should align their time horizon with term cash flows, reflecting company is based on long - the maturity of bonds the and nature term - long the of underlying physical most Similarly, . ) 70 (Figure assets intangible for Average Debt Maturities (2015) derives flows cash from corporate value bonds, the Analyst Time Horizons (S&P) 5 ) . These (Figure findings suggest a years beyond 71 and analysts’ the between disconnect substantial focus Utilities to value creation and management’s focus with regard management . risk Technology want not do - fund Most term long more managers Materials Analysts’ 3 - 5 year focus is research, however . consistent with the holding period of most investors . Industrials for and The average bond holding periods equity and respectively are years, 5 . 1 (see 7 . 1 only investors Health Care . Even page longest - term compensation for buy - 60 ) the extends only to 5 years . side equity research analysts is there that suggest therefore findings preliminary Our Financials risk any . long - term incentive little for assessment, if Energy these Thus, analysts’ ability to fix disconnects is asked better analysts if Even questions to constrained . Consumer Staples company management create to tools developed and Consumer forecasts, they would not necessarily long - term Discretionary would become more successful immediately . Analysts need superior from companies and increased data Communications client demand for long - term analysis . Further research assess will be can factors these how be to needed 15 20 5 0 10 . aligned with investor interests Years Source: Authors from Bloomberg 2015 66

67 ENDNOTES PART WHITE SWANS MAY LOOK BLACK IN THE DARK I : 1 Jorida Dispersion Anomaly and Analyst Recommendations, SSRN, 2012 . , Papakroni, The 2 Herding . Harrison “Security Analysts’ Career Concerns and al of Earnings Forecasts,” RAND Journal of Hong, et , . 31 No . 1 , Economics 121 - 144 , 2000 . Vol Spring, 3 Services , “A Comprehensive History of the Performance of Moody’s Corporate Credit Moody’s Investor Credit ; Ratings Services , “Guide to S&P Ratings Essentials,” 2015 Ratings,” 2015 4 Investor Services, “A Comprehensive History of the Performance of Moody’s Corporate Credit Ratings,” Moody’s . 2015 5 Investor Comprehensive History of the Performance “A Moody’s Corporate Credit Ratings,” Moody’s Services, of S&P 2015 Services, “ 2015 Annual Global Corporate Default Study and Rating Transitions,” 2015 . ; Ratings 6 Investor Services . “Corporate Default and Recovery Rates, 1920 - 2015 ,” 2015 ; S&P Ratings Services Moody’s Rating 2015 Corporate Default Study and Global Transitions,” 2015 . Annual “ 7 Investor Services . “Analyzing the Tradeoff Between Accuracy and Stability : Special Comment,” 2007 Moody’s 8 Moody’s Services . Glossary of Investor Ratings Performance Moody’s 2011 . Metrics, Bibliography some In measures do not capture the full volatility and magnitude of ratings changes . Common cases stability on such and transition rates are calculated based upgrade/downgrade the net change in measures stability as a given time period . In these cases intra - period ratings changes are excluded, potentially ratings over volatility . Further, when ratings are both upgraded and downgraded within a time period underestimating actual individual net the recorded, which obscures the magnitude of the impact movements . 2 ° ii research based only is S&P ratings changes indicates that stability metrics calculated in this way do somewhat understate intra - on volatility period magnitude of changes . and the 9 Investor “Analyzing the Tradeoff Between Accuracy and Stability : Special Comment,” Moody’s ; 2007 Services, Moody’s Investor Comprehensive History of the Performance of “A Corporate Credit Ratings,” Moody’s Services, . 2015 10 NN . The Black Swan : The Impact of the Highly Improbable . New York : Random House . 2007 . Taleb, 11 events “Are we seeing the emergence of more White Swan GW, ?” Logical Management Sikich, Corp . Systems, 2010 . 12 Thamotheram, Raj . Preventable Surprises . 2016 13 CERES, ° Investing Initiative, Energy Transition Advisors and Carbon Tracker Initiative . “Carbon Asset Risk : from 2 to action ” 2015 ; WRI and United Nations Environment Programme Finance Initiative . “Carbon Asset rhetoric . Discussion Financial . ” 2015 ; Task Force on Climate - Related : Disclosures : “ Phase 1 Report of the Framework Risk PRI/UNEP on - Related Financial Disclosures . ” 2016 Climate , “Greening Institutional Investment, 2015 . Force Task 14 Jason, “Is This Time Different? The Opportunities and Challenges of Artificial Intelligence,” 2016 . Furman, 15 Analysis “Global Economic Impacts Associated with Artificial Intelligence,” 2016 . Group, 16 Analysis Economic Impacts Associated with Artificial Intelligence,” 2016 . Group, “Global 17 for “Disruptive Mobility : A Scenario Barclays 2040 ,” 2015 . Research Insights, 18 Presentation in ’ 06 Showed How to Foil Emissions Tests, New “VW Times, 2016 . York 19 Deception,” Says Cars Worldwide Are Affected in Diesel Million New York Times, 2015 . “Volkswagen 11 20 Pattern,” Test Follows a Long Auto Industry Rigging New York Times, 2015 . “Volkswagen 21 “White House to Toughen Fuel Standards for Heavy - Duty Vehicles,” New York Times, 2014 ; US Sets Higher Fuel Efficiency Standards,” York Times, 2012 ; Heavy Trucks to be Subject to New Rules for Mileage ; New York New Standards,” 2011 Obama Mandates Rules to Raise Fuel “ New York Times, 2010 . Times, ; 22 Test Rigging Follows a Long Auto Industry Pattern, New York Times, 2015 Volkswagen . 23 Road, Council Transportation, From Laboratory to Clean 2013 . International on 24 Council International Clean Transportation, From Laboratory to Road 2014 Update, 2014 . on 25 European Federation for Transport & Environment , “ Realistic real - world driving emissions tests : the last chance for diesel ?,” 2015 . cars 26 Peabody 10 - K, 2011 . Energy 27 US Natural Gas Gross Withdrawals . EIA, 28 Carbon Tracker Initiative, “US Coal Crash : Evidence for Structural Change, 2015 ,” pages 26 - 27 . 29 US Natural Gas Gross Withdrawals . EIA, 30 Carbon Tracker Initiative, “US Coal Crash : Evidence for Structural Change, 2015 ,” pages 26 - 27 . 31 . Resources for the Future , Review of Shale Gas Development in the United States, 2013 , pages 14 - 16 67

