The idea of comparing the performance of different risky investments, for example investment funds, on a quantitative basis dates back to the beginnings of the asset management industry and has been an important field of research in finance since then. Performance measures serve as valuable quantitative evidence for the portfolio manager's performance as well as for the evaluation of investment decisions ex post. Based on the idea of the capital asset pricing model proposed by Treynor (1961), Sharpe (1964), and Lintner (1965), Treynor (1965) developed the first quantitative performance measure intended to rate mutual funds, the Treynor Ratio. Since then, a large number of performance measures with very different characteristics have been developed, for example by Sharpe (1966), Jensen (1968), Treynor & Black (1973), Sortino & Price (1994), and Israelsen (2005). In addition to their power of rating investments ex post, their ability to predict future performance has been thoroughly analyzed by Grinblatt & Titman (1992), Brown & Goetzmann (1995), Carhart (1997), and others. Besides academia, the driving force behind the development of more sophisticated performance measures has always been the investors. This is understandable, as "the truly poor managers are afraid, the unlucky managers will be unjustly condemned, and the new managers have no track record. Only the skilled (or lucky) managers are enthusiastic" (Grinold & Kahn, 2000, p. 478).
By combining and applying the results of previous research to a new sample of nearly 10,000 mutual funds that invest in different countries and asset classes, this thesis clarifies its central research question: Is the Information Ratio a useful and reliable performance measure? In order to answer this central question, it has been split up into the following sub-parts: What are the characteristics of a useful and reliable performance measure? What actually is "good" performance? Is the "good" performance a result of luck or of skilled decisions and does it persist over time? How does the Information Ratio compare to other performance measures, and what are its strengths and weaknesses? This empirical study aims at answering all of these questions and provides a framework for performance evaluation by use of the Information Ratio.
Table of Contents
- 1 Introduction
- 1.1 Motivation and Objective
- 1.2 Course of the Investigation
- 2 Theoretical Overview
- 2.1 Methods of Fund Performance Measurement
- 2.1.1 Characteristics of a Reliable Performance Measure
- 2.1.2 The Treynor Ratio
- 2.1.3 The Sharpe Ratio
- 2.1.4 Jensen's Alpha
- 2.1.5 The Sortino Ratio
- 2.1.6 The M² Measure
- 2.1.7 The Omega Measure
- 2.2 The Information Ratio
- 2.3 Sources of Active Returns: How to Beat the Benchmark
- 2.4 Agency Problems Related to Performance Measures
- 3 Data Description and Sources
- 3.1 Mutual Fund Selection
- 3.2 Benchmark Selection
- 3.3 Descriptive Statistics
- 4 Empirical Study on Selected Performance Measures
- 4.1 Is the Information Ratio a Reliable Measure of Performance?
- 4.2 The Information Ratio Versus Other Measures
- 4.3 The Art of Selecting the Benchmark
- 4.4 Does Data Frequency Matter?
- 4.5 Other Influences on Performance Measures
- 4.6 Performance Persistence: Outperformance by Luck or Skill?
- 4.7 Summary of Empirical Results
- 5 A Practical View on Performance Measurement
Objectives and Key Themes
This thesis aims to evaluate the usefulness of the Information Ratio as a performance measure for portfolio managers. It investigates the reliability and effectiveness of the Information Ratio in comparison to other established performance measures. The study explores the impact of various factors, such as benchmark selection and data frequency, on the Information Ratio's results.
- Reliability and validity of the Information Ratio as a performance measure.
- Comparison of the Information Ratio with other performance metrics (Sharpe Ratio, Treynor Ratio, etc.).
- Influence of benchmark selection on performance measurement.
- Impact of data frequency on the Information Ratio.
- Analysis of performance persistence and the role of skill versus luck.
Chapter Summaries
1 Introduction: This chapter introduces the thesis's motivation and objective, focusing on the need for a robust performance measure for portfolio managers. It highlights the limitations of existing metrics and sets the stage for the investigation of the Information Ratio's suitability. The chapter outlines the structure and methodology of the study.
2 Theoretical Overview: This chapter provides a comprehensive overview of various fund performance measurement methods. It discusses the characteristics of a reliable performance measure and delves into a detailed explanation of the Information Ratio, contrasting it with other established measures like the Sharpe Ratio, Treynor Ratio, Jensen's Alpha, Sortino Ratio, M², and Omega Measure. The chapter also explores the sources of active returns and discusses agency problems related to performance measurement, setting the theoretical groundwork for the empirical analysis.
