{"product_id":"empirical-asset-pricing-isbn-9781118095041","title":"Empirical Asset Pricing","description":"\u003cp\u003e\u003cb\u003e“Bali, Engle, and Murray have produced a highly accessible introduction to the techniques and evidence of modern empirical asset pricing. This book should be read and absorbed by every serious student of the field, academic and professional.”\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003ci\u003eEugene Fama, Robert R. McCormick Distinguished Service Professor of Finance, University of Chicago and 2013 Nobel Laureate in Economic Sciences\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e“The empirical analysis of the cross-section of stock returns is a monumental achievement of half a century of finance research. Both the established facts and the methods used to discover them have subtle complexities that can mislead casual observers and novice researchers. Bali, Engle, and Murray’s clear and careful guide to these issues provides a firm foundation for future discoveries.”\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003ci\u003eJohn Campbell, Morton L. and Carole S. Olshan Professor of Economics, Harvard University \u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e“Bali, Engle, and Murray provide clear and accessible descriptions of many of the most important empirical techniques and results in asset pricing.”\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003ci\u003eKenneth R. French, Roth Family Distinguished Professor of Finance, Tuck School of Business, Dartmouth College\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e“This exciting new book presents a thorough review of what we know about the cross-section of stock returns. Given its comprehensive nature, systematic approach, and easy-to-understand language, the book is a valuable resource for any introductory PhD class in empirical asset pricing.”\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003ci\u003eLubos Pastor, Charles P. McQuaid Professor of Finance, University of Chicago\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eEmpirical Asset Pricing: The Cross Section of Stock Returns \u003c\/i\u003eis a comprehensive overview of the most important findings of empirical asset pricing research. The book begins with thorough expositions of the most prevalent econometric techniques with in-depth discussions of the implementation and interpretation of results illustrated through detailed examples. The second half of the book applies these techniques to demonstrate the most salient patterns observed in stock returns. The phenomena documented form the basis for a range of investment strategies as well as the foundations of contemporary empirical asset pricing research. \u003ci\u003eEmpirical Asset Pricing: The Cross Section of Stock Returns \u003c\/i\u003ealso includes:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eDiscussions on the driving forces behind the patterns observed in the stock market\u003c\/li\u003e \u003cli\u003eAn extensive set of results that serve as a reference for practitioners and academics alike\u003c\/li\u003e \u003cli\u003eNumerous references to both contemporary and foundational research articles\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eEmpirical Asset Pricing: The Cross Section of Stock Returns \u003c\/i\u003eis an ideal textbook for graduate-level courses in asset pricing and portfolio management. The book is also an indispensable reference for researchers and practitioners in finance and economics.\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTuran G. Bali, PhD,\u003c\/b\u003e is the Robert Parker Chair Professor of Finance in the McDonough School of Business at Georgetown University. The recipient of the 2014 Jack Treynor prize, he is the coauthor of \u003ci\u003eMathematical Methods for Finance: Tools for Asset and Risk Management\u003c\/i\u003e, also published by Wiley.