Portfolio Construction, Measurement, and Efficiency - Essays in Honor of Jack Treynor

Foreword

Jack Treynor: An Appreciation

References

Tribute to Jack Treynor

Contents

Author Bios

1 The Theory of Risk, Return, and Performance Measurement

1.1 Capital Market Equilibrium

1.2 The Barra Model: The Primary Institutional Risk Model

1.3 The Axioma Risk Model: Fundamental and Statistical Risk Models

1.4 Assessing Mutual Funds: The Treynor Index and Other Measurement Techniques

1.5 Conclusions and Summary

USE4 Descriptor Definitions

References

2 Portfolio Theory: Origins, Markowitz and CAPM Based Selection

2.1 Constrained Optimization

2.2 Portfolio Selection and CAPM

2.3 Conclusion

References

3 Market Timing

3.1 Return-Based Performance Measurement

3.1.1 ch3:Treynor1966

3.1.2 The Relation Between ?p,m and Rm,t

3.1.2.1 Quadratic Characteristic Line

3.1.2.2 Piecewise-Linear Characteristic Line

3.1.3 Derivative Strategies, Frequent Trading, Pseudo Timing, and Portfolio Performance

3.1.3.1 Derivative Strategies and Pseudo Timing

3.1.3.2 Frequent Trading and Pseudo Timing

3.1.4 A Contingent Claims Framework for Valuing the Skills of a Portfolio Manager

3.1.5 Timing and Selection with Return Predictability

3.2 Holdings-Based Performance Measurement

3.3 Summary

References

4 Returns, Risk, Portfolio Selection, and Evaluation

4.1 Introduction and Summary

4.2 Expected Returns Modeling and Stock Selection Models: Recent Evidence

4.3 Constructing Mean-Variance Efficient Portfolios

4.4 Evaluation of Portfolio Performance: Origins

4.5 Portfolio Simulation Results with the USER and GLER Models

4.6 Conclusions

References

5 Validating Return-Generating Models

5.1 The Design of the Experiment

5.1.1 The Validation Criterion

5.1.2 Conditional Expectations

5.2 The Experiment

5.2.1 Data

5.2.2 Factor Models

5.2.3 The Market Model

5.2.4 A January Seasonal

5.2.5 Biases and Inefficiencies

5.2.6 Macroeconomic Variables

5.3 Conclusions

References

6 Invisible Costs and Profitability

6.1 Introduction

6.2 Measures of Trading Cost

6.2.1 Proportional Costs

6.2.2 Nonproportional Costs

6.2.3 Estimation Issues

6.3 Measures of Performance Under Transaction Costs

6.4 Are Return Anomalies Robust to Trading Cost?

6.4.1 Return Anomalies

6.4.2 Performance Net of Transaction Costs

6.4.2.1 The Effect of Proportional Transaction Costs

6.4.2.2 The Effect of Nonproportional Transaction Cost

6.4.3 Optimized Portfolios

6.5 Liquidity Over Time

6.6 Conclusion

References

7 Mean-ETL Portfolio Construction in US Equity Market

7.1 Introduction

7.2 Fundamental Variables

7.3 Mean-ETL Portfolio Construction

7.3.1 Mean-ETL Framework

7.3.2 Scenario Generator

7.4 Portfolio Results and Analysis

7.4.1 Attribution Reports

7.4.2 Comparison

7.5 Summary

References

8 Portfolio Performance Assessment: Statistical Issues and Methods for Improvement

8.1 Introduction: Purposes and Overview

8.1.1 Performance Assessment Problems/Frameworks

8.1.2 Purposes

8.1.3 Chapter Organization

8.1.4 Overview of Some Key Results/Conclusions

8.2 The Problem of Assessing the Performance Potential of a Stock Return Forecast

8.2.1 Forecast Accuracy/Significance Versus Performance Potential

8.2.2 Key Specification Issue: Eliminating/Controlling for Correlation Distortion

8.2.3 Eliminating/Controlling for Systematic Tax Effects: Dividends Versus Gains

8.3 A Framework for Optimal Statistical Design

8.3.1 Key Design Decisions

8.3.2 The Number of Fractile Portfolios: Measurement Error Versus Power

8.4 Isolation Methodology Alternatives: Multivariate Regression Versus Control Matching

8.4.1 Treatment Response Studies

8.4.2 Intuition Motivation: Isolating Well Treatment Response to Drug Dosage Variation