3 Data Description and Sources: This chapter details the data selection process, including the criteria for mutual fund selection and benchmark selection. It provides descriptive statistics of the fund returns used in the empirical study, ensuring transparency and enabling a thorough understanding of the dataset's characteristics and limitations. The chapter justifies the choices made and lays the foundation for the empirical analysis presented in the subsequent chapters.
4 Empirical Study on Selected Performance Measures: This chapter presents the core empirical findings of the thesis. It investigates the reliability of the Information Ratio as a performance measure, comparing its results with other measures across different fund categories. The chapter explores the impact of benchmark selection, data frequency, and other influencing factors on performance measurements. It analyzes performance persistence, exploring the distinction between skill-based outperformance and luck. This chapter forms the heart of the thesis, providing evidence to support or refute the usefulness of the Information Ratio.
5 A Practical View on Performance Measurement: This chapter offers a practical perspective on performance measurement, drawing upon the empirical findings presented earlier. It likely provides recommendations and best practices for practitioners in the field, highlighting the strengths and weaknesses of different methods in real-world applications. This chapter bridges the gap between theoretical concepts and practical implications.
Keywords
Information Ratio, Portfolio Manager Performance, Fund Performance Measurement, Sharpe Ratio, Treynor Ratio, Jensen's Alpha, Benchmark Selection, Data Frequency, Performance Persistence, Active Returns, Agency Problems.
Frequently Asked Questions: A Comprehensive Language Preview of Portfolio Performance Measurement
What is the main topic of this thesis?
This thesis evaluates the Information Ratio as a performance measure for portfolio managers. It compares its reliability and effectiveness to other established measures, considering factors like benchmark selection and data frequency.
What are the key objectives of the study?
The study aims to determine the reliability and validity of the Information Ratio, compare it to other metrics (Sharpe Ratio, Treynor Ratio, etc.), analyze the influence of benchmark selection and data frequency, and investigate performance persistence (skill vs. luck).
What performance measures are discussed in the thesis?
The thesis extensively covers the Information Ratio, comparing it to the Sharpe Ratio, Treynor Ratio, Jensen's Alpha, Sortino Ratio, M² Measure, and Omega Measure. It analyzes the characteristics of a reliable performance measure in general.
How does the thesis analyze the Information Ratio?
The thesis conducts an empirical study analyzing the Information Ratio's reliability, comparing it to other measures across different funds. It examines the impact of benchmark selection, data frequency, and other factors, and explores whether outperformance is due to skill or luck.
What data was used in the empirical study?
The study uses data from selected mutual funds and benchmarks. Chapter 3 details the selection criteria and provides descriptive statistics of the fund returns, ensuring transparency about the dataset’s characteristics and limitations.
What are the key findings of the empirical study (Chapter 4)?
Chapter 4 presents the core empirical findings, examining the reliability of the Information Ratio, its comparison to other measures, the impact of benchmark selection and data frequency, and the analysis of performance persistence (skill vs. luck). Specific results are detailed within the chapter.
What is the practical significance of the thesis (Chapter 5)?
Chapter 5 offers a practical perspective on performance measurement, based on the empirical findings. It provides recommendations and best practices for practitioners, highlighting the strengths and weaknesses of different methods in real-world applications.
What are the key themes explored in the thesis?
Key themes include the reliability and validity of the Information Ratio, its comparison with other performance metrics, the influence of benchmark selection and data frequency on performance measurement, and the analysis of performance persistence and the role of skill versus luck.
What are the chapter summaries?
The thesis includes summaries of each chapter, providing an overview of the content and purpose of each section: Introduction, Theoretical Overview, Data Description and Sources, Empirical Study on Selected Performance Measures, and A Practical View on Performance Measurement.
What are the keywords associated with this thesis?
Keywords include Information Ratio, Portfolio Manager Performance, Fund Performance Measurement, Sharpe Ratio, Treynor Ratio, Jensen's Alpha, Benchmark Selection, Data Frequency, Performance Persistence, Active Returns, and Agency Problems.
- Quote paper
- Christoph Schneider (Author), 2009, How Useful is the Information Ratio to Evaluate the Performance of Portfolio Managers?, Munich, GRIN Verlag, https://www.grin.com/document/132412
