ANDREW GRANT is a Senior Lecturer in Finance at the University of Sydney. His main areas of expertise are behavioural finance, individual investor decision making, and betting markets. He has also been engaged with industry in the Asia-Pacific region. Andrew is a frequent speaker at conferences and seminars. STEPHEN SATCHELL is Fellow of Economics, Trinity College Cambridge, UK. He also works as an advisor to financial institutions and as a quantitative facilitator bringing clients together. Stephen lectures frequently at finance industry seminars and is on the committees for several leading quantitative research groups.

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• Contributors xviiIntroduction xxiiiChapter 1 Behavioural Finance and Momentum 11.1 Introduction 11.2 The failure of risk-based explanations 31.3 Behavioural models of momentum 31.4 Slow information diffusion 51.5 Patterns in information arrival 61.6 The 52-week high and capital gains overhang 81.7 Institutional trading and momentum profits 101.8 Sentiment and momentum 111.9 Discussion 12Chapter 2 A Taxonomy of Momentum Strategies 162.1 Introduction 162.2 Relative strength strategies 172.3 Time-series momentum strategies 182.4 Cross-sectional momentum strategies 202.5 Cross-asset momentum 27Chapter 3 Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations 303.1 Data Description 343.2 Methodology 393.3 Turnover Reduction 423.4 The Recent Underperformance of Time-series Momentum Strategies and the Effect of Pairwise Correlations 523.5 Trading Costs Implications 583.6 Concluding Remarks 63Chapter 4 Risk and Return of Momentum in Developed Equity Markets 684.1 Introduction 684.2 Definition of momentum 694.3 Simple factor portfolios 714.4 Multifactor structure 734.5 Pure factor portfolios 754.6 Empirical results: momentum performance 764.7 Empirical results: momentum risk 804.8 Diversification benefits 834.9 Summary 84Chapter 5 Momentum Across Asset Classes 865.1 Measuring momentum 875.2 Framework: equity momentum and corporate credit risk 875.3 Empirical studies: momentum and credit risk 895.4 Our research on equity momentum and bond returns 915.5 Geographically bound assets 925.6 Momentum in other illiquid assets 945.7 Cross-asset class effects of commodities 955.8 Momentum effects and taxable investors 955.9 Active management and momentum effects 965.10 Conclusions 98Chapter 6 Momentum in Momentum ETFs 1036.1 Introduction 1036.2 Why are momentum ETFs so popular? 1046.3 What is in a momentum ETF? 1126.4 Which factors drive active risk for momentum ETFs? 1146.5 From constrained to unconstrained strategies 1176.6 Conclusions 119Chapter 7 CTA Momentum 1207.1 Introduction 1207.2 Time-series momentum (TSM) 1217.3 Strategy return models 1277.4 Time-series momentum 1317.5 TSM meets CSM with two instruments 1337.6 Conclusions 1357.A.1 Appendix A: Correlation parameter restrictions 1367.A.2 Appendix B: Proofs of variances and covariance 138Chapter 8 Overreaction and Faint Praise – Short-Term Momentum in Contemporary Art 1418.1 Introduction 1418.2 Contemporary art market ecosystem 1448.3 ArtForecaster data 1458.4 Systematic forecasting strategies 1498.5 Conclusions 157Chapter 9 Volatility-Managed Momentum 1609.1 Introduction 1609.2 Data and momentum portfolio construction 1619.3 Volatility-managed momentum strategies 1629.4 Some potential practical issues 1669.5 The best volatility measure for momentum? 