Portfolio Construction, Measurement, and Efficiency (eBook)

Essays in Honor of Jack Treynor

Jr. Guerard John B. (Herausgeber)

eBook Download: PDF

2016 | 1st ed. 2017
XXXIII, 453 Seiten
Springer International Publishing (Verlag)
978-3-319-33976-4 (ISBN)

Infos

This volume, inspired by and dedicated to the work of pioneering investment analyst, Jack Treynor, addresses the issues of portfolio risk and return and how investment portfolios are measured.  In a career spanning over fifty years, the primary questions addressed by Jack Treynor were: Is there an observable risk-return trade-off? How can stock selection models be integrated with risk models to enhance client returns? Do managed portfolios earn positive, and statistically significant, excess returns and can mutual fund managers time the market?

Since the publication of a pair of seminal Harvard Business Review articles in the mid-1960's, Jack Treynor has developed thinking that has greatly influenced security selection, portfolio construction and measurement, and market efficiency.  Key publications addressed such topics as the Capital Asset Pricing Model and stock selection modeling and integration with risk models.  Treynor also served as editor of the Financial Analysts Journal, through which he wrote many columns across a wide spectrum of topics.

This volume showcases original essays by leading researchers and practitioners exploring the topics that have interested Treynor while applying the most current methodologies. Such topics include the origins of portfolio theory, market timing, and portfolio construction in equity markets.  The result not only reinforces Treynor's lasting contributions to the field but suggests new areas for research and analysis.

John B. Guerard, Jr., Ph.D. is Director of Quantitative Research at McKinley Capital Management, in Anchorage, Alaska.  He earned his AB in Economics from Duke University, MA in Economics from the University of Virginia, MSIM from the Georgia Institute of Technology, and Ph.D. in Finance from the University of Texas, Austin. John taught at the McIntire School of Commerce, the University of Virginia, Lehigh University, and Rutgers University. John taught as an adjunct faculty member at the International University of Monaco and the University of Pennsylvania. He worked with the DAIS Group at Drexel, Burnham, Lambert, Daiwa Securities Trust Company, Vantage Global Advisors, and served on the Virtual Research team at GlobeFlex Capital.  John co-managed a Japanese equity portfolio with Harry Markowitz at Daiwa Securities Trust Company. While serving as Director of Quantitative Research at Vantage Global Advisors (formerly MPT Associates), Mr. Guerard was awarded the first Moskowitz Prize for research in socially responsible investing.  Mr. Guerard has published several monographs, including Corporate Financial Policy and R&D Management (Wiley, 2006, second edition), Quantitative Corporate Finance (Springer, 2007, with Eli Schwartz), The Handbook of Portfolio Construction: Contemporary Applications of Markowitz Techniques (Springer, 2010), and Introduction to Financial Forecasting in Investment Analysis (Springer, 2013). John serves an Associate Editor of the Journal of Investing and The International Journal of Forecasting. Mr. Guerard has published research in The International Journal of Forecasting, Management Science, the Journal of Forecasting, Journal of Investing, Research in Finance, the IBM Journal of Research and Development, Research Policy, and the Journal of the Operational Research Society.