68 32 Carbon “US Coal Crash : Evidence for Structural Change,” 2015 , pages 26 - 27 . Tracker Initiative, 33 Direct Credit Ratings . : Morningstar Corporate 34 B Nocera , J, All the Devils Are Here : The Hidden History of the Financial Crisis . , . 2010 McLean ; 35 Bubble,” Use Real Estate Trends to Predict the Next Housing to Harvard Division of Continuing “How Nicolais, T, . 2014 Education, 36 Service, “Testimony of Raymond W . McDaniel Chairman and Chief Executive Officer of Moody’s Investor And Moody’s Yoshizawa Senior Managing Director Moody’s Investors Service before the United Corporation Yuri Permanent on Investigation,” 2010 . Senate Subcommittee States 37 “ 10 million self - driving cars will be on the road by 2020 . ” 2016 Business Insider, . 38 “ Self - driving Cars and $ 87 Billion Opportunity Research, 2013 , Though None Reach Full Autonomy,” 2014 . Lux in 39 Boston Company, “Automobiles and the Age of Autonomy,” 2015 ; ELP, “Self - driving Cars : Disruptive vs . The 2014 Incremental,” . 40 of Autonomous and Driverless Cars, 2016 . Mechanical Institute Engineers, 41 Autonomy, and the Age of Boston 2015 . Company, The Automobiles 42 et al . , “ Potential Impact of Schoettle, - Driving Vehicles on Household Vehicle Demand and Usage,” 2015 . B Self 43 Price - Anderson Act : The Billion Dollar Citizen, for Nuclear Power Operators, 2004 . Public Bailout 44 B et al . “Potential Impact of Self - Driving Vehicles on Household Vehicle Demand and Usage,” 2015 . Schoettle, 45 Public - Anderson Act : The Billion Dollar Bailout for Nuclear Power Operators,” 2004 “Price Citizen, . 46 World Index as of MSCI 28 , 2016 . SlickCharts, Feb II : THE RATIONALE FOR LONG - TERM ANALYSIS : EXPOSING THE WINDOW OF MATERIALITY PART 47 from , “Annual Study of Intangible Asset Market Value Tomo Ocean Tomo , LLC,” 2015 . Ocean PART III: HOW FINANCIAL ANALYSTS EQUIP INVESTORS WITH LOW BEAMS 48 Valuing Aswath , The Dark Side of Valuation : and Young, Distressed Damodaran, Complex Businesses, Upper Saddle River, : FT Press, 2009 . NJ 49 Maubossin, Common Errors in DCF Models," Maubossin on Strategy, 2006 . Michael, “ 50 Distressed Dark Side of Valuation : Valuing Young, The and Complex Businesses, 2009 . Damodaran, Aswath , 51 Ratings, Of Time Dimension Global Standard & Poor’s Credit Rating,” 2010 . S&P “The 52 Ratings the Ratings : What Credit “Inside Mean,” 2007 . Fitch Ratings, 53 Global Ratings, “ESG Risks In Corporate Credit Ratings – An Overview,” 2015 . S&P 54 Moody’s, Approach to Assessing the Credit Impacts of Environmental Risks,” 2015 . “Moody’s 55 S&P Global Opinion: How Asset Level Data Can Improve The Assessment Of Environmental Risk Ratings, “Guest In Credit Analysis,” 2016. PART IV: THE DRIVERS BEHIND THE LOW BEAMS 56 et al . , “ Inside the Black Box of Sell - side Financial Analysts,” Journal of Accounting Research , 2015 . Brown 57 Brochet, ; Loumioti , M ; Serafeim , G, “Speaking of the short F term : Disclosure Horizon and Capital Market - Outputs . ,” 2014 . 58 Kepler Chevreux , “ The Responsible Investor Playbook,” 2016 . Not published . 59 S&P Ratings, “Proposal For Environmental, Social, And Governance (ESG) Assessments,” 2016 . Global 60 risks Sustainability into Valuation : Global Framework for Analyzing ESG “Embedding Morgan Stanley, Opportunities,” 2016 . Not public . and 61 , Phase I Report of the TCFD Force on Climate Related Financial Disclosures, 2016 . Task 62 Not Chevreux “ The Responsible Investor Playbook,” 2016 Kepler , Published ; Posner, Kenneth, Stalking the Black . Swan : Research and Decision Making in a World of Extreme Volatility, 2010 . 63 Financial “Equity research undergoes big structural changes,” 2014 . Times, 64 “Stock Martijn Cremers Sautner, and Ankur Pareek , Zacharias, Duration and Misvaluation . ” Presented at the , Society for Financial Studies Cavalcade and Geneva Summit on Sustainable Finance, 2013 . 65 McKinsey CPPIB, “ Short - termism : and from Business Leaders,” 2013 . Insights PART V: CONCLUSIONS AND DIRECTIONS FOR FURTHER RESEARCH 66 the Foundation, Mercer, and Stikeman Elliott, “ Building a Long - Term Shareholder Base : Assessing Generation . Potential of Loyalty - Driven Securities,” 2013 68