1709.6 Concluding remarks 172Chapter 10 Theoretical Analysis of the Fama-French Portfolios 17410.1 Introduction 17410.2 Strategies, notation and preliminaries 17910.3 Distribution of Fama-French factors 18210.4 Fama-French factors with sequential sorting 18910.5 Conclusion 19410.A.1 Proof of Lemma 1 19410.A.2 Proof of Theorem 3 19510.A.3 Proof of Theorem 4 196Chapter 11 Exploiting the Countercyclical Properties of Momentum and other Factor Premia – A Cross-Country Perspective 19911.1 Introduction 19911.2 Methodology 20011.3 Alternative investment strategies 20611.4 Quantifying the utility of risk premia strategies 21111.5 Summary and conclusions 215Chapter 12 Time-Series Variation in Factor Premia: The Influence of the Business Cycle 21812.1 Introduction 21812.2 Factors and factor rotation 21912.3 Factors and the business cycle 22012.4 Data and summary statistics 22212.5 Empirical results 22412.6 Conclusions 23412.A.1 Derivation of cash-flow news series 23412.A.2 US leading economic indicator and global risk appetite indicator 23612.A.3 Dynamic multifactor strategy: extension to other market segments and regions 236Chapter 13 Where Goes Momentum? 24313.1 Introduction 24313.2 Momentum strategies 24513.3 Data 24613.4 Method 24713.5 Results 25213.6 Risk-adjusted after-transaction costs performance of time-series and cross-sectional momentum strategies 26013.7 Conclusions 269Chapter 14 Time-Series Momentum in Credit: Machine Learning Approach 27314.1 Introduction 27314.2 The philosophy of artificial intelligence 27414.3 Vanilla time-series momentum 27714.4 Generalized linear models (GLM) – Lasso, Ridge and Elastic Net 28014.5 Determining optimal hyper-parameters via cross-validation 28314.6 Results: generalized linear models 28414.7 Random forests 28414.8 Neural networks 28914.9 Results and comments 29114.10 Conclusion 293Chapter 15 Momentum and Business Cycles 29715.1 Introduction 29715.2 Momentum, business cycles and realised market return 29815.3 Momentum and expected market risk premiums 30115.4 Momentum, overconfidence and sentiment 30915.5 Summary and conclusions 311Chapter 16 Momentum as a Fundamental Risk Factor 31416.1 Introduction 31416.2 Defining momentum as a strategy 31616.3 A new framework 31816.4 From realised returns to forecast returns 31916.5 Examining behaviour 31916.6 The momentum trader as a bystander 32316.7 Extending the model 32516.8 Short-term versus long-term investors 32616.9 The impact of the short-term investor 33016.10 The momentum risk premium 33216.11 The Apollo asset pricing model 33416.12 Momentum alpha 33516.13 Beta momentum 33916.14 Beta signal 34016.15 Momentum strategies 34116.16 Results 34716.17 Analysis of results 35316.18 Conclusions 355Chapter 17 Momentum, Value and Carry Commodity Factors for Multi-Asset Portfolios 35917.1 Introduction 35917.2 Methodology and key research questions 36117.3 Commodity factors – insights from the historical data 36217.4 Wealth accumulation strategies and rebalancing considerations 36617.5 Wealth decumulation strategies 37317.6 Long/short versus long only strategies 37517.7 Completion portfolios versus maximum Sharpe ratio portfolios 37917.8 Conclusions 38017.A.1 Momentum factor 38117.A.2 Carry factor 38117.A.3 Value factor 38217.A.4 From commodity factors to factor portfolios 38317.A.5 Factor construction 383Index 387

ANDREW GRANT is a Senior Lecturer in Finance at the University of Sydney. His main areas of expertise are behavioural finance, individual investor decision making, and betting markets. He has also been engaged with industry in the Asia-Pacific region. Andrew is a frequent speaker at conferences and seminars. STEPHEN SATCHELL is Fellow of Economics, Trinity College Cambridge, UK. He also works as an advisor to financial institutions and as a quantitative facilitator bringing clients together. Stephen lectures frequently at finance industry seminars and is on the committees for several leading quantitative research groups.