Inhalt

Foreword 6
Jack Treynor: An Appreciation 10
References 15
Tribute to Jack Treynor 16
Contents 18
Author Bios 20
1 The Theory of Risk, Return, and Performance Measurement 35
1.1 Capital Market Equilibrium 39
1.2 The Barra Model: The Primary Institutional Risk Model 42
1.3 The Axioma Risk Model: Fundamental and Statistical Risk Models 54
1.4 Assessing Mutual Funds: The Treynor Index and Other Measurement Techniques 62
1.5 Conclusions and Summary 68
USE4 Descriptor Definitions 69
References 69
2 Portfolio Theory: Origins, Markowitz and CAPM Based Selection 73
2.1 Constrained Optimization 74
2.2 Portfolio Selection and CAPM 76
2.3 Conclusion 81
References 81
3 Market Timing 83
3.1 Return-Based Performance Measurement 86
3.1.1 ch3:Treynor1966 89
3.1.2 The Relation Between ?p,m and Rm,t 90
3.1.2.1 Quadratic Characteristic Line 90
3.1.2.2 Piecewise-Linear Characteristic Line 93
3.1.3 Derivative Strategies, Frequent Trading, Pseudo Timing, and Portfolio Performance 94
3.1.3.1 Derivative Strategies and Pseudo Timing 94
3.1.3.2 Frequent Trading and Pseudo Timing 95
3.1.4 A Contingent Claims Framework for Valuing the Skills of a Portfolio Manager 97
3.1.5 Timing and Selection with Return Predictability 98
3.2 Holdings-Based Performance Measurement 100
3.3 Summary 101
References 103
4 Returns, Risk, Portfolio Selection, and Evaluation 106
4.1 Introduction and Summary 106
4.2 Expected Returns Modeling and Stock Selection Models: Recent Evidence 107
4.3 Constructing Mean-Variance Efficient Portfolios 121
4.4 Evaluation of Portfolio Performance: Origins 127
4.5 Portfolio Simulation Results with the USER and GLER Models 130
4.6 Conclusions 134
References 140
5 Validating Return-Generating Models 144
5.1 The Design of the Experiment 146
5.1.1 The Validation Criterion 146
5.1.2 Conditional Expectations 147
5.2 The Experiment 149
5.2.1 Data 149
5.2.2 Factor Models 150
5.2.3 The Market Model 156
5.2.4 A January Seasonal 157
5.2.5 Biases and Inefficiencies 158
5.2.6 Macroeconomic Variables 163
5.3 Conclusions 164
References 166
6 Invisible Costs and Profitability 168
6.1 Introduction 168
6.2 Measures of Trading Cost 170
6.2.1 Proportional Costs 171
6.2.2 Nonproportional Costs 172
6.2.3 Estimation Issues 173
6.3 Measures of Performance Under Transaction Costs 174
6.4 Are Return Anomalies Robust to Trading Cost? 175
6.4.1 Return Anomalies 175
6.4.2 Performance Net of Transaction Costs 176
6.4.2.1 The Effect of Proportional Transaction Costs 176
6.4.2.2 The Effect of Nonproportional Transaction Cost 177
6.4.3 Optimized Portfolios 179
6.5 Liquidity Over Time 181
6.6 Conclusion 183
References 184
7 Mean-ETL Portfolio Construction in US Equity Market 187
7.1 Introduction 187
7.2 Fundamental Variables 188
7.3 Mean-ETL Portfolio Construction 189
7.3.1 Mean-ETL Framework 190
7.3.2 Scenario Generator 191
7.4 Portfolio Results and Analysis 192
7.4.1 Attribution Reports 192
7.4.2 Comparison 195
7.5 Summary 198
References 199
8 Portfolio Performance Assessment: Statistical Issues and Methods for Improvement 201
8.1 Introduction: Purposes and Overview 201
8.1.1 Performance Assessment Problems/Frameworks 201
8.1.2 Purposes 202
8.1.3 Chapter Organization 204
8.1.4 Overview of Some Key Results/Conclusions 205
8.2 The Problem of Assessing the Performance Potential of a Stock Return Forecast 205
8.2.1 Forecast Accuracy/Significance Versus Performance Potential 205
8.2.2 Key Specification Issue: Eliminating/Controlling for Correlation Distortion 206
8.2.3 Eliminating/Controlling for Systematic Tax Effects: Dividends Versus Gains 207
8.3 A Framework for Optimal Statistical Design 207
8.3.1 Key Design Decisions 207
8.3.2 The Number of Fractile Portfolios: Measurement Error Versus Power 208
8.4 Isolation Methodology Alternatives: Multivariate Regression Versus Control Matching 209
8.4.1 Treatment Response Studies 209
8.4.2 Intuition Motivation: Isolating Well Treatment Response to Drug Dosage Variation 210
8.4.3 Transforming a Rank-Ordered Cross Section into a Control-Matched Cross Section 212
8.5 A Power Optimizing Mathematical Assignment Program 215
8.5.1 Overview: Formulating the Mathematical Assignment Program 215
8.5.2 Notation Summary 216
8.5.3 The Power Optimizing Objective Function 217