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eRobert F. Engle, PhD,\u003c\/b\u003e is the Michael Armellino Professor of Finance in the Stern School of Business at New York University. He is the 2003 Nobel Laureate in Economic Sciences, Director of the New York University Stern Volatility Institute, and co-founding President of the Society for Financial Econometrics.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eScott Murray, PhD\u003c\/b\u003e, is an Assistant Professor in the Department of Finance in the J. Mack Robinson College of Business at Georgia State University. He is the recipient of the 2014 Jack Treynor prize. \u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003ePart I Statistical Methodologies 1\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Preliminaries 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Sample, 3\u003c\/p\u003e \u003cp\u003e1.2 Winsorization and Truncation, 5\u003c\/p\u003e \u003cp\u003e1.3 Newey and West (1987) Adjustment, 6\u003c\/p\u003e \u003cp\u003e1.4 Summary, 8\u003c\/p\u003e \u003cp\u003eReferences, 8\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Summary Statistics 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Implementation, 10\u003c\/p\u003e \u003cp\u003e2.1.1 Periodic Cross-Sectional Summary Statistics, 10\u003c\/p\u003e \u003cp\u003e2.1.2 Average Cross-Sectional Summary Statistics, 12\u003c\/p\u003e \u003cp\u003e2.2 Presentation and Interpretation, 12\u003c\/p\u003e \u003cp\u003e2.3 Summary, 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Correlation 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Implementation, 18\u003c\/p\u003e \u003cp\u003e3.1.1 Periodic Cross-Sectional Correlations, 18\u003c\/p\u003e \u003cp\u003e3.1.2 Average Cross-Sectional Correlations, 19\u003c\/p\u003e \u003cp\u003e3.2 Interpreting Correlations, 20\u003c\/p\u003e \u003cp\u003e3.3 Presenting Correlations, 23\u003c\/p\u003e \u003cp\u003e3.4 Summary, 24\u003c\/p\u003e \u003cp\u003eReferences, 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Persistence Analysis 25\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Implementation, 26\u003c\/p\u003e \u003cp\u003e4.1.1 Periodic Cross-Sectional Persistence, 26\u003c\/p\u003e \u003cp\u003e4.1.2 Average Cross-Sectional Persistence, 28\u003c\/p\u003e \u003cp\u003e4.2 Interpreting Persistence, 28\u003c\/p\u003e \u003cp\u003e4.3 Presenting Persistence, 31\u003c\/p\u003e \u003cp\u003e4.4 Summary, 32\u003c\/p\u003e \u003cp\u003eReferences, 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Portfolio Analysis 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Univariate Portfolio Analysis, 34\u003c\/p\u003e \u003cp\u003e5.1.1 Breakpoints, 34\u003c\/p\u003e \u003cp\u003e5.1.2 Portfolio Formation, 37\u003c\/p\u003e \u003cp\u003e5.1.3 Average Portfolio Values, 39\u003c\/p\u003e \u003cp\u003e5.1.4 Summarizing the Results, 41\u003c\/p\u003e \u003cp\u003e5.1.5 Interpreting the Results, 43\u003c\/p\u003e \u003cp\u003e5.1.6 Presenting the Results, 45\u003c\/p\u003e \u003cp\u003e5.1.7 Analyzing Returns, 47\u003c\/p\u003e \u003cp\u003e5.2 Bivariate Independent-Sort Analysis, 52\u003c\/p\u003e \u003cp\u003e5.2.1 Breakpoints, 52\u003c\/p\u003e \u003cp\u003e5.2.2 Portfolio Formation, 54\u003c\/p\u003e \u003cp\u003e5.2.3 Average Portfolio Values, 57\u003c\/p\u003e \u003cp\u003e5.2.4 Summarizing the Results, 60\u003c\/p\u003e \u003cp\u003e5.2.5 Interpreting the Results, 64\u003c\/p\u003e \u003cp\u003e5.2.6 Presenting the Results, 66\u003c\/p\u003e \u003cp\u003e5.3 Bivariate Dependent-Sort Analysis, 71\u003c\/p\u003e \u003cp\u003e5.3.1 Breakpoints, 71\u003c\/p\u003e \u003cp\u003e5.3.2 