book

Performance Evaluation and Attribution of Security Portfolios

by Bernd R. Fischer, Russ Wermers

December 2012

Beginner

724 pages

22h 1m

English

Academic Press

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Cover image
Title page
Table of Contents
Introduction to the Series
Copyright
Preface
Section 1: Performance Evaluation
Chapter 1. An Introduction to Asset Pricing Models
1.1 Historical Asset Pricing Models1.2 The Beginning of Modern Asset Pricing Models1.3 Efficient Markets1.4 Studies That Attack the CAPM1.5 Does proving the CAPM wrong = Market inefficiency? Or, do efficient markets = the CAPM is correct?1.6 Small Capitalization and Value Stocks1.7 The Asset Pricing Models of Today1.8 Chapter-End ProblemsReferences
Chapter 2. Returns-Based Performance Evaluation Models
2.1 Introduction2.2 Goals, Guidelines, and Perils of Performance Evaluation2.3 Returns-Based Analysis2.4 Chapter-End ProblemsReferences
Chapter 3. Returns-Based Performance Measures
3.1 Introduction3.2 Luck vs. Skill3.3 The Ultimate Goal of Performance Measures3.4 Two Non-Regression Approaches3.5 Regression-Based Performance Measures3.6 Chapter-End ProblemsReferences

Chapter 4. Portfolio-Holdings Based Performance Evaluation
4.1 Introduction4.2 Unconditional Holdings-Based Performance Measurement4.3 Conditional Holdings-Based Performance Measurement4.4 Chapter-End ProblemsReferences
Chapter 5. Combining Portfolio-Holdings-Based and Returns-Based Performance Evaluation (and the “Return Gap”)
5.1 Introduction5.2 Performance-Decomposition Methodology5.3 Application to U.S. Domestic Equity Mutual Funds5.4 Empirical Results for U.S. Domestic Equity Mutual Funds5.5 Results for U.S. Domestic Corporate BOND Mutual Funds5.6 Appendix A5.7 Appendix B5.8 Chapter-End ProblemsReferences
Chapter 6. Performance Evaluation of Non-Normal Portfolios
6.1 Introduction6.2 Bootstrap Evaluation of Fund Alphas6.3 Data6.4 Results for U.S. Equity Funds6.5 Sensitivity Analysis6.6 Performance Persistence6.7 Chapter-End ProblemsReferences
Chapter 7. Fund Manager Selection Using Macroeconomic Information
7.1 Introduction7.2 A Dynamic Model of Managed Fund Returns7.3 Empirical Example: U.S. Domestic Equity Fund Data7.4 Empirical Example: Results for U.S. Domestic Equity Funds7.5 Chapter-End ProblemsAppendix A Description of Mutual Fund DatabaseAppendix B Investments when fund risk loadings and benchmark returns may be predictableAppendix C Investments when skills may be predictableReferences
Chapter 8. Multiple Fund Performance Evaluation: The False Discovery Rate Approach
8.1 Introduction8.2 The Impact of Luck on Managed Fund Performance8.3 An Empirical Example: U.S. Domestic Equity Mutual Funds8.4 An Empirical Example: Results for U.S. Domestic Equity Funds8.5 Chapter-End ProblemsReferences
Chapter 9. Active Management in Mostly Efficient Markets: A Survey of the Academic Literature
9.1 Introduction9.2 Some Caveats9.3 Does Active Management Add Value?9.4 Active Management and “Mostly Efficient Markets”9.5 Identifying Superior Active Managers (‘SAM’s)9.6 Conclusions9.7 Chapter-End ProblemsReferences
Section 2: Performance Analysis and Reporting
Chapter 10. Basic Performance Evaluation Models
10.1 Basis Formula for the Calculation of Returns10.2 Geometric Linkage and Scaling of Returns10.3 Internal Rate of Return10.4 Time-Weighted Return10.5 Comparison Between the Time-Weighted Return and the Internal Rate of Return10.6 Approximation Methods for the Computation of the Time-Weighted Return10.7 Active Return10.8 Continuously Compounded ReturnsAppendix A Equality between the Time-Weighted Return and the Internal Rate of ReturnAppendix B Solving Polynomial Equations for the Determination of Internal Rate of ReturnAppendix C Time-Weighted Return and the Unit Price Method
Chapter 11. Indices and the Construction of Benchmarks
11.1 Basic Concepts11.2 Equity Indices11.3 Bond Indices11.4 Money Market Indices11.5 Peer Group Comparisons and Fund Universes11.6 Benchmarks for Portfolios Investing in Multiple Asset Classes11.7 Chapter-End Problems
Chapter 12. Attribution Analysis for Equity Portfolios According to the Brinson Approach
12.1 Introduction to Attribution Analysis12.2 Single-Period Attribution Analysis According to the Brinson Approach12.3 Multi-period Attribution Analysis According to Brinson et al.12.4 Attribution Analysis in a Geometric Form12.5 Further Aspects of Attribution Analysis
Chapter 13. Attribution Analysis for Fixed Income Portfolios
Appendix: Duration Measures13.5 Exercises for Chapter-End Problems
Chapter 14. Analysis of Multi-Asset Class Portfolios and Hedge Funds
14.1 Basic Considerations14.2 Attribution Analysis on Two Levels14.3 Attribution Analysis on Three Levels14.4 Implementation in practice14.5 Risk-Adjusted Attribution Analysis Based On the Systematic Risk14.6 Risk-Adjusted Attribution Analysis Based on the Information Ratio14.7 Special Aspects in the Analysis of Hedge Funds14.8 Chapter-End Problems
Chapter 15. Attribution Analysis with Derivatives
15.1 Attribution Analysis with Derivative-Based Currency Management15.2 Treatment of Futures and Forwards15.3 Treatment of Options15.4 Swaps15.5 Chapter-End Problems
Chapter 16. Global Investment Performance Standards (GIPS)
16.1 Background16.2 Definition of Firm16.3 Creation of Composites16.4 Determination of Composite Return16.5 Further Disclosure Requirements for Composite Structure and Sample Presentations16.6 Maintenance of Composites16.7 Independent Verification of Compliance with the Standards16.8 Measurement of the Homogeneity of the Investment Process16.9 Presentation of Risks according to the GIPS16.10 Chapter-End Problems
Index

Content preview from Performance Evaluation and Attribution of Security Portfolios

Chapter 8 Multiple Fund Performance Evaluation: The False Discovery Rate Approach

Abstract

This chapter develops a simple technique that controls for “false discoveries”, or managed funds that exhibit significant alphas by luck alone. The approach presented precisely estimates the proportion of fund managers that are (1) unskilled, (2) zero-alpha, and (3) skilled, even with dependencies in cross-fund estimated alphas. In an application to U.S. domestic equity mutual funds, this approach shows that 75% of funds exhibit zero alphas (net of trading costs and other expenses), consistent with the Berk and Green (2004) equilibrium. Further, we find a significant proportion of skilled (positive alpha) funds prior to 1996, but almost none by 2006. Finally, ...

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Performance Evaluation and Attribution of Security Portfolios

by Bernd R. Fischer, Russ Wermers

Chapter 8

Multiple Fund Performance Evaluation: The False Discovery Rate Approach

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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