8.5.4 Control Matching: The Equal Value Constraint for Each Control Variable 218
8.5.5 Security Usage and Short Sales: Technical Constraints 218
8.5.6 Synthesis of the Power Optimizing Reassignment Program 219
8.6 Forecast Model Overview 220
8.6.1 Selecting an Illustrative Forecast Model 220
8.6.2 Overview of the Illustrative Eight-Variable Forecast Model 221
8.6.3 Variable Weighting: A Step-By-Step Implementation Summary 222
8.7 Control Variables 224
8.7.1 Control Constraints 224
8.7.2 Risk Controls: ?, BP, and Size 224
8.7.3 Tax Controls: DP, EP, and FL 225
8.8 Using Control Variables to Isolate Performance Potential 227
8.8.1 Alternatives to the Full Sample, Relative Rank-Ordering Framework 227
8.8.2 Stepwise Imposition of Control Constraints: Procedure Overview 230
8.8.3 Study Sample and Time Frame 230
8.8.4 Key Efficiency/Power Design Decision: The Number of Fractile Portfolios 232
8.8.5 The Impact of Individual Risk Controls 232
8.8.6 CAPM Performance Assessments 234
8.8.7 The Impact of Size and BP Risk Controls 236
8.8.8 Imposition of Combinations of Risk and Tax Controls 237
8.8.9 Stepwise Imposition of Risk and Tax Controls: High-Minus-Low Differences 241
8.8.10 Estimates of the Dependence of the Return and SD Cross Sections on the Return Forecast 243
8.8.11 The Cross Sections of Realized Standard Deviations for Different Combinations of Controls 246
8.8.12 The Cross Section of Realized Skewness Coefficients 247
8.9 Further Research 248
8.10 Conclusions 250
Appendices 252
Appendix 8.1. Rank-ordered portfolio data: no controls 252
Appendix 8.2. Rank-ordered portfolio data: only a beta control 253
Appendix 8.3. Rank-ordered portfolio data: only a size control 254
Appendix 8.4. Rank-ordered portfolio data: only a BP control 255
Appendix 8.5. Rank-ordered portfolio data: risk controls only 256
Appendix 8.6. Rank-ordered portfolio data: tax controls only 257
Appendix 8.7. Rank-ordered portfolio data: risk and tax controls 258
References 258
9 The Duality of Value and Mean Reversion 261
9.1 Introduction 261
9.2 Short-Term Momentum and Long-Term Mean Reversion 263
9.3 Links Between Value and Mean Reversion Strategies 264
9.3.1 The Value Premium 265
9.3.2 Using Price Ratios to Predict Mean Reversion Effects 266
9.4 Conclusion 269
References 270
10 Performance of Earnings Yield and Momentum Factors in US and International Equity Markets 271
10.1 Introduction 271
10.2 Pure Factor Portfolios 272
10.3 Optimized Factor Portfolios 278
10.4 Unit-Exposure Optimized Portfolios 279
10.5 Fixed-Volatility Optimized Portfolios 283
10.6 Summary 286
References 288
11 Alpha Construction in a Consistent Investment Process 289
11.1 Introduction 289
11.2 Mean Variance Optimization 291
11.3 The Consistent Investment Process 292
11.3.1 Transforming Each Alpha Signal into Factor Mimicking Portfolios 293
11.3.2 Combining Factor Mimicking Portfolios into a Target Portfolio 295
11.3.3 Solving the Portfolio Construction Problem 296
11.4 Illustrative Example 297
11.5 Conclusions 304
References 304
A Technical Appendix 305
12 Empirical Analysis of Market Connectedness as a Risk Factor for Explaining Expected Stock Returns 307
12.1 Introduction 307
12.2 CAPM and the Multi-Factor Asset Pricing Model 308
12.2.1 Empirical Testing of CAPM 309
12.2.2 Multi-Factor Asset Return Model 310
12.3 Market-Connectedness and Systematic Risk in Asset Returns 310
12.3.1 Alternative Measures for Financial Market Connectedness 311
12.3.2 Market Connectedness Measure: Modularity 312
12.4 Modularity Index as a Systematic Risk Factor: Empirical Analysis 313
12.4.1 Clusters of Asset Returns over a Long Period 313
12.4.2 Modularity: A Systematic Risk Factor 317
12.5 Conclusion 319
References 320
13 The Behaviour of Sentiment-Induced Share Returns: Measurement When Fundamentals Are Observable 322
13.1 Related Literature 323
13.2 Hypotheses and Tests 324
13.3 Data 326
13.4 Sentiment and Returns 329
13.4.1 The Influence of Sentiment on the Hi-Lo Portfolio 329
13.4.2 Tests Using Fundamentals and Deviations from Fundamentals 333
13.4.3 The Effect of the Differencing Interval 336
13.4.4 Deep Fundamentals 336
13.5 Robustness Tests 338
13.5.1 Long-Only Portfolios 338
