{"product_id":"quantitative-equity-investing-isbn-9780470262474","title":"Quantitative Equity Investing","description":"\u003cp\u003eA comprehensive look at the tools and techniques used in quantitative equity management\u003c\/p\u003e \u003cp\u003eSome 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.\u003c\/p\u003e \u003cp\u003eThroughout 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eWritten by a solid author team who has extensive financial experience in this area\u003c\/li\u003e \u003cli\u003ePresents state-of-the art quantitative strategies for managing equity portfolios\u003c\/li\u003e \u003cli\u003eFocuses on the implementation of quantitative equity asset management\u003c\/li\u003e \u003cli\u003eOutlines effective analysis, optimization methods, and risk models\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eIn 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.\u003c\/p\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eAbout the Authors xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn Praise of Mathematical Finance 3\u003c\/p\u003e \u003cp\u003eStudies of the Use of Quantitative Equity Management 9\u003c\/p\u003e \u003cp\u003eLooking Ahead for Quantitative Equity Investing 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Financial Econometrics I: Linear Regressions 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHistorical Notes 47\u003c\/p\u003e \u003cp\u003eCovariance and Correlation 49\u003c\/p\u003e \u003cp\u003eRegressions, Linear Regressions, and Projections 61\u003c\/p\u003e \u003cp\u003eMultivariate Regression 76\u003c\/p\u003e \u003cp\u003eQuantile Regressions 78\u003c\/p\u003e \u003cp\u003eRegression Diagnostic 80\u003c\/p\u003e \u003cp\u003eRobust Estimation of Regressions 83\u003c\/p\u003e \u003cp\u003eClassification and Regression Trees 96\u003c\/p\u003e \u003cp\u003eSummary 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Financial Econometrics II: Time Series 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStochastic Processes 101\u003c\/p\u003e \u003cp\u003eTime Series 102\u003c\/p\u003e \u003cp\u003eStable Vector Autoregressive Processes 110\u003c\/p\u003e \u003cp\u003eIntegrated and Cointegrated Variables 114\u003c\/p\u003e \u003cp\u003eEstimation of Stable Vector Autoregressive (VAR) Models 120\u003c\/p\u003e \u003cp\u003eEstimating the Number of Lags 137\u003c\/p\u003e \u003cp\u003eAutocorrelation and Distributional Properties of Residuals 139\u003c\/p\u003e \u003cp\u003eStationary Autoregressive Distributed Lag Models 140\u003c\/p\u003e \u003cp\u003eEstimation of Nonstationary VAR Models 141\u003c\/p\u003e \u003cp\u003eEstimation with Canonical Correlations 151\u003c\/p\u003e \u003cp\u003eEstimation with Principal Component Analysis 153\u003c\/p\u003e \u003cp\u003eEstimation with the Eigenvalues of the Companion Matrix 154\u003c\/p\u003e \u003cp\u003eNonlinear Models in Finance 155\u003c\/p\u003e \u003cp\u003eCausality 156\u003c\/p\u003e \u003cp\u003eSummary 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Common Pitfalls in Financial Modeling 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTheory and Engineering 159\u003c\/p\u003e \u003cp\u003eEngineering and Theoretical Science 161\u003c\/p\u003e \u003cp\u003eEngineering and Product Design in Finance 163\u003c\/p\u003e \u003cp\u003eLearning, Theoretical, and Hybrid Approaches to Portfolio Management 164\u003c\/p\u003e \u003cp\u003eSample Biases 165\u003c\/p\u003e \u003cp\u003eThe Bias in Averages 167\u003c\/p\u003e \u003cp\u003ePitfalls in Choosing from Large Data Sets 170\u003c\/p\u003e \u003cp\u003eTime Aggregation of Models and Pitfalls in the Selection of Data Frequency 173\u003c\/p\u003e \u003cp\u003eModel Risk and its Mitigation 174\u003c\/p\u003e \u003cp\u003eSummary 193\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Factor Models and Their Estimation 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Notion of Factors 195\u003c\/p\u003e \u003cp\u003eStatic Factor Models 196\u003c\/p\u003e \u003cp\u003eFactor Analysis and Principal Components Analysis 205\u003c\/p\u003e \u003cp\u003eWhy Factor Models of Returns 219\u003c\/p\u003e \u003cp\u003eApproximate Factor Models of Returns 221\u003c\/p\u003e \u003cp\u003eDynamic Factor Models 222\u003c\/p\u003e \u003cp\u003eSummary 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Factor-Based Trading Strategies I: Factor Construction and Analysis 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFactor-Based Trading 245\u003c\/p\u003e \u003cp\u003eDeveloping Factor-Based Trading Strategies 247\u003c\/p\u003e \u003cp\u003eRisk to Trading Strategies 249\u003c\/p\u003e \u003cp\u003eDesirable Properties of Factors 251\u003c\/p\u003e \u003cp\u003eSources for Factors 251\u003c\/p\u003e \u003cp\u003eBuilding Factors from Company Characteristics 253\u003c\/p\u003e \u003cp\u003eWorking with Data 253\u003c\/p\u003e \u003cp\u003eAnalysis of Factor Data 261\u003c\/p\u003e \u003cp\u003eSummary 266\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Factor-Based Trading Strategies II: Cross-Sectional Models and Trading Strategies 269\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCross-Sectional