Measuring ESG Effects in Systematic Investing E-Book

Arik Ben Dor

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Finance Series
• Sprache: Englisch

Seitenzahl: 651

Veröffentlichungsjahr: 2024

Beschreibung

A unique perspective on the implications of incorporating ESG considerations in systematic investing In Integrating ESG in Systematic Investing, a team of authors from Barclays' top-ranked Quantitative Portfolio Strategy group (ranked #1 by Institutional Investor in its 2022 Global Fixed Income Research Survey in both the US and Europe) delivers an insightful and practical discussion of how to reflect ESG considerations in systematic investing. The authors offer a cross-asset class perspective--incorporating both credit and equity markets in the United States, Europe, and China--a unique coverage scope amongst books on this subject. They discuss the interaction between ESG ratings and various other security characteristics, suggest a methodology for isolating the ESG-specific risk premia, analyse the impact of an ESG tilt on systematic strategies and risk factors, and identify several ESG-based signals that are predictive of future performance. You'll also discover: * Analysis of companies in the process of improving their ESG ranking ("ESG improvers") vs. firms with best-in-class ESG ratings * A study using natural language processing (NLP) to predict changes in corporate ESG rankings from company job postings for sustainability-related positions * In-depth explorations of ESG equity fund performance and flows and the information content of ESG ratings dispersion across several providers Perfect for portfolio managers including non-quantitative, fundamental investors, risk managers, and research analysts at financial institutions such as asset managers, pension funds, banks, sovereign wealth funds, hedge funds, and insurance companies, Integrating ESG in Systematic Investing is also a must-read resource for academics with a research interest in the performance and risk implications of ESG investing.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Copyright

Dedication

Foreword

NOTE

Preface

NOTE

Acknowledgements

Introduction

NOTE:

PART One: Effect of ESG Constraints on Portfolio Performance and Valuation

CHAPTER 1: How Do ESG Criteria Relate to Other Portfolio Attributes?

HOW ARE ESG RATINGS FORMED?

PROPERTIES OF ESG SCORES

THE EFFECT OF ESG ON BOND VALUATION

CONCLUSION

NOTES

CHAPTER 2: Measuring the ESG Risk Premium: Credit Markets

COMPARISON TO AN ESG-CONSTRAINED INDEX: A BIASED APPROACH

BUILDING ESG-TILTED EXPOSURE-MATCHED PORTFOLIOS

MEASURING ESG RETURNS IN CREDIT

CONCLUSION

REFERENCES

NOTES

CHAPTER 3: Measuring the ESG Risk Premium: Equity Markets

THE DRAWBACKS OF DERIVING ESG RETURNS USING INDICES

METHODOLOGY: MEASURING ESG RETURN PREMIUM USING EXPOSURE-MATCHED PORTFOLIOS

DYNAMICS OF ESG RETURN PREMIUM

ESG RETURN PREMIUM BY SECTOR, PILLAR, AND IN OTHER GEOGRAPHIES

DISCUSSION: AN ALTERNATIVE APPROACH

CONCLUSIONS

REFERENCES

NOTES

CHAPTER 4: Performance Impact of an ESG Tilt in Sovereign Bond Markets

DATA SOURCES

ARE ESG CHARACTERISTICS PRICED INTO SPREADS?

EXAMINING THE RELATIONSHIPS AMONG THE DATA SETS

MEASURING ESG EFFECTS ON RETURNS

ESG SCORES AND CREDIT RATINGS

DISCUSSION

CONCLUSION

REFERENCES

APPENDIX: SUMMARY OF DATA SOURCES

NOTES

CHAPTER 5: Effect of SRI-Motivated Exclusion on Performance of Credit Portfolios

INTRODUCTION

EFFECT OF SRI EXCLUSIONS ON INDEX ALLOCATION

MEASURING ISSUER-SPECIFIC RETURNS OF SRI EXCLUSIONS

MEASURING THE EFFECT OF SRI EXCLUSIONS AT THE INDEX LEVEL: TWO APPROACHES

CONCLUSION

NOTES

PART Two: Systematic Strategies and Factors Subject to ESG Constraints

CHAPTER 6: Effect of ESG Constraints on Credit Active Returns

IMPOSING ESG CONSTRAINTS ON CORPORATE BOND INDICES

NUMERICAL SIMULATIONS: WHAT TO EXPECT?