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• Contributors xviiIntroduction xxiiiChapter 1 Behavioural Finance and Momentum 11.1 Introduction 11.2 The failure of risk-based explanations 31.3 Behavioural models of momentum 31.4 Slow information diffusion 51.5 Patterns in information arrival 61.6 The 52-week high and capital gains overhang 81.7 Institutional trading and momentum profits 101.8 Sentiment and momentum 111.9 Discussion 12Chapter 2 A Taxonomy of Momentum Strategies 162.1 Introduction 162.2 Relative strength strategies 172.3 Time-series momentum strategies 182.4 Cross-sectional momentum strategies 202.5 Cross-asset momentum 27Chapter 3 Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations 303.1 Data Description 343.2 Methodology 393.3 Turnover Reduction 423.4 The Recent Underperformance of Time-series Momentum Strategies and the Effect of Pairwise Correlations 523.5 Trading Costs Implications 583.6 Concluding Remarks 63Chapter 4 Risk and Return of Momentum in Developed Equity Markets 684.1 Introduction 684.2 Definition of momentum 694.3 Simple factor portfolios 714.4 Multifactor structure 734.5 Pure factor portfolios 754.6 Empirical results: momentum performance 764.7 Empirical results: momentum risk 804.8 Diversification benefits 834.9 Summary 84Chapter 5 Momentum Across Asset Classes 865.1 Measuring momentum 875.2 Framework: equity momentum and corporate credit risk 875.3 Empirical studies: momentum and credit risk 895.4 Our research on equity momentum and bond returns 915.5 Geographically bound assets 925.6 Momentum in other illiquid assets 945.7 Cross-asset class effects of commodities 955.8 Momentum effects and taxable investors 955.9 Active management and momentum effects 965.10 Conclusions 98Chapter 6 Momentum in Momentum ETFs 1036.1 Introduction 1036.2 Why are momentum ETFs so popular? 1046.3 What is in a momentum ETF? 1126.4 Which factors drive active risk for momentum ETFs? 1146.5 From constrained to unconstrained strategies 1176.6 Conclusions 119Chapter 7 CTA Momentum 1207.1 Introduction 1207.2 Time-series momentum (TSM) 1217.3 Strategy return models 1277.4 Time-series momentum 1317.5 TSM meets CSM with two instruments 1337.6 Conclusions 1357.A.1 Appendix A: Correlation parameter restrictions 1367.A.2 Appendix B: Proofs of variances and covariance 138Chapter 8 Overreaction and Faint Praise – Short-Term Momentum in Contemporary Art 1418.1 Introduction 1418.2 Contemporary art market ecosystem 1448.3 ArtForecaster data 1458.4 Systematic forecasting strategies 1498.5 Conclusions 157Chapter 9 Volatility-Managed Momentum 1609.1 Introduction 1609.2 Data and momentum portfolio construction 1619.3 Volatility-managed momentum strategies 1629.4 Some potential practical issues 1669.5 The best volatility measure for momentum? 1709.6 Concluding remarks 172Chapter 10 Theoretical Analysis of the Fama-French Portfolios 17410.1 Introduction 17410.2 Strategies, notation and preliminaries 17910.3 Distribution of Fama-French factors 18210.4 Fama-French factors with sequential sorting 18910.5 Conclusion 19410.A.1 Proof of Lemma 1 19410.A.2 Proof of Theorem 3 19510.A.3 Proof of Theorem 4 196Chapter 11 Exploiting the Countercyclical Properties of Momentum and other Factor Premia – A Cross-Country Perspective 19911.1 Introduction 19911.2 Methodology 20011.3 Alternative investment strategies 20611.4 Quantifying the utility of risk premia strategies 21111.5 Summary and conclusions 215Chapter 12 Time-Series Variation in Factor Premia: The Influence of the Business Cycle 21812.1 Introduction 21812.2 Factors and factor rotation 21912.3 Factors and the business cycle 22012.4 Data and summary statistics 22212.5 Empirical results 22412.6 Conclusions 23412.A.1 Derivation of cash-flow news series 23412.A.2 US leading economic indicator and global risk appetite indicator 23612.A.3 Dynamic multifactor strategy: extension to other market segments and regions 236Chapter 13 Where Goes Momentum? 