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
6 Conclusion
Research Objectives and Key Topics
The primary objective of this thesis is to empirically evaluate the utility and reliability of the Information Ratio as a performance measurement tool for portfolio managers. By analyzing a comprehensive dataset of nearly 10,000 mutual funds, the research investigates whether the Information Ratio serves as a stable and precise indicator of skill, how it compares to alternative performance metrics, and how external factors such as benchmark selection and data frequency influence its validity.
- Critical analysis of the Information Ratio and its theoretical foundations.
- Comparative evaluation of performance measures including Sharpe, Sortino, and Omega.
- Examination of the impact of benchmark selection and data frequency on performance metrics.
- Investigation of performance persistence to distinguish between manager skill and luck.
- Integration of practical perspectives from industry experts on performance measurement.
Excerpt from the Book
1.1 Motivation and Objective
“I do not want a good General, I want a lucky one.” (Napoleon Bonaparte)
In contrast to Napoleon, investors typically do not want to pick a lucky person to administer their funds, but both Napoleon and the investor face a similar problem: how to separate the lucky from the skilled. Historic data shows that five out of one hundred portfolio managers achieve an outstanding performance just by luck, and statistics also reveal that luck – in most cases – does not persist over time. The lucky managers will, however, always cite their superior skills as a reason for their success, while the unsuccessful ones will place the blame on bad luck. By assessing all active managers on the two dimensions luck and skill, four groups are created. The separation of the skilled and lucky from the unskilled but lucky managers and the separation of the skilled but unlucky from the unskilled and unlucky managers is of special interest to all stakeholders in the investment industry. It is, therefore, the investor’s task to apply understandable guidelines, preferably on a quantitative basis, when it comes to evaluating a portfolio manager. On the other hand, it is the fund administration’s task to judge the performance of its managers objectively and to transfer the results into a variable remuneration scheme or to decide about the replacement of a certain manager. (Grinold & Kahn, 2000, pp. 478-480)
Summary of Chapters
1 Introduction: Discusses the motivation for using quantitative performance measures to separate luck from skill in fund management and outlines the thesis structure.
2 Theoretical Overview: Reviews key performance measures including the Treynor, Sharpe, and Sortino ratios, and details the Information Ratio and the fundamental law of active management.
3 Data Description and Sources: Describes the selection process for the 9,632 mutual funds analyzed and the criteria used for benchmark selection and descriptive statistics.
4 Empirical Study on Selected Performance Measures: Analyzes the stability, reliability, and robustness of the Information Ratio across different timeframes, benchmarks, and data frequencies.
5 A Practical View on Performance Measurement: Complements empirical findings with insights from industry practitioners regarding real-world fund management requirements and limitations.
6 Conclusion: Summarizes the key findings, confirms the utility and limitations of the Information Ratio, and provides suggestions for future academic research.
Keywords
Information Ratio, Portfolio Management, Performance Measurement, Active Management, Benchmark Selection, Tracking Error, Fund Performance, Performance Persistence, Sharpe Ratio, Sortino Ratio, Risk-Adjusted Return, Investment Funds, Quantitative Finance, Agency Problems, Active Share.
Frequently Asked Questions
What is the core focus of this thesis?
The thesis evaluates the usefulness and reliability of the Information Ratio as a performance measurement tool for distinguishing skilled portfolio managers from those who are merely lucky.
Which specific performance measures are evaluated?
Besides the Information Ratio, the author examines the Treynor Ratio, Sharpe Ratio, Jensen’s Alpha, Sortino Ratio, M² Measure, and the Omega Measure.
What is the primary research question?
The central question is: Is the Information Ratio a useful and reliable performance measure? This is addressed by testing its stability over time and across different asset classes.
Which scientific methods were applied?
The study uses empirical analysis of nearly 10,000 mutual funds, applying statistical tests such as the Wilcoxon signed-rank test and Pearson correlation, alongside a qualitative survey of industry practitioners.
What does the main body cover?
The main body covers the theoretical background of performance metrics, the empirical dataset construction, rigorous testing of the Information Ratio against benchmarks and data frequencies, and a practical comparison with industry views.
What are the characterizing keywords?
Key terms include Information Ratio, active management, tracking error, benchmark selection, and performance persistence.
How does the Information Ratio relate to the "Fundamental Law of Active Management"?
The thesis explains that the Information Ratio is closely linked to the manager's Information Coefficient (skill) and the breadth of investment decisions, serving as a framework for active portfolio construction.
Does the author recommend the Information Ratio for all fund types?
No, the author explicitly advises against using the Information Ratio for money market funds due to their strong non-normal return distributions, which render the measure unreliable.
How is the "luck vs. skill" problem addressed?
The study uses a quartile-based ranking system and analyzes performance persistence over rolling three-year periods to identify if managers can consistently outperform their benchmarks through skill.
What is the role of the "Active Share" in this thesis?
The author discusses the Active Share measure as a necessary second dimension to complement the Information Ratio, helping to identify "closet indexers" who provide minimal active management despite higher fees.
- 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