SYSTEMATIC STRATEGIES IN CREDIT

EFFECT OF ESG CONSTRAINTS

EFFECT OF SRI EXCLUSIONS

IMPLEMENTING SYSTEMATIC CREDIT PORTFOLIOS WITH ESG CONSTRAINTS

CONCLUSION

REFERENCES

NOTES

CHAPTER 7: Incorporating ESG Considerations in Equity Factor Construction

SAMPLE AND ESG-AGNOSTIC FACTOR PORTFOLIO CONSTRUCTION

INCORPORATING ESG CONSIDERATIONS: EXCLUSIONARY POLICIES

INCORPORATING ESG CONSIDERATIONS: PORTFOLIO OPTIMIZATION

DECOMPOSING THE RETURN DIFFERENCE BETWEEN ESG-TILTED AND ESG-AGNOSTIC FACTOR PORTFOLIOS

CONSISTENT RESULTS ACROSS MARKETS

CONCLUSION

APPENDIX: EFFECT OF NEGATIVE SCREENING ON ESG-TILT AND FACTOR STYLE IN EUROPE AND JAPAN

REFERENCES

NOTES

PART Three: Performance Implications of Companies' ESG Policies

CHAPTER 8: ESG Rating Improvement and Subsequent Portfolio Performance

DATA: UNDERSTANDING ESG SCORE CHANGES

METHODOLOGY: EXPOSURE-MATCHED PORTFOLIOS

EFFECTS OF ESG MOMENTUM ON PERFORMANCE

COMPARING ESG MOMENTUM AND ESG LEVEL EFFECT

CONSISTENCY OF ESG MOMENTUM EFFECTS

ALTERNATIVE SPECIFICATIONS FOR PORTFOLIO CONSTRUCTION

CONCLUSION

APPENDIX: COVERAGE OF MSCI ESG SCORES

REFERENCES

NOTES

CHAPTER 9: Predicting Companies' ESG Rating Changes Using Job-posting Data

DATA AND METHODOLOGY

MEASURING ESG HIRING INTEREST

ESG HIRING AND SUBSEQUENT ESG RATING CHANGES

ESG HIRING AND SUBSEQUENT PERFORMANCE

CONCLUSION

APPENDIX: A SAMPLE JOB POST

REFERENCES

NOTES

CHAPTER 10: The Relationship Between Corporate Governance and Profitability

OVERVIEW OF MSCI GOVERNANCE SCORE DATA

MEASURING THE GOVERNANCE–PROFITABILITY RELATIONSHIP USING THE EXPOSURE-MATCHED APPROACH

MEASURING GOVERNANCE–PROFITABILITY RELATIONSHIPS USING REGRESSIONS

CONCLUSION

NOTES

PART Four: The Lack of Uniformity in ESG Definitions—Investment Implications

CHAPTER 11: ESG Equity Funds: Looking Beyond the Label

DATA

COMPARING ESG AND NON-ESG FUNDS

RISK-ADJUSTED RETURNS AND FACTOR LOADINGS OF ESG AND NON-ESG FUNDS

ECONOMIC CONSEQUENCES OF LABELLING A FUND AS ESG

CHALLENGES OF ESG CLASSIFICATION

CONCLUSIONS

REFERENCES

NOTES

CHAPTER 12: Combining Scores from Multiple ESG Ratings Providers

THE CHALLENGE OF ESG SCORING

BENEFIT OF CONSTRUCTING A CONSENSUS ESG SCORE

SOLVING ONE PROBLEM, CREATES SEVERAL MORE

APPENDIX: THE METHODOLOGY

REFERENCE

NOTES

CHAPTER 13: The Informational Content of Dispersion in Firms' ESG Ratings across Providers

UNDERSTANDING DISPERSION OF ESG SCORES

ESG DISPERSION STABILITY AND RELATION WITH FUTURE ESG SCORE CHANGES

ESG DISPERSION AND FUTURE STOCK PERFORMANCE

ALTERNATIVE PORTFOLIO SPECIFICATIONS

CONCLUSIONS

REFERENCES

NOTES

Index

End User License Agreement

List of Tables

Chapter 1

TABLE 1.1 Sample issues considered in forming ESG scores.

TABLE 1.2 Average characteristics of bonds in different tiers of ESG scores ...

TABLE 1.3 Transition frequencies across MSCI ESG tiers on a one-year horizon...

TABLE 1.4 Example spread attribution to ESG attributes in the US IG market a...

TABLE 1.5 Average spread premium, by ESG pillar, in US IG and Euro IG market...

Chapter 2

TABLE 2.1 US Corp. Sustainability vs US Corp. indices, 31 December 2020.

TABLE 2.2 Euro Corp. Sustainability vs Euro Corp. indices, 31 December 2020....

TABLE 2.3 Building max and min ESG exposure-matched portfolios by sector....

TABLE 2.4 Characteristics of max and min ESG exposure-matched portfolios in ...

TABLE 2.5 US IG: ESG returns (returns of max over min ESG portfolios) by sec...

TABLE 2.6 Euro IG: ESG returns (returns of max over min ESG portfolios) by s...

TABLE 2.7 ESG return premium for E, S, and G pillar scores, US and EUR IG, 2...

TABLE 2.8 Max ESG portfolios separately for E, S, and G pillar scores, US an...

TABLE 2.9 Min ESG portfolios vs index, separately for E, S, and G pillar sco...

TABLE 2.10 Portfolios of high ESG issuers have outperformed low ESG portfoli...

Chapter 3

TABLE 3.1 Construction details of exposure-matched Max- and Min-ESG stock po...

TABLE 3.2 MSCI ESG score coverage of US equity indices.

TABLE 3.3 Average number of stocks in the Max- and Min-ESG portfolios.

TABLE 3.4 Average MSCI ESG scores (Scale 0–10) of exposure-matched portfolio...

TABLE 3.5 Performance statistics of ESG return premia and ESG returns from t...

TABLE 3.6 Comparison of performance statistics of ESG return premia for diff...

TABLE 3.7 ESG return premium correlations for S&P 500, MSCI USA, Russell 100...

TABLE 3.8 Average number of stocks in Max-ESG or Min-ESG portfolios (January...

TABLE 3.9 Return premium performances of E, S, G pillars and ESG return prem...

TABLE 3.10 Correlation between ESG return premium and individual pillar retu...

TABLE 3.11 Distribution of ESG level and momentum in China, US, and European...

TABLE 3.12 ESG return premia in China A-share, US, and European equity marke...

TABLE 3.13 Correlations among China A-share, US, and European stock markets,...

TABLE 3.14 Ex post regression of ESG return premium (exposure-matched approa...

Chapter 4

TABLE 4.1 Results of single-variable regressions for (log) EM sovereign spre...

TABLE 4.2 Regression coefficients for (log) EM sovereign spreads vs gross de...

TABLE 4.3 Regression coefficients for (log) EM sovereign spreads vs credit r...

TABLE 4.4 Regression results for country-level log spreads, 2013–2020.

TABLE 4.5 Correlation matrix of market quantities, fundamental variables, an...

TABLE 4.6 Detailed correlation matrix of ESG factors, including all six indi...

TABLE 4.7 Correlations among freedom-related scores from different organizat...

TABLE 4.8 Optimization procedure used to form ESG-tilted EM sovereign portfo...

TABLE 4.9 Historical performance of ESG-tilted tracking portfolios, October ...

TABLE 4.10 Dependence of year-over-year ratings transitions on year-over-yea...

TABLE 4.11 Regression model for year-over-year (YoY) changes in credit ratin...

TABLE 4.12 Unintended tilts in long/short portfolios as a fraction of the in...

TABLE 4.A1 List of data sources used in this study, with frequency, sign and...

Chapter 5

TABLE 5.1 Percentage market value excluded by MSCI SRI screens from the Bloo...

TABLE 5.2 Example SRI–compliant peer group composition, 29 April 2022.

TABLE 5.3 Selected SRI exclusions with the strongest and weakest issuer-spec...

TABLE 5.4 SRI excluded issuers with the largest positive or negative trackin...

TABLE 5.5 Bloomberg MSCI US IG SRI Corp. index vs Bloomberg US IG Corp. inde...

TABLE 5.6 Building exposure-matched SRI portfolios by sector.

TABLE 5.7 Tracking errors of exposure-matched SRI portfolios by sector, June...

Chapter 6

TABLE 6.1 Building max and min exposure-matched portfolios for individual sy...

TABLE 6.2 Summary performance statistics of systematic exposure-matched valu...

TABLE 6.3 Effect of progressively excluding issues with the lowest ESG score...

TABLE 6.4 Effect of ESG constraints on systematic active returns of value, m...

TABLE 6.5 Reduction in value, momentum, and sentiment alphas in US IG as a r...

TABLE 6.6 Effect of progressively excluding issuers with the low ESG ratings...

TABLE 6.7 Effect of ESG and SRI constraints on active returns of the systema...

Chapter 7

TABLE 7.1 Average monthly pairwise cross-sectional rank correlations between...

TABLE 7.2 Average number of ESG-agnostic factor portfolio constituents exclu...

TABLE 7.3 Construction details of the portfolio optimization approach.

TABLE 7.4 Annualized average factor returns, and their decompositions across...

Chapter 8

TABLE 8.1 ESG momentum summary statistics (S&P 500 universe, MSCI ESG scores...