8.4.3 Transforming a Rank-Ordered Cross Section into a Control-Matched Cross Section

8.5 A Power Optimizing Mathematical Assignment Program

8.5.1 Overview: Formulating the Mathematical Assignment Program

8.5.2 Notation Summary

8.5.3 The Power Optimizing Objective Function

8.5.4 Control Matching: The Equal Value Constraint for Each Control Variable

8.5.5 Security Usage and Short Sales: Technical Constraints

8.5.6 Synthesis of the Power Optimizing Reassignment Program

8.6 Forecast Model Overview

8.6.1 Selecting an Illustrative Forecast Model

8.6.2 Overview of the Illustrative Eight-Variable Forecast Model

8.6.3 Variable Weighting: A Step-By-Step Implementation Summary

8.7 Control Variables

8.7.1 Control Constraints

8.7.2 Risk Controls: ?, BP, and Size

8.7.3 Tax Controls: DP, EP, and FL

8.8 Using Control Variables to Isolate Performance Potential

8.8.1 Alternatives to the Full Sample, Relative Rank-Ordering Framework

8.8.2 Stepwise Imposition of Control Constraints: Procedure Overview

8.8.3 Study Sample and Time Frame

8.8.4 Key Efficiency/Power Design Decision: The Number of Fractile Portfolios

8.8.5 The Impact of Individual Risk Controls

8.8.6 CAPM Performance Assessments

8.8.7 The Impact of Size and BP Risk Controls

8.8.8 Imposition of Combinations of Risk and Tax Controls

8.8.9 Stepwise Imposition of Risk and Tax Controls: High-Minus-Low Differences

8.8.10 Estimates of the Dependence of the Return and SD Cross Sections on the Return Forecast

8.8.11 The Cross Sections of Realized Standard Deviations for Different Combinations of Controls

8.8.12 The Cross Section of Realized Skewness Coefficients

8.9 Further Research

8.10 Conclusions

Appendices

Appendix 8.1. Rank-ordered portfolio data: no controls

Appendix 8.2. Rank-ordered portfolio data: only a beta control

Appendix 8.3. Rank-ordered portfolio data: only a size control

Appendix 8.4. Rank-ordered portfolio data: only a BP control

Appendix 8.5. Rank-ordered portfolio data: risk controls only

Appendix 8.6. Rank-ordered portfolio data: tax controls only

Appendix 8.7. Rank-ordered portfolio data: risk and tax controls

References

9 The Duality of Value and Mean Reversion

9.1 Introduction

9.2 Short-Term Momentum and Long-Term Mean Reversion

9.3 Links Between Value and Mean Reversion Strategies

9.3.1 The Value Premium

9.3.2 Using Price Ratios to Predict Mean Reversion Effects

9.4 Conclusion

References

10 Performance of Earnings Yield and Momentum Factors in US and International Equity Markets

10.1 Introduction

10.2 Pure Factor Portfolios

10.3 Optimized Factor Portfolios

10.4 Unit-Exposure Optimized Portfolios

10.5 Fixed-Volatility Optimized Portfolios

10.6 Summary

References

11 Alpha Construction in a Consistent Investment Process

11.1 Introduction

11.2 Mean Variance Optimization

11.3 The Consistent Investment Process

11.3.1 Transforming Each Alpha Signal into Factor Mimicking Portfolios

11.3.2 Combining Factor Mimicking Portfolios into a Target Portfolio

11.3.3 Solving the Portfolio Construction Problem

11.4 Illustrative Example

11.5 Conclusions

References

A Technical Appendix

12 Empirical Analysis of Market Connectedness as a Risk Factor for Explaining Expected Stock Returns

12.1 Introduction

12.2 CAPM and the Multi-Factor Asset Pricing Model

12.2.1 Empirical Testing of CAPM

12.2.2 Multi-Factor Asset Return Model

12.3 Market-Connectedness and Systematic Risk in Asset Returns

12.3.1 Alternative Measures for Financial Market Connectedness

12.3.2 Market Connectedness Measure: Modularity

12.4 Modularity Index as a Systematic Risk Factor: Empirical Analysis

12.4.1 Clusters of Asset Returns over a Long Period

12.4.2 Modularity: A Systematic Risk Factor

12.5 Conclusion

References

13 The Behaviour of Sentiment-Induced Share Returns: Measurement When Fundamentals Are Observable

