Professional Investment Portfolio Management : Boosting Performance with Machine-Made Portfolios and Stock Market Evidence

Professional investment portfolio management is increasingly utilizing sophisticated statistical and computer techniques to better control risks and improve performance. This book provides new quantitative tools and technology for securities professionals to help boost the performance of their investment portfolios offered to clients. Unlike other books in this area, the authors utilize revolutionary asset pricing methods and models to analyze data for U.S. stocks and show how to apply them to the problem of creating highly diversified portfolios that are efficient in terms of returns per unit risk. Preface Acknowledgements Contents About the Authors List of Figures List of Tables Part I Introduction 1 Portfolio Theory and Practice 1.1 The Two-Step Portfolio Process 1.2 Real World Portfolio Analyses 1.3 New Investment Parabola Insights 1.4 A New Approach to Finding Efficient Portfolios 1.5 Summary Appendix: Optimal Weights for Many Assets References Part II Previous Asset Pricing Models 2 General Equilibrium Asset Pricing Models 2.1 The Present Value Formula 2.2 The CAPM 2.2.1 Existence of a Riskless Asset 2.2.2 Capital Market Line 2.2.3 Deriving the CAPM 2.2.4 Security Market Line 2.3 The Market Model 2.3.1 Early CAPM Tests 2.3.2 Investment Portfolio Implications 2.4 The Zero-Beta CAPM 2.4.1 Investment Portfolio Implications 2.5 Alternative CAPM Forms 2.6 Road Map of General Equilibrium Models 2.7 Summary References 3 Multifactor Asset Pricing Models 3.1 Arbitrage Pricing Theory 3.2 Fama and French Three-Factor Model 3.3 The Factor Zoo and Multifactor Models 3.3.1 Carhart Four-Factor Model 3.3.2 Hou, Xue, and Zhang Four-Factor Model 3.3.3 Stambaugh and Yuan Four-Factor Mispricing Model 3.3.4 Fama and French Five-Factor Model 3.3.5 Fama and French Six-Factor Model 3.3.6 Machine Learning Models 3.4 Roadmap of Multifactor Models 3.4.1 Investment Portfolio Implications 3.5 Summary References Part III The ZCAPM 4 A New Asset Pricing Model: The ZCAPM 4.1 Theoretical ZCAPM 4.1.1 Markowitz Investment Parabola 4.1.2 Derivation of the ZCAPM Equilibrium Relation 4.2 Graphical Depictions of the ZCAPM 4.2.1 Beta Risk and Zeta Risk in the ZCAPM 4.2.2 Architecture of the Investment Parabola and the ZCAPM 4.3 Summary References 5 The Empirical ZCAPM 5.1 Specification of the Empirical ZCAPM 5.2 Cross-Sectional Test Methodology 5.3 Cross-Sectional Test Results 5.4 Portfolio Implications of the ZCAPM 5.5 Recognition of the Empirical ZCAPM 5.6 Summary References Part IV Portfolio Performance 6 Portfolio Performance Measures 6.1 Return Metrics 6.2 Performance Comparison 6.2.1 Sharpe Ratio 6.2.2 Manipulation-Proof Performance Measure 6.2.3 Treynor Measure 6.2.4 Jensen’s Alpha 6.2.5 Market Timing 6.2.6 Value at Risk 6.2.7 Drawdown 6.3 Summary References Part V Building Stock Portfolios with the ZCAPM 7 Building the Global Minimum Variance Portfolio G 7.1 Previous Literature 7.2 Global Minimum Variance Portfolio 7.2.1 Mechanics of Building Portfolio G 7.2.2 Second Stage Portfolios 7.3 Empirical Results for the G Portfolio 7.3.1 Overall Sample G Results 7.3.2 Top 3,000 Sample G Results 7.4 Summary References 8 Net Long Portfolio Performance Analyses 8.1 Background Discussion 8.2 Empirical Methods 8.2.1 Review of the ZCAPM 8.3 Building Net Long Portfolios Using the ZCAPM 8.4 Empirical Results 8.4.1 Net Long Portfolios in the Analysis Period 8.4.2 Net Long Portfolios for Subperiods 8.5 Summary Appendix: Long-Short Portfolios Based on Zeta Risk Levels References 9 Net Long Portfolio Risk Analyses 9.1 GRS Risk Metrics 9.2 Value at Risk Metrics 9.3 Drawdown Risk Metrics 9.4 Summary References 10 Long Only Efficient Portfolios 10.1 Empirical Methods 10.1.1 Building Long Only Portfolios 10.2 Empirical Results 10.2.1 Long Only Zeta Risk Portfolios 10.2.2 Long Only Beta Risk Portfolios 10.3 Summary References 11 The Beta-Zeta Risk Architecture of the Mean-Variance Parabola 11.1 Empirical Methods 11.1.1 Building Long Only Portfolios 11.2 Empirical Results 11.2.1 Zeta-Beta Risk Portfolios 11.2.2 Beta-Zeta Risk Portfolios 11.2.3 Subperiod Results for Beta Risk and Zeta Risk Portfolios 11.2.4 Results After Dropping High Idiosyncratic Risk Stocks 11.3 Summary References 12 Mutual Fund Portfolios 12.1 Empirical Methods 12.1.1 Building Mutual Fund Portfolios 12.2 Empirical Results 12.2.1 Mutual Fund Portfolios Sorted on Zeta Risk 12.2.2 Mutual Fund Portfolios Sorted on Beta Risk 12.3 Summary References Part VI Conclusion 13 The Future of Investment Practice, Artificial Intelligence, and Machine Learning 13.1 Asset Pricing Discussion 13.2 The ZCAPM and Investment Practice 13.3 Implications of Artificial Intelligence and Machine Learning References Index