TABLE 8.2 Construction details of exposure-matched Max- and Min-ESGMOM portf...

TABLE 8.3 Average number of securities in the Max- and Min-ESGMOM portfolios...

TABLE 8.4 Performance of ESG momentum portfolios with different ESG levels (...

TABLE 8.5 Performance of ESG level vs ESG momentum effects (Max-over-Min por...

TABLE 8.6 Cumulative excess returns of ESG momentum in Q4 2015–Q4 2016 perio...

TABLE 8.7 ESG momentum returns by different over/underweight constraints and...

Chapter 9

TABLE 9.1 Selected data fields in the job posting data from Burning Glass Te...

TABLE 9.2 Sectoral breakdown of Burning Glass versus S&P 500.

TABLE 9.3 Handling messiness, magnitude, mapping, and measurement.

TABLE 9.4 Examples of ESG-related job posts identified by the algorithm.

TABLE 9.5 Average firm characteristics of terciles ranked by abnormal ESG HI...

TABLE 9.6 MSCI ESG rating (scale: 0–10) percentiles for the 2015 cohort.

TABLE 9.7 Difference in rating change percent between firms with top and bot...

TABLE 9.8 Differences in E-rating change percent between firms with top and ...

TABLE 9.9 Differences in S- and G-rating change percent between firms with t...

TABLE 9.10 Portfolio performance of firms sensitive to environmental issues,...

TABLE 9.11 Factor regression estimates, January 2016–December 2020.

TABLE 9.12 Portfolio performance from a double sort between ESG hiring and r...

Chapter 10

TABLE 10.1 Distribution of MSCI composite G-score and theme score.

TABLE 10.2 Construction details of the exposure-matched approach.

TABLE 10.3 Summary statistics of the Governance–Future ROE relation by the e...

TABLE 10.4 Summary statistics of the Governance-Future ROE relation using th...

Chapter 11

TABLE 11.1 US equity funds summary statistics by type of ESG and non-ESG fun...

TABLE 11.2 Percentiles of AUM of ESG and non-ESG US equity funds.

TABLE 11.3 Total AUM by fund type and total market capitalization of S&P 500...

TABLE 11.4 Benchmarks of ESG US equity funds.

TABLE 11.5 Coverage of US equity funds.

TABLE 11.6 Types of ESG funds.

TABLE 11.7 Factor loadings of US equity funds in two subsamples.

TABLE 11.8 Flow–performance relationship.

Chapter 12

TABLE 12.A1 Company GICS/ BICS classifications are mapped to seven sector ca...

Chapter 13

TABLE 13.1 Coverage of ESG data for US stock indices.

TABLE 13.2 Correlations of different ESG score dispersions.

TABLE 13.3 Characteristic differences of Q5 (high)–Q1 (low) sorted on ESG le...

TABLE 13.4 Average coefficients of monthly cross-sectional regressions of ES...

TABLE 13.5 Transition frequencies across ESG level and dispersion terciles o...

TABLE 13.6 ESG dispersion and subsequent ESG score changes.

TABLE 13.7 Construction details of ESG-dispersion-tilted stock portfolios....

TABLE 13.8 Performance of index-replicating portfolio with Max- and Min-ESG ...

TABLE 13.9 Valuation ratios with independent double sorts on ESG levels and ...

TABLE 13.10 Performance of max-over-min-ESG-dispersion portfolio in original...

TABLE 13.11 Performance of max-dispersion over min-dispersion portfolios con...

TABLE 13.12 Performance of max-over-min-ESG-dispersion portfolios in extende...

TABLE 13.13 Construction details of ESG-dispersion-tilted corporate bond por...

TABLE 13.14 Corporate bond excess return differences in max-over-min ESG dis...

List of Illustrations

Chapter 1

FIGURE 1.1 Average difference in credit rating between top and bottom tier o...

FIGURE 1.2 Average correlation between credit rating and MSCI ESG scores (Au...

FIGURE 1.3 Correlations among MSCI E, S, and G scores.

FIGURE 1.4 Historical ESG spread premium in the US IG market(bp per one stan...

FIGURE 1.5 Historical ESG spread premium in the Euro IG market(bp per one st...

FIGURE 1.6 Historical ESG spread premium in the US HY market (bp).

Chapter 2

FIGURE 2.1 Contributions to TEVs of US and Euro Corp. Sustainability indices...

FIGURE 2.2 Contributions to TEV of US max ESG portfolio and the US Corp. Sus...

FIGURE 2.3 Average ESG scores of ESG-tilted portfolios, Sustainability indic...

FIGURE 2.4 Cumulative excess returns of max ESG portfolios and Sustainabilit...

FIGURE 2.5 Cumulative ESG returns (max over min ESG portfolios) in US and Eu...

FIGURE 2.6 Rolling average number of downgrade notches per issuer and per ye...

Chapter 3

FIGURE 3.1 Comparison of average key risk characteristics between MSCI USA E...

FIGURE 3.2 Sector weight difference of MSCI USA ESG leaders index over MSCI ...

FIGURE 3.3 Cumulative return difference of the MSCI ESG index over its paren...

FIGURE 3.4 Cumulative performance of ESG return premium (January 2009–Decemb...

FIGURE 3.5 Scatter plot of Max-ESG over benchmark vs ESG returns from the in...

FIGURE 3.6 Scatter plot of Max-ESG-over-benchmark monthly returns in S&P 500...

FIGURE 3.7 Cumulative performance of ESG return premium for S&P 500, MSCI US...

FIGURE 3.8 Max- and Min-ESG portfolios with alternative weight constraints....

FIGURE 3.9 ESG return premium performances by sector.

FIGURE 3.10 Cumulative return of return premia of E, S, and G pillars and ES...

FIGURE 3.11 ESG return premium in European markets.

FIGURE 3.12 Coverage of ESG data in China A-share market (CSI 800 index, Jan...

FIGURE 3.13 Cumulative returns of ESG premia in China A-share, US, and Europ...

Chapter 4

FIGURE 4.1 (Log) sovereign spreads vs WGI governance scores, October 2020....

FIGURE 4.2 (Log) sovereign spreads vs credit rating, 30 October 2020.

FIGURE 4.3 Cumulative returns of portfolios that maximize or minimize SPI sc...

FIGURE 4.4 SPI Scores of Max-ESG and Min-ESG portfolios relative to SPI scor...

FIGURE 4.5 Gross debt vs average WGI governance score, BBB-rated countries, ...

FIGURE 4.6 Gross debt vs average WGI governance score, BB-rated countries, O...

Chapter 5

FIGURE 5.1 An example tobacco company (Issuer A) vs SRI issuers in the non-c...

FIGURE 5.2 Cumulative excess return (%) of Issuer A over its customized SRI ...

