Details

Quantitative Equity Investing


Quantitative Equity Investing

Techniques and Strategies
Frank J. Fabozzi Series 1. Aufl.

von: Frank J. Fabozzi, Sergio M. Focardi, Petter N. Kolm

60,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 02.02.2010
ISBN/EAN: 9780470617519
Sprache: englisch
Anzahl Seiten: 528

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Beschreibungen

<p>A comprehensive look at the tools and techniques used in quantitative equity management</p> <p>Some books attempt to extend portfolio theory, but the real issue today relates to the practical implementation of the theory introduced by Harry Markowitz and others who followed. The purpose of this book is to close the implementation gap by presenting state-of-the art quantitative techniques and strategies for managing equity portfolios.</p> <p>Throughout these pages, Frank Fabozzi, Sergio Focardi, and Petter Kolm address the essential elements of this discipline, including financial model building, financial engineering, static and dynamic factor models, asset allocation, portfolio models, transaction costs, trading strategies, and much more. They also provide ample illustrations and thorough discussions of implementation issues facing those in the investment management business and include the necessary background material in probability, statistics, and econometrics to make the book self-contained.</p> <ul> <li>Written by a solid author team who has extensive financial experience in this area</li> <li>Presents state-of-the art quantitative strategies for managing equity portfolios</li> <li>Focuses on the implementation of quantitative equity asset management</li> <li>Outlines effective analysis, optimization methods, and risk models</li> </ul> <p>In today's financial environment, you have to have the skills to analyze, optimize and manage the risk of your quantitative equity investments. This guide offers you the best information available to achieve this goal.</p>
<p>Preface xi</p> <p>About the Authors xv</p> <p><b>Chapter 1 Introduction 1</b></p> <p>In Praise of Mathematical Finance 3</p> <p>Studies of the Use of Quantitative Equity Management 9</p> <p>Looking Ahead for Quantitative Equity Investing 45</p> <p><b>Chapter 2 Financial Econometrics I: Linear Regressions 47</b></p> <p>Historical Notes 47</p> <p>Covariance and Correlation 49</p> <p>Regressions, Linear Regressions, and Projections 61</p> <p>Multivariate Regression 76</p> <p>Quantile Regressions 78</p> <p>Regression Diagnostic 80</p> <p>Robust Estimation of Regressions 83</p> <p>Classification and Regression Trees 96</p> <p>Summary 99</p> <p><b>Chapter 3 Financial Econometrics II: Time Series 101</b></p> <p>Stochastic Processes 101</p> <p>Time Series 102</p> <p>Stable Vector Autoregressive Processes 110</p> <p>Integrated and Cointegrated Variables 114</p> <p>Estimation of Stable Vector Autoregressive (VAR) Models 120</p> <p>Estimating the Number of Lags 137</p> <p>Autocorrelation and Distributional Properties of Residuals 139</p> <p>Stationary Autoregressive Distributed Lag Models 140</p> <p>Estimation of Nonstationary VAR Models 141</p> <p>Estimation with Canonical Correlations 151</p> <p>Estimation with Principal Component Analysis 153</p> <p>Estimation with the Eigenvalues of the Companion Matrix 154</p> <p>Nonlinear Models in Finance 155</p> <p>Causality 156</p> <p>Summary 157</p> <p><b>Chapter 4 Common Pitfalls in Financial Modeling 159</b></p> <p>Theory and Engineering 159</p> <p>Engineering and Theoretical Science 161</p> <p>Engineering and Product Design in Finance 163</p> <p>Learning, Theoretical, and Hybrid Approaches to Portfolio Management 164</p> <p>Sample Biases 165</p> <p>The Bias in Averages 167</p> <p>Pitfalls in Choosing from Large Data Sets 170</p> <p>Time Aggregation of Models and Pitfalls in the Selection of Data Frequency 173</p> <p>Model Risk and its Mitigation 174</p> <p>Summary 193</p> <p><b>Chapter 5 Factor Models and Their Estimation 195</b></p> <p>The Notion of Factors 195</p> <p>Static Factor Models 196</p> <p>Factor Analysis and Principal Components Analysis 205</p> <p>Why Factor Models of Returns 219</p> <p>Approximate Factor Models of Returns 221</p> <p>Dynamic Factor Models 222</p> <p>Summary 239</p> <p><b>Chapter 6 Factor-Based Trading Strategies I: Factor Construction and Analysis 243</b></p> <p>Factor-Based Trading 245</p> <p>Developing Factor-Based Trading Strategies 247</p> <p>Risk to Trading Strategies 249</p> <p>Desirable Properties of Factors 251</p> <p>Sources for Factors 251</p> <p>Building Factors from Company Characteristics 253</p> <p>Working with Data 253</p> <p>Analysis of Factor Data 261</p> <p>Summary 266</p> <p><b>Chapter 7 Factor-Based Trading Strategies II: Cross-Sectional Models and Trading Strategies 269</b></p> <p>Cross-Sectional Methods for Evaluation of Factor Premiums 270</p> <p>Factor Models 278</p> <p>Performance Evaluation of Factors 288</p> <p>Model Construction Methodologies for a Factor-Based Trading Strategy 295</p> <p>Backtesting 306</p> <p>Backtesting Our Factor Trading Strategy 308</p> <p>Summary 309</p> <p><b>Chapter 8 Portfolio Optimization: Basic Theory and Practice 313</b></p> <p>Mean-Variance Analysis: Overview 314</p> <p>Classical Framework for Mean-Variance Optimization 317</p> <p>Mean-Variance Optimization with a Risk-Free Asset 321</p> <p>Portfolio Constraints Commonly Used in Practice 327</p> <p>Estimating the Inputs Used in Mean-Variance Optimization: Expected Return and Risk 333</p> <p>Portfolio Optimization with