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Active Portfolio Management a Quantitative Approach for Providing Superior Returns and Controlling Risk

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Active Portfolio Management a Quantitative Approach for Providing Superior Returns and Controlling Risk Page iii Active Portfolio Management A Quantitative Approach for Providing Superior Returns and Controlling Risk Richard C. Grinold Ronald N. Kahn SECOND EDITION Page vii CONTENTS Preface xi Acknowledgments xv Chapter 1 1 Introduction Part One Foundations Chapter 2 11 Consensus Expected Returns: The Capital Asset Pricing Model Chapter 3 41 Risk Chapter 4 87 Exceptional Return, Benchmarks, and Value Added Chapter 5 109 Residual Risk and Return: The Information Ratio Chapter 6 147 The Fundamental Law of Active Management Part Two Expected Returns and Valuation Chapter 7 173 Expected Returns and the Arbitrage Pricing Theory Page viii Chapter 8 199 Valuation in Theory Chapter 9 225 Valuation in Practice Part Three Information Processing Chapter 10 261 Forecasting Basics Chapter 11 295 Advanced Forecasting Chapter 12 315 Information Analysis Chapter 13 347 The Information Horizon Part Four Implementation Chapter 14 377 Portfolio Construction Chapter 15 419 Long/Short Investing Chapter 16 445 Transactions Costs, Turnover, and Trading Chapter 17 477 Performance Analysis Page ix Chapter 18 517 Asset Allocation Chapter 19 541 Benchmark Timing Chapter 20 559 The Historical Record for Active Management Chapter 21 573 Open Questions Chapter 22 577 Summary Appendix A 581 Standard Notation Appendix B 583 Glossary Appendix C 587 Return and Statistics Basics Index 591 Page xi PREFACE Why a second edition? Why take time from busy lives? Why devote the energy to improving an existing text rather than writing an entirely new one? Why toy with success? The short answer is: our readers. We have been extremely gratified by Active Portfolio Management's reception in the investment community. The book seems to be on the shelf of every practicing or aspiring quantitatively oriented investment manager, and the shelves of many fundamental portfolio managers as well. But while our readers have clearly valued the book, they have also challenged us to improve it. Cover more topics of relevance to today. Add empirical evidence where appropriate. Clarify some discussions. The long answer is that we have tried to improve Active Portfolio Management along exactly these dimensions. First, we have added significant amounts of new material in the second edition. New chapters cover Advanced Forecasting (Chap. 11), The Information Horizon (Chap. 13), Long/Short Investing (Chap. 15), Asset Allocation (Chap. 18), The Historical Record for Active Management (Chap. 20), and Open Questions (Chap. 21). Some previously existing chapters also cover new material. This includes a more detailed discussion of risk (Chap. 3), dispersion (Chap. 14), market impact (Chap. 16), and academic proposals for performance analysis (Chap. 17). Second, we receive exhortations to add more empirical evidence, where appropriate. At the most general level: how do we know this entire methodology works? Chapter 20, on The Historical Record for Active Management, provides some answers. We have also added empirical evidence about the accuracy of risk models, in Chap. 3. At the more detailed level, readers have wanted more information on typical numbers for information ratios and active risk. Chapter 5 now includes empirical distributions of these statistics. Chapter 15 provides similar empirical results for long/short portfolios. Chapter 3 includes empirical distributions of asset level risk statistics. Page xii Third, we have tried to clarify certain discussions. We received feedback on how clearly we had conveyed certain ideas through at least two channels. First, we presented a talk summarizing the book at several investment management conferences.1 "Seven Quantitative Insights into Active Management" presented the key ideas as: 1. Active Management is Forecasting: consensus views lead to the benchmark. 2. The Information Ratio (IR) is the Key to Value-Added. 3. The Fundamental Law of Active Management: 4. Alphas must control for volatility, skill, and expectations: Alpha = Volatility · IC · Score. 5. Why Datamining is Easy, and guidelines to avoid it. 6. Implementation should subtract as little value as possible. 