FIGURE 5.3 Cumulative excess return (%) of oil producer Issuer L over its cu...

FIGURE 5.4 Distributions of average returns of SRI excluded issuers relative...

FIGURE 5.5 Contributions to tracking error volatility (TEV in bp/month) over...

FIGURE 5.6 Cumulative excess returns over Bloomberg US Corp. index, %.

FIGURE 5.7 Monthly tracking errors of exposure-matched SRI portfolios and SR...

FIGURE 5.8 SRI exposure-matched portfolios outperform the Bloomberg US corp....

Chapter 6

FIGURE 6.1 Cumulative excess returns associated with positive and negative E...

FIGURE 6.2 Percentage reduction in the number of bonds in the Bloomberg US I...

FIGURE 6.3 Percentage reduction in the number of bonds in the Bloomberg US I...

FIGURE 6.4 Portfolio alpha increases with the number of eligible issuers in ...

FIGURE 6.5 Portfolio alpha and information ratio as functions of portfolio s...

FIGURE 6.6 Effect of ESG exclusions on portfolio alpha: the role of the corr...

FIGURE 6.7 Relative value (Excess Spread to Peers [ESP]) model implementatio...

FIGURE 6.8 Momentum (Equity Momentum in Credit [EMC]) model implementation....

FIGURE 6.9 Cumulative excess returns of systematic exposure-matched portfoli...

FIGURE 6.10 Average ESG score of the remaining US IG index as a function of ...

FIGURE 6.11 Average DTS and OASD of the remaining US IG index as a function ...

FIGURE 6.12 Cross-sectional Kendall rank correlations between systematic val...

FIGURE 6.13 Average percentage reduction in signal tilts of max over min sys...

FIGURE 6.14 Percentage reduction in signal tilt of max over min systematic p...

FIGURE 6.15 Percentage reduction in signal tilt of max over min systematic p...

FIGURE 6.16 Percentage reduction in signal alpha as a function of ESG exclus...

FIGURE 6.17 Cumulative effect of excluding 20% of issuers with the lowest ES...

FIGURE 6.18 Cumulative effect of excluding 40% of issuers with the lowest ES...

FIGURE 6.19 Percentage reductions in signal tilts of systematic strategies w...

FIGURE 6.20 Cumulative effect of SRI exclusions on active returns of value, ...

Chapter 7

FIGURE 7.1 Cumulative ESG return premium.

FIGURE 7.2 Monthly cross-sectional rank correlations between factor and ESG ...

FIGURE 7.3 Average ESG score of factor portfolios relative to the universe a...

FIGURE 7.4 Sector composition after stock exclusion based on ESG scores: acr...

FIGURE 7.5 Sector composition after stock exclusion based on ESG scores and ...

FIGURE 7.6 Size factor composition after excluding 10% of stocks with lowest...

FIGURE 7.7 Quality factor composition after excluding 10% of stocks with low...

FIGURE 7.8 Effect of negative screening on ESG-tilt and factor style in the ...

FIGURE 7.9 Effect on ESG-tilt and factor style for Value: negative screening...

FIGURE 7.10 Effect on ESG-tilt and factor style for Quality: negative screen...

FIGURE 7.11 Cumulative excess returns of generic and ESG-tilted Value factor...

FIGURE 7.12 Cumulative excess returns of generic and ESG-tilted Quality fact...

FIGURE 7.13

t

-statistics for ESG premium beta per ESG-improvement, January 2...

FIGURE 7.14

t

-statistics for factor premium beta per ESG-improvement, Januar...

FIGURE 7.15 Decomposing the effect on Value performance for 10% ESG-tilt (Un...

FIGURE 7.16 Decomposing the effect on Quality performance for 10% ESG-tilt (...

FIGURE 7.17 Cumulative return of the ESG premium contribution in the spread ...

FIGURE 7.18 Cumulative return of the factor premium contribution in the spre...

FIGURE 7.A1 Effect of negative screening on ESG-tilt and factor style in Eur...

FIGURE 7.A2 Effect of negative screening on ESG-tilt and factor style in Jap...

FIGURE 7.A3 Effect on ESG-tilt and factor style for Value in Europe: negativ...

FIGURE 7.A4 Effect on ESG-tilt and factor style for Quality in Europe: negat...

FIGURE 7.A5 Effect on ESG-tilt and factor style for Value in Japan: negative...

FIGURE 7.A6 Effect on ESG-tilt and factor style for Quality in Japan: negati...

FIGURE 7.A7

t

-statistics for ESG premium beta per ESG-improvement in Europe,...

FIGURE 7.A8

t

-statistics for factor premium beta per ESG-improvement in Euro...

FIGURE 7.A9

t

-statistics for ESG premium beta per ESG-improvement in Japan, ...

FIGURE 7.A10

t

-statistics for factor premium beta per ESG-improvement in Jap...

Chapter 8

FIGURE 8.1 Summary of number of ESG score changes.

FIGURE 8.2 Average number of ESG score updates (non-zero ESG score changes) ...

FIGURE 8.3 Time-series averages of group mean and median ESG levels (end of ...

FIGURE 8.4 Average ESG momentum of the Max- and Min-ESGMOM portfolios.

FIGURE 8.5 Effect of ESG momentum on performance.

FIGURE 8.6 Cumulative returns of ESG momentum portfolios with different ESG ...

FIGURE 8.7 Momentum effects from individual E, S, and G pillars and composit...

FIGURE 8.8 Cumulative performance of portfolios capturing ESG momentum and E...

FIGURE 8.9 ESG momentum returns for S&P 500, Russell 1000, and Russell 2000....

FIGURE 8.10 Cumulative excess returns of ESG momentum (Max-over-Min, US HY I...

FIGURE 8.11 ESG momentum effect by ESG score type (S&P 500 universe).

Chapter 9

FIGURE 9.1 Percentage of openings grouped by NAICS for Burning Glass sample ...

FIGURE 9.2 Sample coverage of the S&P 500 index.

FIGURE 9.3 Percentage of postings by ESG pillar.

FIGURE 9.4 Required years of experience for ESG roles.

FIGURE 9.5 Seniority of ESG roles based on title search.

FIGURE 9.6 Overall ESG hiring interest in the sample.

FIGURE 9.7 Rolling 12 month weighted ESG hiring interest, by GICS sectors....

FIGURE 9.8 Average ESG hiring interest across sectors.

FIGURE 9.9 Rolling 12 month weighted ESG hiring interest within financial su...

FIGURE 9.10 Histogram of abnormal ESG HI for firms at the end of 2015.

FIGURE 9.11 Histograms of rating change percent till 2020 for the 2015 cohor...

FIGURE 9.12 Average differences in E-rating change percent between firms wit...

FIGURE 9.13 Differences in E-rating change between firms with top and bottom...

FIGURE 9.14 Cumulative return difference between high and low abnormal ESG H...