Methods for Evaluation of Factor Premiums 270\u003c\/p\u003e \u003cp\u003eFactor Models 278\u003c\/p\u003e \u003cp\u003ePerformance Evaluation of Factors 288\u003c\/p\u003e \u003cp\u003eModel Construction Methodologies for a Factor-Based Trading Strategy 295\u003c\/p\u003e \u003cp\u003eBacktesting 306\u003c\/p\u003e \u003cp\u003eBacktesting Our Factor Trading Strategy 308\u003c\/p\u003e \u003cp\u003eSummary 309\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Portfolio Optimization: Basic Theory and Practice 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMean-Variance Analysis: Overview 314\u003c\/p\u003e \u003cp\u003eClassical Framework for Mean-Variance Optimization 317\u003c\/p\u003e \u003cp\u003eMean-Variance Optimization with a Risk-Free Asset 321\u003c\/p\u003e \u003cp\u003ePortfolio Constraints Commonly Used in Practice 327\u003c\/p\u003e \u003cp\u003eEstimating the Inputs Used in Mean-Variance Optimization: Expected Return and Risk 333\u003c\/p\u003e \u003cp\u003ePortfolio Optimization with Other Risk Measures 342\u003c\/p\u003e \u003cp\u003eSummary 357\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Portfolio Optimization: Bayesian Techniques and the Black-Litterman Model 361\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePractical Problems Encountered in Mean-Variance Optimization 362\u003c\/p\u003e \u003cp\u003eShrinkage Estimation 369\u003c\/p\u003e \u003cp\u003eThe Black-Litterman Model 373\u003c\/p\u003e \u003cp\u003eSummary 394\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Robust Portfolio Optimization 395\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRobust Mean-Variance Formulations 396\u003c\/p\u003e \u003cp\u003eUsing Robust Mean-Variance Portfolio Optimization in Practice 411\u003c\/p\u003e \u003cp\u003eSome Practical Remarks on Robust Portfolio Optimization Models 416\u003c\/p\u003e \u003cp\u003eSummary 418\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Transaction Costs and Trade Execution 419\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Taxonomy of Transaction Costs 420\u003c\/p\u003e \u003cp\u003eLiquidity and Transaction Costs 427\u003c\/p\u003e \u003cp\u003eMarket Impact Measurements and Empirical Findings 430\u003c\/p\u003e \u003cp\u003eForecasting and Modeling Market Impact 433\u003c\/p\u003e \u003cp\u003eIncorporating Transaction Costs in Asset-Allocation Models 439\u003c\/p\u003e \u003cp\u003eIntegrated Portfolio Management: Beyond Expected Return and Portfolio Risk 444\u003c\/p\u003e \u003cp\u003eSummary 446\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Investment Management and Algorithmic Trading 449\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMarket Impact and the Order Book 450\u003c\/p\u003e \u003cp\u003eOptimal Execution 452\u003c\/p\u003e \u003cp\u003eImpact Models 455\u003c\/p\u003e \u003cp\u003ePopular Algorithmic Trading Strategies 457\u003c\/p\u003e \u003cp\u003eWhat Is Next? 465\u003c\/p\u003e \u003cp\u003eSome Comments about the High-Frequency Arms Race 467\u003c\/p\u003e \u003cp\u003eSummary 470\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Data Descriptions and Factor Definitions 473\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe MSCI World Index 473\u003c\/p\u003e \u003cp\u003eOne-Month LIBOR 482\u003c\/p\u003e \u003cp\u003eThe Compustat Point-in-Time, IBES Consensus Databases and Factor Definitions 483\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Summary of Well-Known Factors and Their Underlying Economic Rationale 487\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C Review of Eigenvalues and Eigenvectors 493\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe SWEEP Operator 494\u003c\/p\u003e \u003cp\u003eIndex 497\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eFRANK J. FABOZZI\u003c\/b\u003e is Professor in the Practice of Finance and Becton Fellow at the Yale School of Management and Editor of the \u003ci\u003eJournal of Portfolio Management.\u003c\/i\u003e He is a Chartered Financial Analyst and earned a doctorate in economics from the City University of New York. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eSERGIO M. FOCARDI\u003c\/b\u003e 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 \u003ci\u003eJournal of Portfolio Management.\u003c\/i\u003e 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). \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePETTER N. KOLM\u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003eIn 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.\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003eThey 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.\u003c\/p\u003e  \u003cp\u003eIn 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. \u003c\/p\u003e\u003cp\u003eThis 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. \u003c\/p\u003e\u003cp\u003eThey 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989895594213,"sku":"NP9780470262474","price":95.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470262474.jpg?v=1761785828","url":"https:\/\/k12savings.com\/products\/quantitative-equity-investing-isbn-9780470262474","provider":"K12savings","version":"1.0","type":"link"}