Portfolio Formation, 74\u003c\/p\u003e \u003cp\u003e5.3.3 Average Portfolio Values, 76\u003c\/p\u003e \u003cp\u003e5.3.4 Summarizing the Results, 80\u003c\/p\u003e \u003cp\u003e5.3.5 Interpreting the Results, 80\u003c\/p\u003e \u003cp\u003e5.3.6 Presenting the Results, 81\u003c\/p\u003e \u003cp\u003e5.4 Independent Versus Dependent Sort, 85\u003c\/p\u003e \u003cp\u003e5.5 Trivariate-Sort Analysis, 87\u003c\/p\u003e \u003cp\u003e5.6 Summary, 87\u003c\/p\u003e \u003cp\u003eReferences, 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Fama and Macbeth Regression Analysis 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Implementation, 90\u003c\/p\u003e \u003cp\u003e6.1.1 Periodic Cross-Sectional Regressions, 90\u003c\/p\u003e \u003cp\u003e6.1.2 Average Cross-Sectional Regression Results, 91\u003c\/p\u003e \u003cp\u003e6.2 Interpreting FM Regressions, 95\u003c\/p\u003e \u003cp\u003e6.3 Presenting FM Regressions, 98\u003c\/p\u003e \u003cp\u003e6.4 Summary, 99\u003c\/p\u003e \u003cp\u003eReferences, 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II the Cross Section of Stock Returns 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The CRSP Sample and Market Factor 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 The U.S. Stock Market, 103\u003c\/p\u003e \u003cp\u003e7.1.1 The CRSP U.S.-Based Common Stock Sample, 104\u003c\/p\u003e \u003cp\u003e7.1.2 Composition of the CRSP Sample, 105\u003c\/p\u003e \u003cp\u003e7.2 Stock Returns and Excess Returns, 111\u003c\/p\u003e \u003cp\u003e7.2.1 CRSP Sample (1963–2012), 115\u003c\/p\u003e \u003cp\u003e7.3 The Market Factor, 115\u003c\/p\u003e \u003cp\u003e7.4 The CAPM Risk Model, 120\u003c\/p\u003e \u003cp\u003e7.5 Summary, 120\u003c\/p\u003e \u003cp\u003eReferences, 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Beta 122\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Estimating Beta, 123\u003c\/p\u003e \u003cp\u003e8.2 Summary Statistics, 126\u003c\/p\u003e \u003cp\u003e8.3 Correlations, 128\u003c\/p\u003e \u003cp\u003e8.4 Persistence, 129\u003c\/p\u003e \u003cp\u003e8.5 Beta and Stock Returns, 131\u003c\/p\u003e \u003cp\u003e8.5.1 Portfolio Analysis, 132\u003c\/p\u003e \u003cp\u003e8.5.2 Fama–MacBeth Regression Analysis, 140\u003c\/p\u003e \u003cp\u003e8.6 Summary, 143\u003c\/p\u003e \u003cp\u003eReferences, 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 The Size Effect 146\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Calculating Market Capitalization, 147\u003c\/p\u003e \u003cp\u003e9.2 Summary Statistics, 150\u003c\/p\u003e \u003cp\u003e9.3 Correlations, 152\u003c\/p\u003e \u003cp\u003e9.4 Persistence, 154\u003c\/p\u003e \u003cp\u003e9.5 Size and Stock Returns, 155\u003c\/p\u003e \u003cp\u003e9.5.1 Univariate Portfolio Analysis, 155\u003c\/p\u003e \u003cp\u003e9.5.2 Bivariate Portfolio Analysis, 162\u003c\/p\u003e \u003cp\u003e9.5.3 Fama–MacBeth Regression Analysis, 168\u003c\/p\u003e \u003cp\u003e9.6 The Size Factor, 171\u003c\/p\u003e \u003cp\u003e9.7 Summary, 173\u003c\/p\u003e \u003cp\u003eReferences, 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 The Value Premium 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Calculating Book-to-Market Ratio, 177\u003c\/p\u003e \u003cp\u003e10.2 Summary Statistics, 181\u003c\/p\u003e \u003cp\u003e10.3 Correlations, 183\u003c\/p\u003e \u003cp\u003e10.4 Persistence, 184\u003c\/p\u003e \u003cp\u003e10.5 Book-to-Market Ratio and Stock Returns, 185\u003c\/p\u003e \u003cp\u003e10.5.1 Univariate Portfolio Analysis, 185\u003c\/p\u003e \u003cp\u003e10.5.2 Bivariate Portfolio Analysis, 190\u003c\/p\u003e \u003cp\u003e10.5.3 Fama–MacBeth Regression Analysis, 198\u003c\/p\u003e \u003cp\u003e10.6 The Value Factor, 200\u003c\/p\u003e \u003cp\u003e10.7 The Fama and French Three-Factor Model, 202\u003c\/p\u003e \u003cp\u003e10.8 Summary, 203\u003c\/p\u003e \u003cp\u003eReferences, 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 The Momentum Effect 206\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Measuring Momentum, 207\u003c\/p\u003e \u003cp\u003e11.2 Summary Statistics, 208\u003c\/p\u003e \u003cp\u003e11.3 Correlations, 210\u003c\/p\u003e \u003cp\u003e11.4 Momentum and Stock Returns, 211\u003c\/p\u003e \u003cp\u003e11.4.1 Univariate Portfolio Analysis, 211\u003c\/p\u003e \u003cp\u003e11.4.2 Bivariate Portfolio Analysis, 220\u003c\/p\u003e \u003cp\u003e11.4.3 Fama–MacBeth Regression Analysis, 234\u003c\/p\u003e \u003cp\u003e11.5 The Momentum Factor, 236\u003c\/p\u003e \u003cp\u003e11.6 The Fama, French, and Carhart Four-Factor Model, 238\u003c\/p\u003e \u003cp\u003e11.7 Summary, 239\u003c\/p\u003e \u003cp\u003eReferences, 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Short-Term Reversal 242\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Measuring Short-Term Reversal, 243\u003c\/p\u003e \u003cp\u003e12.2 Summary Statistics, 243\u003c\/p\u003e \u003cp\u003e12.3 Correlations, 243\u003c\/p\u003e \u003cp\u003e12.4 Reversal and Stock Returns, 244\u003c\/p\u003e \u003cp\u003e12.4.1 Univariate Portfolio Analysis, 244\u003c\/p\u003e \u003cp\u003e12.4.2 Bivariate Portfolio Analyses, 249\u003c\/p\u003e \u003cp\u003e12.5 Fama–MacBeth Regressions, 263\u003c\/p\u003e \u003cp\u003e12.6 The Reversal Factor, 268\u003c\/p\u003e \u003cp\u003e12.7 Summary, 270\u003c\/p\u003e \u003cp\u003eReferences, 271\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Liquidity 272\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Measuring Liquidity, 274\u003c\/p\u003e \u003cp\u003e13.2 Summary Statistics, 276\u003c\/p\u003e \u003cp\u003e13.3 Correlations, 277\u003c\/p\u003e \u003cp\u003e13.4 Persistence, 280\u003c\/p\u003e \u003cp\u003e13.5 Liquidity and Stock Returns, 281\u003c\/p\u003e \u003cp\u003e13.5.1 Univariate Portfolio Analysis, 281\u003c\/p\u003e \u003cp\u003e13.5.2 Bivariate Portfolio Analysis, 288\u003c\/p\u003e \u003cp\u003e13.5.3 Fama–MacBeth Regression Analysis, 300\u003c\/p\u003e \u003cp\u003e13.6 Liquidity Factors, 308\u003c\/p\u003e \u003cp\u003e13.6.1 Stock-Level Liquidity, 309\u003c\/p\u003e \u003cp\u003e13.6.2 Aggregate Liquidity, 310\u003c\/p\u003e \u003cp\u003e13.6.3 Liquidity Innovations, 312\u003c\/p\u003e \u003cp\u003e13.6.4 Traded Liquidity Factor, 312\u003c\/p\u003e \u003cp\u003e13.7 Summary, 316\u003c\/p\u003e \u003cp\u003eReferences, 316\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Skewness 319\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Measuring Skewness, 321\u003c\/p\u003e \u003cp\u003e14.2 Summary Statistics, 323\u003c\/p\u003e \u003cp\u003e14.3 Correlations, 326\u003c\/p\u003e \u003cp\u003e14.3.1 Total Skewness, 326\u003c\/p\u003e \u003cp\u003e14.3.2 Co-Skewness, 329\u003c\/p\u003e \u003cp\u003e14.3.3 Idiosyncratic Skewness, 330\u003c\/p\u003e \u003cp\u003e14.3.4 Total Skewness, Co-Skewness, and Idiosyncratic Skewness, 331\u003c\/p\u003e \u003cp\u003e14.3.5 Skewness and Other Variables, 333\u003c\/p\u003e \u003cp\u003e14.4 Persistence, 336\u003c\/p\u003e \u003cp\u003e14.4.1 Total Skewness, 336\u003c\/p\u003e \u003cp\u003e14.4.2 Co-Skewness, 338\u003c\/p\u003e \u003cp\u003e14.4.3 Idiosyncratic Skewness, 339\u003c\/p\u003e \u003cp\u003e14.5 Skewness and Stock Returns, 341\u003c\/p\u003e \u003cp\u003e14.5.1 Univariate Portfolio Analysis, 341\u003c\/p\u003e \u003cp\u003e14.5.2 Fama–MacBeth Regressions, 350\u003c\/p\u003e \u003cp\u003e14.6 Summary, 359\u003c\/p\u003e \u003cp\u003eReferences, 360\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Idiosyncratic Volatility 363\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Measuring Total Volatility, 365\u003c\/p\u003e \u003cp\u003e15.2 Measuring Idiosyncratic Volatility, 366\u003c\/p\u003e \u003cp\u003e15.3 Summary Statistics, 367\u003c\/p\u003e \u003cp\u003e15.4 Correlations, 370\u003c\/p\u003e \u003cp\u003e15.5 Persistence, 380\u003c\/p\u003e \u003cp\u003e15.6 Idiosyncratic Volatility and Stock Returns, 381\u003c\/p\u003e \u003cp\u003e15.6.1 Univariate Portfolio Analysis, 382\u003c\/p\u003e \u003cp\u003e15.6.2 Bivariate Portfolio Analysis, 389\u003c\/p\u003e \u003cp\u003e15.6.3 Fama–MacBeth Regression Analysis, 402\u003c\/p\u003e \u003cp\u003e15.6.4 Cumulative Returns of IdioVol \u003csup\u003eFF,1M\u003c\/sup\u003e Portfolio, 407\u003c\/p\u003e \u003cp\u003e15.7 Summary, 409\u003c\/p\u003e \u003cp\u003eReferences, 410\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Liquid Samples 412\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Samples, 413\u003c\/p\u003e \u003cp\u003e16.2 Summary Statistics, 414\u003c\/p\u003e \u003cp\u003e16.3 Correlations, 418\u003c\/p\u003e \u003cp\u003e16.3.1 CRSP Sample and Price Sample, 418\u003c\/p\u003e \u003cp\u003e16.3.2 Price Sample and Size Sample, 420\u003c\/p\u003e \u003cp\u003e16.4 Persistence, 421\u003c\/p\u003e \u003cp\u003e16.5 Expected Stock Returns, 424\u003c\/p\u003e \u003cp\u003e16.5.1 Univariate Portfolio Analysis, 425\u003c\/p\u003e \u003cp\u003e16.5.2 Fama–MacBeth Regression Analysis, 435\u003c\/p\u003e \u003cp\u003e16.6 Summary, 438\u003c\/p\u003e \u003cp\u003eReferences, 439\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Option-Implied Volatility 441\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Options Sample, 443\u003c\/p\u003e \u003cp\u003e17.2 Option-Based Variables, 444\u003c\/p\u003e \u003cp\u003e17.2.1 Predictive Variables, 444\u003c\/p\u003e \u003cp\u003e17.2.2 Option Returns, 447\u003c\/p\u003e \u003cp\u003e17.2.3 Additional Notes, 448\u003c\/p\u003e \u003cp\u003e17.3 Summary Statistics, 449\u003c\/p\u003e \u003cp\u003e17.4 Correlations, 451\u003c\/p\u003e \u003cp\u003e17.5 Persistence, 453\u003c\/p\u003e \u003cp\u003e17.6 Stock Returns, 455\u003c\/p\u003e \u003cp\u003e17.6.1 IVolSpread, IVolSkew, and Vol \u003csup\u003e1M \u003c\/sup\u003e− IVol, 456\u003c\/p\u003e \u003cp\u003e17.6.2 ΔIVolC and ΔIVolP, 460\u003c\/p\u003e \u003cp\u003e17.7 Option Returns, 469\u003c\/p\u003e \u003cp\u003e17.8 Summary, 474\u003c\/p\u003e \u003cp\u003eReferences, 474\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Other Stock Return Predictors 477\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Asset Growth, 478\u003c\/p\u003e \u003cp\u003e18.2 Investor Sentiment, 479\u003c\/p\u003e \u003cp\u003e18.3 Investor Attention, 481\u003c\/p\u003e \u003cp\u003e18.4 Differences of Opinion, 482\u003c\/p\u003e \u003cp\u003e18.5 Profitability and Investment, 482\u003c\/p\u003e \u003cp\u003e18.6 Lottery Demand, 483\u003c\/p\u003e \u003cp\u003eReferences, 484\u003c\/p\u003e \u003cp\u003eIndex 489\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eTuran G. Bali, PhD,\u003c\/b\u003e is the Robert Parker Chair Professor of Finance in the McDonough School of Business at Georgetown University. The recipient of the 2014 Jack Treynor prize, he is the co-author of \u003ci\u003eMathematical Methods for Finance: Tools for Asset and Risk Management,\u003c\/i\u003e also published by Wiley. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eRobert F. Engle, PhD,\u003c\/b\u003e is the Michael Armellino Professor of Finance in the Stern School of Business at New York University. He is the 2003 Nobel Laureate in Economic Sciences, Director of the New York University Stern Volatility Institute, and co-founding President of the Society for Financial Econometrics. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eScott Murray, PhD,\u003c\/b\u003e is an Assistant Professor in the Department of Finance in the J. Mack Robinson College of Business at Georgia State University. He is the recipient of the 2014 Jack Treynor prize.   \u003c\/p\u003e\u003cp\u003e\"Bali, Engle, and Murray have produced a highly accessible introduction to the techniques and evidence of modern empirical asset pricing. This book should be read and absorbed by every serious student of the field, academic and professional.\" \u003cb\u003e\u003ci\u003eEugene Fama, Robert R. McCormick Distinguished Service Professor of Finance, University of Chicago and 2013 Nobel Laureate in Economic Sciences\u003c\/i\u003e\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\"The empirical analysis of the cross-section of stock returns is a monumental achievement of half a century of finance research. Both the established facts and the methods used to discover them have subtle complexities that can mislead casual observers and novice researchers. Bali, Engle, and Murray's clear and careful guide to these issues provides a firm foundation for future discoveries.\" \u003cb\u003e\u003ci\u003eJohn Campbell, Morton L. and Carole S. Olshan Professor of Economics, Harvard University\u003c\/i\u003e\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\"Bali, Engle, and Murray provide clear and accessible descriptions of many of the most important empirical techniques and results in asset pricing.\" \u003cb\u003e\u003ci\u003eKenneth R. French, Roth Family Distinguished Professor of Finance, Tuck School of Business, Dartmouth College\u003c\/i\u003e\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\"This exciting new book presents a thorough review of what we know about the cross-section of stock returns. Given its comprehensive nature, systematic approach, and easy-to-understand language, the book is a valuable resource for any introductory PhD class in empirical asset pricing.\" \u003cb\u003e\u003ci\u003eLubos Pastor, Charles P. McQuaid Professor of Finance, University of Chicago\u003c\/i\u003e\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eEmpirical Asset Pricing: The Cross Section of Stock Returns\u003c\/i\u003e is a comprehensive overview of the most important findings of empirical asset pricing research. The book begins with thorough expositions of the most prevalent econometric techniques with in-depth discussions of the implementation and interpretation of results illustrated through detailed examples. The second half of the book applies these techniques to demonstrate the most salient patterns observed in stock returns. The phenomena documented form the basis for a range of investment strategies as well as the foundations of contemporary empirical asset pricing research. \u003ci\u003eEmpirical Asset Pricing: The Cross Section of Stock Returns\u003c\/i\u003e also includes: \u003c\/p\u003e\u003cul\u003e \u003cli\u003e Discussions on the driving forces behind the patterns observed in the stock market\u003c\/li\u003e \u003cli\u003e An extensive set of results that serve as a reference for practitioners and academics alike\u003c\/li\u003e \u003cli\u003e Numerous references to both contemporary and foundational research articles\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eEmpirical Asset Pricing: The Cross Section of Stock Returns\u003c\/i\u003e is an ideal textbook for graduate-level courses in asset pricing and portfolio management. The book is also an indispensable reference for researchers and practitioners in finance and economics.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989126136037,"sku":"NP9781118095041","price":106.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118095041.jpg?v=1761782900","url":"https:\/\/k12savings.com\/products\/empirical-asset-pricing-isbn-9781118095041","provider":"K12savings","version":"1.0","type":"link"}