Chapter 10

FIGURE 10.1 MSCI Governance scores structure.

FIGURE 10.2 Coverage of composite G-Score in # of stocks (Russell 1000 and S...

FIGURE 10.3 Coverage of theme scores in # of stocks (Russell 1000 and STOXX ...

FIGURE 10.4 Time series of the Governance-Future ROE relation by exposure-ma...

FIGURE 10.5 Time series of the Governance-Future ROE relation by exposure-ma...

Chapter 11

FIGURE 11.1 Number of non-ESG and ESG US equity funds.

FIGURE 11.2 Total AUM by non-ESG and ESG US equity funds.

FIGURE 11.3 Expense ratios by fund type.

FIGURE 11.4 Coverage of ESG data for stocks in the S&P 500 index.

FIGURE 11.5 Coverage of ESG data for the US stock market.

FIGURE 11.6 Average MSCI ESG score for ESG and non-ESG funds benchmarked to ...

FIGURE 11.7 Average Vigeo Eiris ESG score for ESG and non-ESG funds benchmar...

FIGURE 11.8 MSCI ESG score for S&P 500 index.

FIGURE 11.9 Vigeo Eiris ESG score for S&P 500 index.

FIGURE 11.10 Average market capitalization of holdings for ESG and non-ESG f...

FIGURE 11.11 Average MSCI ESG score for ESG funds with and without ESG bench...

FIGURE 11.12 Average Vigeo Eiris ESG score for ESG funds with and without ES...

FIGURE 11.13 Average MSCI ESG score around the adoption of an ESG objective....

FIGURE 11.14 Average Vigeo Eiris ESG score around the adoption of an ESG obj...

FIGURE 11.15 Abnormal cumulative return around the adoption of an ESG object...

FIGURE 11.16 Fund flows by fund type.

FIGURE 11.17 Cumulative performance by fund type.

FIGURE 11.18 Piecewise linear dependence of fund flows on past performance....

Chapter 12

FIGURE 12.1 ESG ranking imposes a uniform distribution on the ESG characteri...

FIGURE 12.2 In reality, the ESG characteristics of companies are thought to ...

FIGURE 12.3 Distribution of E pillar scores from Vigeo Eiris.

FIGURE 12.4 Transformed E pillar scores from Sustainalytics.

FIGURE 12.5 Distribution of Vigeo Eiris E scores.

FIGURE 12.6 Partitioning of (transformed) Sustainalytics E scores.

FIGURE 12.7 Vigeo Eiris E scores after being normalized.

FIGURE 12.8 Sustainalytics E scores after being normalized.

FIGURE 12.9 Re-scaled Vigeo Eiris E scores post-algorithm.

FIGURE 12.10 Re-scaled Sustainalytics E scores post-algorithm.

FIGURE 12.11 Final distribution of averaged Environmental scores.

FIGURE 12.12 Distribution of averaged Environmental scores for Consumer and ...

Chapter 13

FIGURE 13.1 Descriptive statistics of ESG score and dispersion.

FIGURE 13.2 Average ESG dispersion across sectors.

FIGURE 13.3 Time series and sub-period performances of Max-over-Min replicat...

Guide

Cover

Title Page

Copyright

Dedication

Foreword

Preface

Acknowledgements

Introduction

Table of Contents

Begin Reading

Index

End User License Agreement

Pages

ii

iii

iv

v

xiii

xiv

xv

xvi

xvii

xix

xx

xxi

xxii

xxiii

xxiv

1

2

3

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

Founded in 1807, John Wiley & Sons is the oldest independent publishing company in the United States. With offices in North America, Europe, Australia, and Asia, Wiley is globally committed to developing and marketing print and electronic products and services for our customers’ professional and personal knowledge and understanding.

The Wiley Finance series contains books written specifically for finance and investment professionals as well as sophisticated individual investors and their financial advisors. Book topics range from portfolio management to e-commerce, risk management, financial engineering, valuation, and financial instrument analysis, as well as much more.

For a list of available titles, visit our Web site at www.WileyFinance.com.

Measuring ESG Effects in Systematic Investing

ARIK BEN DOR, ALBERT DESCLÉE, LEV DYNKIN, JINGLING GUAN, JAY HYMAN SIMON POLBENNIKOV

This edition first published 2024

Arik Ben Dor, Albert Desclée, Lev Dynkin, Jingling Guan, Jay Hyman, and Simon Polbennikov ©2024

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

The right of Arik Ben Dor, Albert Desclée, Lev Dynkin, Jingling Guan, Jay Hyman, and Simon Polbennikov to be identified as the authors of this work has been asserted in accordance with law.

Registered OfficesJohn Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA

John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK

Editorial OfficeThe Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Wiley also publishes its books in a variety of electronic formats and by print-on- demand. Some content that appears in standard print versions of this book may not be available in other formats. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book.

Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates in the United States and other countries and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and author endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

Library of Congress Cataloging-in-Publication Data is Available:

ISBN 9781394214785 (Cloth)ISBN 9781394214792 (ePDF)ISBN 9781394214808 (ePub)

Cover Design: WileyCover Image: © fantasyform/Shutterstock

To my parents, Lya and Ron, for their lifelong dedication, love, and sacrifice, my wife Melina for her support and encouragement throughout, my brother Oren for always being there for me, and my biggest pride and achievement, my children Shiraz, Shelly, Tamir, and Nili

–ABD

To my wife, Anne-Louise, for her patience and support

–AD

To my wife Alina for her unwavering support and to my children David, Aryeh, Joseph, and Rachel who inspire all my work

–LD

To my mother Zuohua Yu and my father Xiaogang Guan for their unconditional love, support, and encouragement

–JG

To my dear wife Ella, who continually inspires and empowers me with her indomitable creative spirit

–JH

To my colleagues

–SP

Foreword

There are many ways in which the financial industry can facilitate the path to a sustainable economy. These include financing relevant companies and projects, developing themed investment products, contributing to the development of regulatory guidelines, and influencing corporate disclosure in related areas. One of the key tasks in motivating the efforts in all of these directions is the quantification of the impact of ESG investing on the performance and valuation of financial assets. While financial performance is not the only decision variable in shaping the integration of sustainability principles into corporate practice and investment management, it is certainly an important consideration, given the fiduciary responsibilities of corporate boards to shareholders and portfolio managers to their investors.

Has an ESG tilt been additive, all else equal, to performance of credit and equity portfolios? Have investments by corporations in improving their ESG ratings paid off in improved valuation of their bonds and stock? Objective, data-driven answers to such questions have only recently become feasible because they require, in addition to quantitative research expertise, sufficient accumulation of historical data. The authors took full advantage of such data to develop innovative methodologies of quantifying ESG effects on financial assets.