24313.1 Introduction 24313.2 Momentum strategies 24513.3 Data 24613.4 Method 24713.5 Results 25213.6 Risk-adjusted after-transaction costs performance of time-series and cross-sectional momentum strategies 26013.7 Conclusions 269Chapter 14 Time-Series Momentum in Credit: Machine Learning Approach 27314.1 Introduction 27314.2 The philosophy of artificial intelligence 27414.3 Vanilla time-series momentum 27714.4 Generalized linear models (GLM) – Lasso, Ridge and Elastic Net 28014.5 Determining optimal hyper-parameters via cross-validation 28314.6 Results: generalized linear models 28414.7 Random forests 28414.8 Neural networks 28914.9 Results and comments 29114.10 Conclusion 293Chapter 15 Momentum and Business Cycles 29715.1 Introduction 29715.2 Momentum, business cycles and realised market return 29815.3 Momentum and expected market risk premiums 30115.4 Momentum, overconfidence and sentiment 30915.5 Summary and conclusions 311Chapter 16 Momentum as a Fundamental Risk Factor 31416.1 Introduction 31416.2 Defining momentum as a strategy 31616.3 A new framework 31816.4 From realised returns to forecast returns 31916.5 Examining behaviour 31916.6 The momentum trader as a bystander 32316.7 Extending the model 32516.8 Short-term versus long-term investors 32616.9 The impact of the short-term investor 33016.10 The momentum risk premium 33216.11 The Apollo asset pricing model 33416.12 Momentum alpha 33516.13 Beta momentum 33916.14 Beta signal 34016.15 Momentum strategies 34116.16 Results 34716.17 Analysis of results 35316.18 Conclusions 355Chapter 17 Momentum, Value and Carry Commodity Factors for Multi-Asset Portfolios 35917.1 Introduction 35917.2 Methodology and key research questions 36117.3 Commodity factors – insights from the historical data 36217.4 Wealth accumulation strategies and rebalancing considerations 36617.5 Wealth decumulation strategies 37317.6 Long/short versus long only strategies 37517.7 Completion portfolios versus maximum Sharpe ratio portfolios 37917.8 Conclusions 38017.A.1 Momentum factor 38117.A.2 Carry factor 38117.A.3 Value factor 38217.A.4 From commodity factors to factor portfolios 38317.A.5 Factor construction 383Index 387

ANDREW GRANT is a Senior Lecturer in Finance at the University of Sydney. His main areas of expertise are behavioural finance, individual investor decision making, and betting markets. He has also been engaged with industry in the Asia-Pacific region. Andrew is a frequent speaker at conferences and seminars. STEPHEN SATCHELL is Fellow of Economics, Trinity College Cambridge, UK. He also works as an advisor to financial institutions and as a quantitative facilitator bringing clients together. Stephen lectures frequently at finance industry seminars and is on the committees for several leading quantitative research groups.

Select Guide Rating

• Contributors xviiIntroduction xxiiiChapter 1 Behavioural Finance and Momentum 11.1 Introduction 11.2 The failure of risk-based explanations 31.3 Behavioural models of momentum 31.4 Slow information diffusion 51.5 Patterns in information arrival 61.6 The 52-week high and capital gains overhang 81.7 Institutional trading and momentum profits 101.8 Sentiment and momentum 111.9 Discussion 12Chapter 2 A Taxonomy of Momentum Strategies 162.1 Introduction 162.2 Relative strength strategies 172.3 Time-series momentum strategies 182.4 Cross-sectional momentum strategies 202.5 Cross-asset momentum 27Chapter 3 Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations 303.1 Data Description 343.2 Methodology 393.3 Turnover Reduction 423.4 The Recent Underperformance of Time-series Momentum Strategies and the Effect of Pairwise Correlations 523.5 Trading Costs Implications 583.6 Concluding Remarks 63Chapter 4 Risk and Return of Momentum in Developed Equity Markets 684.1 Introduction 684.2 Definition of momentum 694.3 Simple factor portfolios 714.4 Multifactor structure 734.5 Pure factor portfolios 754.6 Empirical results: momentum performance 764.7 Empirical results: momentum risk 804.8 Diversification benefits 834.9 Summary 84Chapter 5 Momentum Across Asset Classes 865.1 Measuring momentum 875.2 Framework: equity momentum and corporate credit risk 875.3 Empirical studies: momentum and credit risk 895.4 Our research on equity momentum and bond returns 915.5 Geographically bound assets 925.6 Momentum in other illiquid assets 945.7 Cross-asset class effects of commodities 955.8 Momentum effects and taxable investors 955.9 Active management and momentum effects 965.10 Conclusions 98Chapter 6 Momentum in Momentum ETFs 1036.1 Introduction 1036.2 Why are momentum ETFs so popular? 