69 GENERAL INDUSTRY FEEDBACK key findings this paper received support from across the equity research and credit rating The of and The 150 + equity research firm included credit rating agency representatives, . industries workshops analysts and managers from 30 + institutions (see page 4 ) . including both . the findings of Research equity research portion were largely validated by the industry ; Equity The term managers and the focus of analysis is short - agreed (typically limited to 3 - 5 years) . analysts that the proposed solutions to this However, were somewhat more contentious . For instance, the problem technical and the associated cost - benefit of pursuing methodological improvements to feasibility are valuation to be seen . Moreover, the models remains only part of the story ; herd existing techniques and confirmation biases among analysts can have a bigger impact on the embedded mentality and timeframe of price targets than the models themselves . Further, some industry participants value while the on analysts to develop new solutions, placed others cited the lack of client demand burden from investors themselves as a major obstacle to undertaking longer - term analysis . Credit Rating On the credit rating side, our findings were more contentious . Some credit rating agency . stressing acknowledged - 5 year focus of the analysis, 3 that ratings are intended to be managers the and will change before long - term risks materialize ‘dynamic’ On the contrary, others disputed our . framing a short - term time horizon altogether, arguing of they do have a long - term focus and that incorporate long - term risks as far as it is allowed by the availability of data from issuers and the general uncertainty that on the long term . Thus, our paper has not gained universal validation in the increase ratings . credit industry from respondents not to be quoted . Some quotes chose those who accepted are presented below : The “For years, the industry has been peeling back the layers of the long - term investing onion. This report makes a significant contribution in interrogating time horizon considerations in sell - side financial analysis, and presents practitioners with some noteworthy challenges and opportunities. Contemplating and pricing risk in new ways is critical, given the growing and changing nature of global risks .” Jane Ambachtsheer , Partner, Mercer Investments - “Investors and financial industry leaders are increasingly recognizing the need to adopt to a longer term investment focus. This much needed report helps provide the missing tools and incentives to get there .” Stephen Freedman, Head of Thematic & Sustainable Investment Strategy, UBS “S&P Global Ratings shares common vision to enhance the systematic and transparent considerations of long - term risk factors, such as ESG, in the assessment of creditworthiness. In this regard, we welcome the opportunity to collaborate with the 2 Degrees Investing Initiative among others to help better risks” identify and understand such Mike Wilkins, Managing Director, Environmental & Climate Risk Research, S&P Global Ratings “This is an impressive report that sheds light on how endemic short - termism in financial analysis can result in stranded assets. Significant value will be lost and opportunities missed unless these biases are addressed proactively by financial institutions. Doing so will also help to make finance better aligned with global environmental sustainability.” Ben Caldecott, Director of the Sustainable Finance Program at the University of Oxford CONTINUED OVERLEAF 69