The authors of this book are part of the top-ranked Quantitative Portfolio Strategy (QPS) team within Barclays Research.1 They do not seek to present their views on ESG investing. Rather, they approach ESG investing from a purely quantitative perspective. They offer important methodologies for measuring ESG factor returns and quantifying their effects on portfolio performance. ESG is a firm-level attribute. Its impact on performance of financial assets must be analysed in a consistent fashion across the debt and equity securities of a company. An integrated approach not only provides the reader an opportunity to understand ESG effects more broadly, but also to demonstrate how robust these effects are. By drawing on their experience across bond and equity markets, as well as ESG-related expertise across Barclays Research, the authors of this book are uniquely positioned to offer readers a map for consistently navigating ESG implications in both credit and equity investing.

This work represents yet another successful installment in the research efforts of the QPS team.

C.S. Venkatakrishnan

Group Chief Executive Officer, Barclays

NOTE

1

.  The QPS team was ranked number 1 in quantitative analysis in the Fixed Income Institutional Investor Survey in 2023.

Preface

This book views the sustainability aspect of institutional investing—a topic often debated based on convictions and opinions—through a purely quantitative, objective lens. The authors are members of the Quantitative Portfolio Strategy (QPS) Group, which has been a part of Barclays' research for over 15 years. The group's mandate includes advising the largest institutional investors around the globe on any quantitative aspects of portfolio management across asset classes including fixed income and equity.

As a result, all of the research from this team, this book included, addresses practical issues of the investment process. The group enjoys a strong reputation in the industry as evidenced by its long-standing high ranking in the Institutional Investor Fixed Income research survey for the past 15 years and the readership of its prior four books—all on different aspects of quantitative portfolio management. The group's dual focus on equity and fixed income portfolio management allows it to apply consistent methodologies across asset classes and perform additional verification of their robustness. QPS research on ESG investing is informed by the focus of the larger Barclays' Research on various related topics—from the evolution of the regulatory landscape to natural language processing of ESG-related text.

This book doesn't take sides in the debate on the merits of ESG investing but rather informs it by providing data-driven evidence of the impact of the sustainability tilt on portfolio performance and valuation. Quantifying this impact requires controlling all other systematic exposures in an ESG-compliant portfolio. The authors propose a comprehensive approach to isolating ESG-related effects on investment performance and valuation, apply it consistently to both credit and equity portfolios, and track these effects historically in both markets. The authors also address one of the main challenges of ESG research: the lack of an industry standard for what aspects of corporate activity should be measured as part of the evaluation of ESG compliance, how to measure them, and how to summarize the complex set of disparate activities undertaken across the breadth of a large corporation. The authors not only present a mechanism for normalizing diverse scores across providers to make them comparable, but also show that the extent of their dispersion itself has implications for future portfolio performance.1

The authors investigate the impact of an ESG tilt on characteristics of traditional equity style factors and on systematic credit style factors such as value and momentum.

In addition to a detailed presentation of the issues facing ESG investors, the book discusses the implications for corporations of the investments they make to improve their ESG footprint.

The methodologies and findings described in this book are relevant to all investment practitioners active in sustainable investing in either equity or credit as well as to researchers, risk managers, and academics in this field.

Jeff Meli

Global Head of Research, Barclays

NOTE

1

.  

This book is published for academic purposes. The information provided in this book does not constitute ‘investment research’ or a ‘research report’ and should not be relied on as such. This book does not contain investment advice or recommendations and it should not be used to make investment decisions. Information in this book does not constitute a financial benchmark. Information in this book may not be accurate or complete and may be sourced from third parties. Any past or simulated past performance including back-testing, modelling or scenario analysis contained herein is no indication as to future performance or results

.

Environmental, Social, and Governance (‘ESG’) Related Information: There is currently no globally accepted framework or definition (legal, regulatory or otherwise) of, nor market consensus as to what constitutes, an ‘ESG’, ‘green’, ‘sustainable’, ‘climate-friendly’ or an equivalent company, investment, strategy or consideration or what precise attributes are required to be eligible to be categorized by such terms. This means there are different ways to evaluate a company or an investment and so different values may be placed on certain ESG credentials as well as adverse ESG-related impacts of companies and ESG controversies. The evolving nature of ESG considerations, models and methodologies means it can be challenging to definitively and universally classify a company or investment under an ESG label and there may be areas where such companies and investments could improve or where adverse ESG-related impacts or ESG controversies exist. The evolving nature of sustainable finance related regulations and the development of jurisdiction-specific regulatory criteria also means that there is likely to be a degree of divergence as to the interpretation of such terms in the market. It is expected that industry guidance, market practice, and regulations in this field will continue to evolve. Any references to ‘sustainable’, ‘sustainability’, ‘green’, ‘social’, ‘ESG’, ‘ESG considerations’, ‘ESG factors’, ‘ESG issues’ or other similar or related terms in this book are not references to any jurisdiction-specific regulatory definition or other interpretation of these terms, unless specified otherwise.

Acknowledgements

The authors would like to thank their colleagues from the Quantitative Portfolio Strategy (QPS) team at Barclays Research—Mathieu Dubois, Stephan Florig, Felix Kempf, Vadim Konstantinovsky, Hugues Langlois, Alberto Pellicioli, Yunpeng Sun and Xiaming Zeng—for their contributions to this book and their help in preparing and reviewing the manuscript.

We would also like to thank our colleagues from other parts of Barclays Research: Maggie O'Neal for valuable discussions of ESG-related topics and for writing the introduction to Part IV; Ryan Preclaw and Adam Kelleher for their partnership in analyzing some of the large data sets used in this book; and Valerie Monchi and Amy Pompliano for their guidance on compliance aspects of the production of this book.

The authors are grateful to Jeff Meli, Global Head of Barclays Research, for his continued support of the group's work.

Finally, the authors would like to thank their families for bearing over the years the sacrifices of family time necessary to produce the research in this book and prepare the book for publication.

Introduction

The ongoing debate about the merits of ESG (Environment, Social, Governance) investing in financial markets requires careful measurement of its effect on portfolio performance. Investors may choose to integrate ESG tilts in their portfolios for different reasons, based on sustainability considerations and/or because they believe that ESG ratings reflect material risks and corresponding performance opportunities. These considerations may be reflected in the investment policy in different ways, ranging from strict exclusion of companies and sectors involved in non-compliant activities to a more nuanced best-in-class approach that selects the companies with the best ESG rankings within each peer group.

A simple comparison between the returns of a sustainability index and the standard underlying index, whether in equities or in credit, can result in a distorted view of the ESG effect on performance. Two such indices could differ in sector allocations, average issue size, and credit ratings—all sources of performance with risk premia of their own. How should we measure the effect of ESG investing on portfolio performance? Do traditional risk factors in both equity and credit markets retain their properties when subjected to ESG constraints? Do measures taken by corporate issuers to improve their ESG profile help their subsequent ratings and the performance of their debt and equity securities? How should investors handle the lack of uniformity in ESG definitions? Addressing all these issues requires a quantitative framework aligned with the systematic approach to investing.