13.1 Related Literature

13.2 Hypotheses and Tests

13.3 Data

13.4 Sentiment and Returns

13.4.1 The Influence of Sentiment on the Hi-Lo Portfolio

13.4.2 Tests Using Fundamentals and Deviations from Fundamentals

13.4.3 The Effect of the Differencing Interval

13.4.4 Deep Fundamentals

13.5 Robustness Tests

13.5.1 Long-Only Portfolios

13.5.2 Nasadaq Stocks

13.5.3 The Effect of Lagged Market Returns

13.5.4 Lagged Fundamentals

13.6 Conclusions

Appendix: Principal Data Sources

References

14 Constructing Mean Variance Efficient Frontiers Using Foreign Large Blend Mutual Funds

14.1 Introduction

14.2 Single-Period and Ex Post Mean Variance Efficient Frontier

14.3 Risk Models and Expected Return Models

14.3.1 Raw Return

14.3.2 Risk-Adjusted Return

14.3.3 Mutual Fund Characteristics

14.4 Data and Universe

14.4.1 Transaction Cost, Turnover, and Upper Bound

14.5 Ex Post Efficient Frontiers

14.5.1 Conclusion

References

15 Fundamental Versus Traditional Indexation for International Mutual Funds: Evaluating DFA, WisdomTree, and RAFI PowerShares

15.1 Style Analysis

15.2 How Do We Create the Clone Portfolio?

15.3 Why Use a Clone Portfolio and Style Analysis?

15.4 Why Use Continuous Compounding and Geometric Average Return?

15.5 Why Use Equally Weighted Portfolios and Risk-Averse Portfolios?

15.6 Why Do Some of Our Portfolios Allow Short Selling?

15.7 Data

15.8 The Exhibits

15.9 DFA Individual Funds

15.10 RAFI Individual Funds

15.11 WisdomTree Individual Funds

15.12 The Individual Fundamental Index Funds

15.13 Fundamental Index Portfolios

15.14 DFA Aggregates

15.15 RAFI Aggregates

15.16 WisdomTree Aggregates

15.17 Does the First Half Period ? Predict the Second Half Period ??

15.18 Are the ?S Explained by Different Sector Returns?

15.19 Conclusion

References

16 Forecasting Implied Volatilities for Options on Index Futures: Time-Series and Cross-Sectional Analysis versus Constant Elasticity of Variance (CEV) Model

16.1 Introduction

16.2 Literature Review

16.2.1 Black-Scholes-Merton Option Pricing Model (BSM) and CEV Model

16.2.2 Time-Varying Volatility and Time-Series Analysis

16.3 Data and Methodology

16.3.1 Data

16.3.2 Methodology

16.3.2.1 Estimating BSM IV

16.3.2.2 Forecasting IV by Cross-Sectional and Time-Series Analysis

16.3.2.3 Forecasting IV by CEV Model

16.4 Empirical Analysis

16.4.1 Distributional Qualities of IV time series

16.4.2 Time-Series and Cross-Sectional Analysis for IV Series

16.4.3 Ex-Post Test for Forecastability of Time-Series and Cross-Sectional Regression Models

16.4.4 Structural Parameter Estimation and Performance of CEV Model

16.5 Conclusion

References

17 The Swiss Black Swan Unpegging Bad Scenario: The Losers and the Winners

17.1 The Swiss Franc Peg

17.2 Why Did the SNB Start the Peg and Why Did They Eliminate It?

17.3 How Does Quantitative Easing Work and What Are It Is Costs and Benefits?

17.4 The Currency Moves

17.5 Review of How to Lose Money Trading Derivatives

17.6 The Folly of the Misleading Value at Risk Measure

17.7 Losers and How It Affected Them

17.8 Banks and Hedge Funds

17.9 What Types of Traders Lost Money

17.10 Mortgage Losses

17.11 Final Remarks

References

18 Leveling the Playing Field

18.1 Methodology and Data

18.2 Results

18.3 Conclusion

References

19 Against the `Wisdom of Crowds': The Investment Performance of Contrarian Funds

19.1 Introduction

19.2 Identifying Contrarian Funds

19.3 Distribution and Characteristics of Contrarian/Herding Funds

19.4 Performance of Contrarian and Herding Funds

19.5 What Does It Take to Be A Successful Contrarian? Parsing Through Fund Trades

19.6 Extracting Stock Selection Information From Contrarian Fund Holdings

19.7 Conclusions

References