Other Risk Measures 342</p> <p>Summary 357</p> <p><b>Chapter 9 Portfolio Optimization: Bayesian Techniques and the Black-Litterman Model 361</b></p> <p>Practical Problems Encountered in Mean-Variance Optimization 362</p> <p>Shrinkage Estimation 369</p> <p>The Black-Litterman Model 373</p> <p>Summary 394</p> <p><b>Chapter 10 Robust Portfolio Optimization 395</b></p> <p>Robust Mean-Variance Formulations 396</p> <p>Using Robust Mean-Variance Portfolio Optimization in Practice 411</p> <p>Some Practical Remarks on Robust Portfolio Optimization Models 416</p> <p>Summary 418</p> <p><b>Chapter 11 Transaction Costs and Trade Execution 419</b></p> <p>A Taxonomy of Transaction Costs 420</p> <p>Liquidity and Transaction Costs 427</p> <p>Market Impact Measurements and Empirical Findings 430</p> <p>Forecasting and Modeling Market Impact 433</p> <p>Incorporating Transaction Costs in Asset-Allocation Models 439</p> <p>Integrated Portfolio Management: Beyond Expected Return and Portfolio Risk 444</p> <p>Summary 446</p> <p><b>Chapter 12 Investment Management and Algorithmic Trading 449</b></p> <p>Market Impact and the Order Book 450</p> <p>Optimal Execution 452</p> <p>Impact Models 455</p> <p>Popular Algorithmic Trading Strategies 457</p> <p>What Is Next? 465</p> <p>Some Comments about the High-Frequency Arms Race 467</p> <p>Summary 470</p> <p><b>Appendix A Data Descriptions and Factor Definitions 473</b></p> <p>The MSCI World Index 473</p> <p>One-Month LIBOR 482</p> <p>The Compustat Point-in-Time, IBES Consensus Databases and Factor Definitions 483</p> <p><b>Appendix B Summary of Well-Known Factors and Their Underlying Economic Rationale 487</b></p> <p><b>Appendix C Review of Eigenvalues and Eigenvectors 493</b></p> <p>The SWEEP Operator 494</p> <p>Index 497</p>
<p><b>FRANK J. FABOZZI</b> is Professor in the Practice of Finance and Becton Fellow at the Yale School of Management and Editor of the <i>Journal of Portfolio Management.</i> He is a Chartered Financial Analyst and earned a doctorate in economics from the City University of New York. <p><b>SERGIO M. FOCARDI</b> is Professor of Finance at EDHEC Business School in Nice and a founding partner of the Paris-based consulting firm The Intertek Group. He is also a member of the Editorial Board of the <i>Journal of Portfolio Management.</i> Sergio holds a degree in electronic engineering from the University of Genoa and a PhD in mathematical finance from the University of Karlsruhe as well as a postgraduate degree in communications from the Galileo Ferraris Electrotechnical Institute (Turin). <p><b>PETTER N. KOLM</b> is the Deputy Director of the Mathematics in Finance Master's Program and Clinical Associate Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University; and a founding Partner of the New York–based financial consulting firm the Heimdall Group, LLC. Previously, Petter worked in the Quantitative Strategies Group at Goldman Sachs Asset Management. He received an MS in mathematics from ETH in Zurich; an MPhil in applied mathematics from the Royal Institute of Technology in Stockholm; and a PhD in applied mathematics from Yale University.
<p>In 1952, Harry Markowitz introduced a critical innovation in investment management—popularly referred to as modern portfolio theory—in which he suggested that investors should decide the allocation of their investment funds on the basis of the trade-off between portfolio risk, as measured by the standard deviation of investment returns, and portfolio return, as measured by the expected value of the investment return. Entire new research areas grew from his groundbreaking idea, which, with the spread of low-cost powerful computers, found important practical applications in several fields of finance. Developing the necessary inputs for constructing portfolios based on modern portfolio theory has been facilitated by the development of Bayesian statistics, shrinkage techniques, factor models, and robust portfolio optimization. Modern quantitative techniques have now made it possible to manage large investment portfolios with computer programs that look for the best risk-return trade-off available in the market. <p>This book shows you how to perform quantitative equity portfolio management using these modern techniques. It skillfully presents state-of-the-art advances in the theory and practice of quantitative equity portfolio management. Page by page, the expert authors—who have all worked closely with hedge fund and quantitative asset management firms—cover the most up-to-date techniques, tools, and strategies used in the industry today. <p>They begin by discussing the role and use of mathematical techniques in finance, offering sound theoretical arguments in support of finance as a rigorous science. They go on to provide extensive background material on one of the principal tools used in quantitative equity management—financial econometrics—covering modern regression theory, applications of Random Matrix Theory, dynamic time series models, vector autoregressive models, and cointegration analysis. The authors then look at financial engineering, the pitfalls of estimation, methods to control model risk, and the modern theory of factor models, including approximate and dynamic factor models. After laying a firm theoretical foundation, they provide practical advice on optimization techniques and trading strategies based on factors and factormodels, offering a modern view on how to construct factor models.

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