We pursue a consistent parallel analysis of the ESG effect on systematic strategies in equity and bond markets. Applied to security selection these strategies involve the systematic use of financial models for all securities within the investment universe, and the construction of highly diversified portfolios that reflect a number of investment themes, or factors, in a risk-efficient manner. While systematic investing has been in the mainstream of equity investing for decades, it has recently started gaining popularity among bond investors as well. There are several reasons for these past differences and for the recent convergence in acceptance of algorithmic investing between the two markets. Most equities are exchange traded and more liquid than bonds. Equity market data have been broadly available to researchers in academia and the financial industry for many years. As a result, all aspects of quantitative investing in equities—from definition of the factors driving stock returns, to selection signals predictive of future security or sector performance, to portfolio optimization methodologies—have been well researched, exploited by investors, and widely accepted alongside the traditional fundamental, discretionary investment style. In the past few years fixed-income investors also saw increased availability of bond market data from vendors, improved price transparency, increased liquidity due to regulatory reporting requirements to shared databases such as TRACE, and a rise in e-trading, ETFs, and portfolio trading. All of these developments, coupled with the influence of established quantitative insights from the equity markets, enabled the expansion of systematic investing to fixed income, as we discussed in our book, Systematic Investing in Credit (Wiley, 2021). In the current volume, we focus on the intersection of systematic investing with the trend towards ESG integration, particularly on the impact of an ESG ratings tilt (‘positive screening’) or of ESG-related exclusions (‘negative screening’) on the performance of systematic strategies in credit and equities and on the valuation of securities. Our objectives are to offer consistent methodologies for measuring the effects of ESG on the performance of equity and fixed income portfolios, to document the historical magnitude of these effects and the related valuation trends, to quantify the impact of ESG constraints on the performance of systematic strategies and style factors, and to measure the efficacy of corporate actions in the sustainability area.

The book is purely methodological and relies on historical analysis of market data,1 offering no subjective views on the merits of ESG investing. This is in line with the long-standing mandate of our research group. The authors are members of the Quantitative Portfolio Strategy (QPS) group, which has been a part of Barclays (and previously Lehman Brothers) Research for over three decades. The group has a unique focus on working with major institutional investors across the globe on any issues of portfolio management that are quantitative in nature. As a result of this focus, research produced by the group tends to be practical and implementable. The group's publications target portfolio managers and other investment practitioners, as well as research analysts and academics. The group's past involvement in the creation of fixed-income indices and expertise in quantitative research in both equities and bonds further helped it develop consistent methodologies across the two markets. To enable parallel analysis in equity and bond markets, we rely on a proprietary issuer-level historical mapping (that accounts for corporate events) between corporate bonds and equity of a given company. The approach taken in this book is fully objective and free of any views or opinions. Rather, we ‘let the data speak’.

The conventional definition of systematic strategies includes fully rule-based algorithmic methodologies aimed at improving portfolio performance by generating alpha. Some of them fall into the ‘smart beta’ category and take advantage of inefficiencies in the design of traditional market indices. Others harvest risk premia associated with risk factors, both traditional and new. In this book, we take a more expansive view of systematic investing to include any aspects of portfolio construction that are quantitative in nature. For example, we will include in this expanded definition methodologies for isolating the ESG risk premium from other unrelated systematic exposures. In the language of systematic investing, a risk factor is a source of portfolio risk independent of other established risk factors, which is priced in the market and is expected to be compensated by extra portfolio return—the risk premium. Is ESG a risk factor? Do bonds issued by firms that have strong ESG ratings have fundamentally different risk profiles than those with low ESG ratings? On the one hand, many proponents of ESG investing hold the view that stronger governance is associated with management quality, and hence corporate decisions that lead to higher investor cash flows. Stronger credentials on the Environmental and Social dimensions may reduce exposure to adverse corporate developments such as litigation, changes in regulation, or changes in customer acceptance. On the other hand, there has been insufficient empirical evidence so far that ESG ratings are indeed associated with systematic risk. In this book, although we use the term ‘ESG risk premium’ to refer to the isolated ESG-related return (free of any other risk factor exposures and idiosyncratic risk), we are not taking a view on whether ESG exposure is a risk factor that should be expected to carry a risk premium. (In fact, in Chapter 4 we show that for sovereign bonds the ESG-related return is subsumed by the credit rating.) We hope that our work to document the relationships between ESG characteristics will help inform this discussion going forward.

All the materials included in the book reflect original QPS research as it was first published. With few exceptions where an update was essential, we decided against going back and updating the data analysis in individual chapters to avoid any possibility of hindsight tainting the results.

This book is structured in four parts.

In Part I, we address the seemingly simple question of how to measure the returns associated with an ESG tilt in a portfolio or an index. Most sustainable versions of broad market indices in both equity and credit are defined by exclusion of non-compliant issuers or industries. However, the difference in performance between these sustainable indices and the original index cannot be interpreted as return due to ESG, as the two indices differ in sector allocations, credit quality, issuer size, and a number of other characteristics that also affect security returns. Even if sector allocations are constrained to match the broad index, tilting a portfolio within sectors towards high ESG issuers will simultaneously tilt it towards higher rated, large-cap companies, which tend to be more compliant. We propose a methodology for isolating the performance effect of ESG while matching the underlying index in all other risk dimensions, and we document the behaviour of this premium in equities and bonds over time. The ESG risk premium obtained in this exposure-matched way, free of all systematic biases, can differ from the simple performance differential between a sustainable and standard index not only in magnitude, but also in sign. Separately we study the ESG effect on the pricing and performance of sovereign bond portfolios. In addition to our methodology for measuring the performance of ‘best-in-class’ ESG investing, we also study the effect of the exclusionary approach of Socially Responsible Investing (SRI) on credit portfolio performance. The negative screening of entire industry groups makes it difficult to exactly match index risk characteristics; we therefore introduce a new technique for measuring the performance effect of such constraints.

In Part II, we measure the impact of ESG constraints on the performance of a systematic credit strategy that utilizes three of our proprietary signals—value, momentum, and sentiment. The key question addressed is whether the ESG constraints interfere with the strategy's ability to generate alpha. We follow this up with a study of the ESG effect on the return profile of equity style factors introduced by our group. These include, among others, well-established factors such as momentum, value, growth, quality, yield, low volatility, and size (some of them with proprietary changes in definition), which our group publishes across global equity markets. We test whether the return profile associated with each factor is preserved after applying ESG constraints of different types.

In Part III, we switch our focus from studies of ESG-related choices made by investors to the implications of ESG-related activities of the issuers. Does the market reward corporations with improving ESG scores by raising the valuations of their debt and equity? Do ratings providers reward companies that hire for ESG-related positions at a greater rate than their peers by raising their ESG ratings? Does improved corporate governance as measured by the G in the ESG ratings lead to higher company profitability?