7. Distinguishing skill from luck is difficult. This talk provided many opportunities to gauge understanding and confusion over these basic ideas. We also presented a training course version of the book, called "How to Research Active Strategies." Over 500 investment professionals from New York to London to Hong Kong and Tokyo have participated. This course, which involved not only lectures, but problem sets and extensive discussions, helped to identify some remaining confusions with the material. For example, how does the forecasting methodology in the book, which involves information about returns over time, apply to the standard case of information about many assets at one time? We have devoted Chap. 11, Advanced Forecasting, to that important discussion. Finally, we have fixed some typographical errors, and added more problems and exercises to each chapter. We even added a new type of problem—applications exercises. These use commercially available analytics to demonstrate many of the ideas in the 1The BARRA Newsletter presented a serialized version of this talk during 1997 and 1998. Page xiii book. These should help make some of the more technical results accessible to less mathematical readers. Beyond these many reader-inspired improvements, we may also bring a different perspective to the second edition of Active Portfolio Management. Both authors now earn their livelihoods as active managers. To readers of the first edition of Active Portfolio Management, we hope this second edition answers your challenges. To new readers, we hope you continue to find the book important, useful, challenging, and comprehensive. RICHARD C. GRINOLD RONALD N. KAHN Page xv ACKNOWLEDGMENTS Many thanks to Andrew Rudd for his encouragement of this project while the authors were employed at BARRA, and to Blake Grossman for his continued enthusiasm and support of this effort at Barclays Global Investors. Any close reader will realize that we have relied heavily on the path breaking work of Barr Rosenberg. Barr was the pioneer in applying economics, econometrics and operations research to solve practical investment problems. To a lesser, but not less crucial extent, we are indebted to the original and practical work of Bill Sharpe and Fischer Black. Their ideas are the foundation of much of our analysis. Many people helped shape the final form of this book. Internally at BARRA and Barclays Global Investors, we benefited from conversations with and feedback from Andrew Rudd, Blake Grossman, Peter Algert, Stan Beckers, Oliver Buckley, Vinod Chandrashekaran, Naozer Dadachanji, Arjun DiVecha, Mark Engerman, Mark Ferrari, John Freeman, Ken Hui, Ken Kroner, Uzi Levin, Richard Meese, Peter Muller, George Patterson, Scott Scheffler, Dan Stefek, Nicolo Torre, Marco Vangelisti, Barton Waring, and Chris Woods. Some chapters appeared in preliminary form at BARRA seminars and as journal articles, and we benefited from broader feedback from the quantitative investment community. At the more detailed level, several members of the research groups at BARRA and Barclays Global Investors helped generate the examples in the book, especially Chip Castille, Mikhail Dvorkin, Cliff Gong, Josh Rosenberg, Mike Shing, Jennifer Soller, and Ko Ushigusa. BARRA and Barclays Global Investors have also been supportive throughout. Finally, we must thank Leslie Henrichsen, Amber Mayes, Carolyn Norton, and Mary Wang for their administrative help over many years. Page 1 Chapter 1— Introduction The art of investing is evolving into the science of investing. This evolution has been happening slowly and will continue for some time. The direction is clear; the pace varies. As new generations of increasingly scientific investment managers come to the task, they will rely more on analysis, process, and structure than on intuition, advice, and whim. This does not mean that heroic personal investment insights are a thing of the past. It means that managers will increasingly capture and apply those insights in a systematic fashion. We hope this book will go part of the way toward providing the analytical underpinnings for the new class of active investment managers. We are addressing a fresh topic. Quantitative active management—applying rigorous analysis and a rigorous process to try to beat the market—is a cousin of the modern study of financial economics. Financial economics is conducted with much vigor at leading universities, safe from any need to deliver investment returns. Indeed, from the perspective of the financial economist, active portfolio management appears to be a mundane consideration, if not an entirely dubious proposition. Modern financial economics, with its theories of market efficiency, inspired the move over the past decade away from active management (trying to beat the market) to passive management (trying to match the market). This academic view of active management is not monolithic, since the academic cult of market efficiency has split. One group now enthusiastically investigates possible market inefficiencies. Page 2 Still, a hard core remains dedicated to the notion of efficient markets, although they have become more and more subtle in their defense of the market.1 Thus we can look to the academy for structure and insight, but not for solutions. We will take a pragmatic approach and develop a systematic approach to active management, assuming that this is a worthwhile goal. Worthwhile, but not easy. We remain
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