13.5.2 Nasadaq Stocks 340
13.5.3 The Effect of Lagged Market Returns 340
13.5.4 Lagged Fundamentals 340
13.6 Conclusions 341
Appendix: Principal Data Sources 342
References 343
14 Constructing Mean Variance Efficient Frontiers Using Foreign Large Blend Mutual Funds 345
14.1 Introduction 345
14.2 Single-Period and Ex Post Mean Variance Efficient Frontier 346
14.3 Risk Models and Expected Return Models 347
14.3.1 Raw Return 348
14.3.2 Risk-Adjusted Return 349
14.3.3 Mutual Fund Characteristics 349
14.4 Data and Universe 350
14.4.1 Transaction Cost, Turnover, and Upper Bound 352
14.5 Ex Post Efficient Frontiers 352
14.5.1 Conclusion 355
References 358
15 Fundamental Versus Traditional Indexation for International Mutual Funds: Evaluating DFA, WisdomTree, and RAFI PowerShares 360
15.1 Style Analysis 361
15.2 How Do We Create the Clone Portfolio? 362
15.3 Why Use a Clone Portfolio and Style Analysis? 362
15.4 Why Use Continuous Compounding and Geometric Average Return? 363
15.5 Why Use Equally Weighted Portfolios and Risk-Averse Portfolios? 363
15.6 Why Do Some of Our Portfolios Allow Short Selling? 363
15.7 Data 364
15.8 The Exhibits 364
15.9 DFA Individual Funds 375
15.10 RAFI Individual Funds 376
15.11 WisdomTree Individual Funds 376
15.12 The Individual Fundamental Index Funds 376
15.13 Fundamental Index Portfolios 377
15.14 DFA Aggregates 377
15.15 RAFI Aggregates 379
15.16 WisdomTree Aggregates 379
15.17 Does the First Half Period ? Predict the Second Half Period ?? 379
15.18 Are the ?S Explained by Different Sector Returns? 380
15.19 Conclusion 381
References 382
16 Forecasting Implied Volatilities for Options on Index Futures: Time-Series and Cross-Sectional Analysis versus Constant Elasticity of Variance (CEV) Model 383
16.1 Introduction 383
16.2 Literature Review 385
16.2.1 Black-Scholes-Merton Option Pricing Model (BSM) and CEV Model 385
16.2.2 Time-Varying Volatility and Time-Series Analysis 389
16.3 Data and Methodology 390
16.3.1 Data 390
16.3.2 Methodology 391
16.3.2.1 Estimating BSM IV 391
16.3.2.2 Forecasting IV by Cross-Sectional and Time-Series Analysis 392
16.3.2.3 Forecasting IV by CEV Model 395
16.4 Empirical Analysis 396
16.4.1 Distributional Qualities of IV time series 397
16.4.2 Time-Series and Cross-Sectional Analysis for IV Series 398
16.4.3 Ex-Post Test for Forecastability of Time-Series and Cross-Sectional Regression Models 401
16.4.4 Structural Parameter Estimation and Performance of CEV Model 405
16.5 Conclusion 412
References 413
17 The Swiss Black Swan Unpegging Bad Scenario: The Losers and the Winners 416
17.1 The Swiss Franc Peg 416
17.2 Why Did the SNB Start the Peg and Why Did They Eliminate It? 419
17.3 How Does Quantitative Easing Work and What Are It Is Costs and Benefits? 420
17.4 The Currency Moves 422
17.5 Review of How to Lose Money Trading Derivatives 423
17.6 The Folly of the Misleading Value at Risk Measure 433
17.7 Losers and How It Affected Them 439
17.8 Banks and Hedge Funds 440
17.9 What Types of Traders Lost Money 442
17.10 Mortgage Losses 443
17.11 Final Remarks 443
References 445
18 Leveling the Playing Field 448
18.1 Methodology and Data 450
18.2 Results 452
18.3 Conclusion 455
References 456
19 Against the `Wisdom of Crowds': The Investment Performance of Contrarian Funds 458
19.1 Introduction 458
19.2 Identifying Contrarian Funds 460
19.3 Distribution and Characteristics of Contrarian/Herding Funds 462
19.4 Performance of Contrarian and Herding Funds 467
19.5 What Does It Take to Be A Successful Contrarian? Parsing Through Fund Trades 470
19.6 Extracting Stock Selection Information From Contrarian Fund Holdings 475
19.7 Conclusions 479
References 479

Produkt-Details

• Erscheint lt. Verlag: 23.9.2016
• Zusatzinfo: XXXIII, 453 p. 56 illus., 49 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik
• Themenwelt: Technik
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management
• Themenwelt: Wirtschaft ► Volkswirtschaftslehre
• Schlagworte: Efficient Markets • Investment portfolio • Jack Treynor • market timing • Performance Measurement • Portfolio Construction • Quantitative Finance • Risk Management
• ISBN-10: 3-319-33976-1 / 3319339761
• ISBN-13: 978-3-319-33976-4 / 9783319339764