In Part IV, we analyse the investment implications of the dispersion in ESG scores across different providers and of ESG labelling of mutual funds. Sustainable investing is still a young field and convergence to standards is not yet complete. This applies both to ESG rating methodologies and to the scale on which these ratings are assigned. This dispersion complicates score comparison across vendors. We show how to properly calculate a consensus score among multiple providers despite these difficulties by first normalizing the scores. Even after this normalization, there can be significant disagreement among score providers. Does such disagreement have implications for future ESG returns? A similar lack of clarity can be found in the labelling of mutual funds, particularly in the United States. Do ESG-labelled funds indeed invest in issuers with above-average ESG ratings? How does this label influence fund performance, fund flows, and AUM?

This book could not have been written until a sufficient history of ESG scores became available across multiple vendors utilizing comparable (even if different) methodologies. Some of the ESG-related effects discussed in Parts III and IV had persistent implications for performance of equities and bonds over the period of the respective studies. We find that both equity and credit securities of issuers with improving ESG ratings outperformed their peers with unchanged or declining ESG scores on an all-else-equal basis. Securities of issuers with significant dispersion of ESG scores across rating providers underperformed their risk-matched peers with more consensus on their ratings. Firms with an above-average rate of ESG-related hiring saw their ratings subsequently improve and their equity outperform risk-matched peers. We document these predictive relationships between ESG attributes and subsequent performance, but hesitate to label them as persistent alpha sources since these relationships may change according to investor interest in ESG investing. In fact, all of our numeric findings are subject to change—ESG-related returns that were positive over one period of history can turn negative in another. The evolving regulatory landscape can change the dynamics of ESG ratings produced by different vendors or the rules of ESG fund labelling. However, it is our hope that the methodologies outlined here will remain applicable throughout changing markets and regulations and will help investors navigate ESG-related decisions in their bond and equity portfolios.

We would like to thank our clients for the stimulating questions and continual dialogue that led to many of the results covered in this book, our colleagues who provided invaluable help with the analysis and preparation of the manuscript, and the senior management of Barclays for their unwavering support and encouragement of our work. We hope that portfolio managers, research analysts, and academics in the field of systematic investing, in both fixed income and equities, will find these chapters useful. As always, we welcome inquiries and challenges to our work.

Lev Dynkin

Global Head of Quantitative Portfolio Strategy, Barclays Research

NOTE:

      Notes on Data Providers:

“MSCI” refers to MSCI ESG Research.

“Compustat” refers to S&P Global Market Intelligence Compustat®.

“CSI” refers to the China Securities Index Company. All rights in the CSI 800 (“Index”) vest in China Securities Index Company (“CSI”). CSI does not make any warranties, express or implied, regarding the accuracy or completeness of any data related to the Index. CSI is not liable to any person for any error of the Index (whether due to negligence or otherwise), nor shall it be under any obligation to advise any person of any error therein.

PART OneEffect of ESG Constraints on Portfolio Performance and Valuation

INTRODUCTION TO PART I

The very first question to address in discussing ESG-related investing is the effect an ESG tilt has on portfolio performance and the valuation of securities. Has ESG compliance been a benefit or a cost to portfolio returns? Have investors who elected to introduce an ESG tilt been rewarded by superior performance compared with ones that ignored this tilt or even took a contrarian view on its return impact? In markets and time periods when the ESG tilt benefited the portfolio performance, has it been achieved by an increase in valuation of high-ESG securities, which should at some point stop, mean-revert, and generate future underperformance? Given the fact that ESG ratings are formed at the issuer level, has the ESG risk premium been consistent across the equity and bonds of an issuer? With European asset managers leading the United States in ESG adoption, has this risk premium been consistent across the two geographies?

This seemingly simple question of what part of a portfolio return is related to ESG is often answered incorrectly. One approach to computing this risk premium is to sort the universe of securities by ESG scores and measure the performance difference between the highly rated and low-rated parts of this universe. Another common approach is to compare the return of a standard index describing a given market segment to its sustainable version, often built by excluding non-ESG-compliant sectors and issuers. In Part I we argue that both of these approaches are misleading. As we show in Chapter 1, securities with high ESG ratings tend to be issued by large, highly rated companies which are able to fund ESG compliance initiatives and related reporting. They also tend to be concentrated in compliant market sectors which can perform differently from the broad market. So a simple difference between the performance of a high-ESG-rated portfolio and one with low ESG scores can reflect a size risk premium, a quality premium, or sector timing mixed in with the ESG risk premium.

In Chapters 2 and 3, we propose a consistent approach across credit and equity markets to computing a pure ESG risk premium (or the ESG part of the return) in isolation, controlling for all other risk factors. For a given market segment (e.g. S&P 500, investment grade credit, high yield) we create portfolios that are risk-matched to the corresponding index in every risk exposure except the ESG rating. We first seek to maximize this rating subject to constraints on all other systematic risk exposures and to a diversification constraint (to avoid impact of issuer-specific risk). We then we similarly find the low-ESG risk-matched portfolio. These two portfolios match in all risk attributes that affect performance (average issue size, credit rating, sector distribution, etc.) and differ only in their ESG exposure. We suggest that the difference in returns between these max-ESG and min-ESG portfolios represents a pure ESG risk premium and can be used to evaluate the effect of an ESG tilt on portfolio performance. We apply this risk-matched approach consistently across credit, high yield, and equities in different geographies and document the trajectory of this pure ESG risk premium in all these markets over the study period. Interestingly, results obtained using this risk-matched methodology can differ from the naïve measures of the ESG risk premium described earlier not only in magnitude but also in sign.

In Chapter 4, we study the effect of ESG on the pricing and performance of sovereign bonds. As in corporate markets, we find that ESG criteria tend to favour higher-quality sovereign issuers. ESG-tilted sovereign bond portfolios, if unconstrained, will therefore have higher credit quality and lower spreads. However, once we control for credit quality, we find that ESG attributes do not have a statistically significant effect on either spreads or portfolio returns. This is established using both a statistical approach and using our risk-matched portfolio construction methodology.

The risk-matched methodology featured in Chapters 2 to 4 is aligned with the ‘best-in-class’ approach, in which each market sector is represented in the portfolio by the issuers with the highest ESG ratings in the sector. However, many ESG-labelled credit funds employ a negative screening approach, in which they exclude issuers whose business activities conflict with certain values or social norms. The negative screening approach can lead to a very different effect on portfolio performance, as the systematic risk premium of the excluded sectors may fluctuate with market regimes and result in unintended portfolio volatility. In Chapter 5, we analyse the effect of such negative screening strategies, often referred to as Socially Responsible Investing (SRI), on the performance of credit portfolios, from both a bottom-up and a top-down perspective.