70 GENERAL INDUSTRY FEEDBACK (CONT.) “Every risk manager knows that ignoring a risk doesn’t make it disappear. That’s why it’s so important to - term risks so investors are more aware find the right assessment tools to identify and characterize long and accountable for their investment decisions. The findings of this report enlighten everyone about the term risk assessment, and we look forward to building on the recommendations to - current lack of long - help secure a more sustainable allocation of capital for a decarbonized future” Romain Poivet , Climate Program Officer at the French Environmental & Energy Agency “This report reveals why most investors miss obvious technological innovation and societal signals that lead to trillions in losses. Investors who ignore the recommendations from this report put both the planet and their profits at risk !” Ian Monroe, President at Etho Capital ° Investing Initiative & The Generation Foundation have “With ‘All Swans Are Black in the Dark’, the 2 - term investors can better manage their made an important contribution to understanding how long exposure to long - term risks and balance these with the short - term risks their portfolios face every day. In particular, their suggestion that incorporating scenario analyses into the investment process, promises to - making poses to help address the difficult challenge of managing the risks investors’ cumulative decision – the broad systems – upon which their investments depend.” environmental, societal, and financial Steve Lydenberg , Partner at Domini Social Investments “Through sophisticated graphics and careful research, this report illuminates challenges and solutions to avoiding the Tragedy of the Horizon. It’s consumable by leaders of a variety of backgrounds and thus is - poised to influence better informed financial analysis that results in forward looking projects for our stronger future .” Joyce Coffee, President of Climate Resilience Consulting “By understanding the key characteristics or risks that are incorrectly or only partially priced by the market, research analysts are able to focus on a smaller set of themes which are likely to be financially relevant. This in turn supports the development of targeted tools to complement financial modelling, such as scenario analysis or intangible asset valuations. This report and the Tragedy of the Horizons project makes a valuable contribution in this respect .” Julie Raynaud, Senior Research Analyst at Kepler Cheuvreux “With this report, the 2 ° Investing Initiative and The Generation Foundation perfectly sum up – in a very clear, comprehensive way the issues faced by investors for long - run analysis and risk - assessment. As – such, this report makes an important contribution to the debate on long - term risk - assessment, providing helpful solutions to position ourselves against not only the threats of climate change, but other sustainability and financial risks and opportunities of the future.” Investment Valery Lucas - Leclin , President & Founder of Grizzly Responsible 70

71

72 2dii and The Generation Foundation welcome comment and discussion on www.tragedyofthehorizon.com this study. For more information please visit CONTACT: Mona Naqvi, Program Manager investing.org - [email protected] - investing.org www.2degrees 205 Grand Central ) E 42nd Street, 10017 NY ( Ⓜ

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