1046.3 What is in a momentum ETF? 1126.4 Which factors drive active risk for momentum ETFs? 1146.5 From constrained to unconstrained strategies 1176.6 Conclusions 119Chapter 7 CTA Momentum 1207.1 Introduction 1207.2 Time-series momentum (TSM) 1217.3 Strategy return models 1277.4 Time-series momentum 1317.5 TSM meets CSM with two instruments 1337.6 Conclusions 1357.A.1 Appendix A: Correlation parameter restrictions 1367.A.2 Appendix B: Proofs of variances and covariance 138Chapter 8 Overreaction and Faint Praise – Short-Term Momentum in Contemporary Art 1418.1 Introduction 1418.2 Contemporary art market ecosystem 1448.3 ArtForecaster data 1458.4 Systematic forecasting strategies 1498.5 Conclusions 157Chapter 9 Volatility-Managed Momentum 1609.1 Introduction 1609.2 Data and momentum portfolio construction 1619.3 Volatility-managed momentum strategies 1629.4 Some potential practical issues 1669.5 The best volatility measure for momentum? 1709.6 Concluding remarks 172Chapter 10 Theoretical Analysis of the Fama-French Portfolios 17410.1 Introduction 17410.2 Strategies, notation and preliminaries 17910.3 Distribution of Fama-French factors 18210.4 Fama-French factors with sequential sorting 18910.5 Conclusion 19410.A.1 Proof of Lemma 1 19410.A.2 Proof of Theorem 3 19510.A.3 Proof of Theorem 4 196Chapter 11 Exploiting the Countercyclical Properties of Momentum and other Factor Premia – A Cross-Country Perspective 19911.1 Introduction 19911.2 Methodology 20011.3 Alternative investment strategies 20611.4 Quantifying the utility of risk premia strategies 21111.5 Summary and conclusions 215Chapter 12 Time-Series Variation in Factor Premia: The Influence of the Business Cycle 21812.1 Introduction 21812.2 Factors and factor rotation 21912.3 Factors and the business cycle 22012.4 Data and summary statistics 22212.5 Empirical results 22412.6 Conclusions 23412.A.1 Derivation of cash-flow news series 23412.A.2 US leading economic indicator and global risk appetite indicator 23612.A.3 Dynamic multifactor strategy: extension to other market segments and regions 236Chapter 13 Where Goes Momentum? 24313.1 Introduction 24313.2 Momentum strategies 24513.3 Data 24613.4 Method 24713.5 Results 25213.6 Risk-adjusted after-transaction costs performance of time-series and cross-sectional momentum strategies 26013.7 Conclusions 269Chapter 14 Time-Series Momentum in Credit: Machine Learning Approach 27314.1 Introduction 27314.2 The philosophy of artificial intelligence 27414.3 Vanilla time-series momentum 27714.4 Generalized linear models (GLM) – Lasso, Ridge and Elastic Net 28014.5 Determining optimal hyper-parameters via cross-validation 28314.6 Results: generalized linear models 28414.7 Random forests 28414.8 Neural networks 28914.9 Results and comments 29114.10 Conclusion 293Chapter 15 Momentum and Business Cycles 29715.1 Introduction 29715.2 Momentum, business cycles and realised market return 29815.3 Momentum and expected market risk premiums 30115.4 Momentum, overconfidence and sentiment 30915.5 Summary and conclusions 311Chapter 16 Momentum as a Fundamental Risk Factor 31416.1 Introduction 31416.2 Defining momentum as a strategy 31616.3 A new framework 31816.4 From realised returns to forecast returns 31916.5 Examining behaviour 31916.6 The momentum trader as a bystander 32316.7 Extending the model 32516.8 Short-term versus long-term investors 32616.9 The impact of the short-term investor 33016.10 The momentum risk premium 33216.11 The Apollo asset pricing model 33416.12 Momentum alpha 33516.13 Beta momentum 33916.14 Beta signal 34016.15 Momentum strategies 34116.16 Results 34716.17 Analysis of results 35316.18 Conclusions 355Chapter 17 Momentum, Value and Carry Commodity Factors for Multi-Asset Portfolios 35917.1 Introduction 35917.2 Methodology and key research questions 36117.3 Commodity factors – insights from the historical data 36217.4 Wealth accumulation strategies and rebalancing considerations 36617.5 Wealth decumulation strategies 37317.6 Long/short versus long only strategies 37517.7 Completion portfolios versus maximum Sharpe ratio portfolios 37917.8 Conclusions 38017.A.1 Momentum factor 38117.A.2 Carry factor 38117.A.3 Value factor 38217.A.4 From commodity factors to factor portfolios 38317.A.5 Factor construction 383Index 387