“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.”
Eugene Fama, Robert R. Mc Cormick Distinguished Service Professor of Finance, University of Chicago and 2013 Nobel Laureate in Economic Sciences
“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.”
John Campbell, Morton L. and Carole S. Olshan Professor of Economics, Harvard University
“Bali, Engle, and Murray provide clear and accessible descriptions of many of the most important empirical techniques and results in asset pricing.”
Kenneth R. French, Roth Family Distinguished Professor of Finance, Tuck School of Business, Dartmouth College
“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 Ph D class in empirical asset pricing.”
Lubos Pastor, Charles P. Mc Quaid Professor of Finance, University of Chicago
Empirical Asset Pricing: The Cross Section of Stock Returns 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. Empirical Asset Pricing: The Cross Section of Stock Returns also includes:
- Discussions on the driving forces behind the patterns observed in the stock market
- An extensive set of results that serve as a reference for practitioners and academics alike
- Numerous references to both contemporary and foundational research articles
Empirical Asset Pricing: The Cross Section of Stock Returns 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.
Turan G. Bali, Ph D, is the Robert Parker Chair Professor of Finance in the Mc Donough School of Business at Georgetown University. The recipient of the 2014 Jack Treynor prize, he is the coauthor of Mathematical Methods for Finance: Tools for Asset and Risk Management, also published by Wiley.
Robert F. Engle, Ph D, 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.
Scott Murray, Ph D , 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.
Table of Content
Preface xv
Part I Statistical Methodologies 1
1 Preliminaries 3
1.1 Sample, 3
1.2 Winsorization and Truncation, 5
1.3 Newey and West (1987) Adjustment, 6
1.4 Summary, 8
References, 8
2 Summary Statistics 9
2.1 Implementation, 10
2.1.1 Periodic Cross-Sectional Summary Statistics, 10
2.1.2 Average Cross-Sectional Summary Statistics, 12
2.2 Presentation and Interpretation, 12
2.3 Summary, 16
3 Correlation 17
3.1 Implementation, 18
3.1.1 Periodic Cross-Sectional Correlations, 18
3.1.2 Average Cross-Sectional Correlations, 19
3.2 Interpreting Correlations, 20
3.3 Presenting Correlations, 23
3.4 Summary, 24
References, 24
4 Persistence Analysis 25
4.1 Implementation, 26
4.1.1 Periodic Cross-Sectional Persistence, 26
4.1.2 Average Cross-Sectional Persistence, 28
4.2 Interpreting Persistence, 28
4.3 Presenting Persistence, 31
4.4 Summary, 32
References, 32
5 Portfolio Analysis 33
5.1 Univariate Portfolio Analysis, 34
5.1.1 Breakpoints, 34
5.1.2 Portfolio Formation, 37
5.1.3 Average Portfolio Values, 39
5.1.4 Summarizing the Results, 41
5.1.5 Interpreting the Results, 43
5.1.6 Presenting the Results, 45
5.1.7 Analyzing Returns, 47
5.2 Bivariate Independent-Sort Analysis, 52
5.2.1 Breakpoints, 52
5.2.2 Portfolio Formation, 54
5.2.3 Average Portfolio Values, 57
5.2.4 Summarizing the Results, 60
5.2.5 Interpreting the Results, 64
5.2.6 Presenting the Results, 66
5.3 Bivariate Dependent-Sort Analysis, 71
5.3.1 Breakpoints, 71
5.3.2 Portfolio Formation, 74
5.3.3 Average Portfolio Values, 76
5.3.4 Summarizing the Results, 80
5.3.5 Interpreting the Results, 80
5.3.6 Presenting the Results, 81
5.4 Independent Versus Dependent Sort, 85
5.5 Trivariate-Sort Analysis, 87
5.6 Summary, 87
References, 88
6 Fama and Macbeth Regression Analysis 89
6.1 Implementation, 90
6.1.1 Periodic Cross-Sectional Regressions, 90
6.1.2 Average Cross-Sectional Regression Results, 91
6.2 Interpreting FM Regressions, 95
6.3 Presenting FM Regressions, 98
6.4 Summary, 99
References, 99
Part II the Cross Section of Stock Returns 101
7 The CRSP Sample and Market Factor 103
7.1 The U.S. Stock Market, 103
7.1.1 The CRSP U.S.-Based Common Stock Sample, 104
7.1.2 Composition of the CRSP Sample, 105
7.2 Stock Returns and Excess Returns, 111
7.2.1 CRSP Sample (1963–2012), 115
7.3 The Market Factor, 115
7.4 The CAPM Risk Model, 120
7.5 Summary, 120
References, 121
8 Beta 122
8.1 Estimating Beta, 123
8.2 Summary Statistics, 126
8.3 Correlations, 128
8.4 Persistence, 129
8.5 Beta and Stock Returns, 131
8.5.1 Portfolio Analysis, 132
8.5.2 Fama–Mac Beth Regression Analysis, 140
8.6 Summary, 143
References, 144
9 The Size Effect 146
9.1 Calculating Market Capitalization, 147
9.2 Summary Statistics, 150
9.3 Correlations, 152
9.4 Persistence, 154
9.5 Size and Stock Returns, 155
9.5.1 Univariate Portfolio Analysis, 155
9.5.2 Bivariate Portfolio Analysis, 162
9.5.3 Fama–Mac Beth Regression Analysis, 168
9.6 The Size Factor, 171
9.7 Summary, 173
References, 174
10 The Value Premium 175
10.1 Calculating Book-to-Market Ratio, 177
10.2 Summary Statistics, 181
10.3 Correlations, 183
10.4 Persistence, 184
10.5 Book-to-Market Ratio and Stock Returns, 185
10.5.1 Univariate Portfolio Analysis, 185
10.5.2 Bivariate Portfolio Analysis, 190
10.5.3 Fama–Mac Beth Regression Analysis, 198
10.6 The Value Factor, 200
10.7 The Fama and French Three-Factor Model, 202
10.8 Summary, 203
References, 203
11 The Momentum Effect 206
11.1 Measuring Momentum, 207
11.2 Summary Statistics, 208
11.3 Correlations, 210
11.4 Momentum and Stock Returns, 211
11.4.1 Univariate Portfolio Analysis, 211
11.4.2 Bivariate Portfolio Analysis, 220
11.4.3 Fama–Mac Beth Regression Analysis, 234
11.5 The Momentum Factor, 236
11.6 The Fama, French, and Carhart Four-Factor Model, 238
11.7 Summary, 239
References, 239
12 Short-Term Reversal 242
12.1 Measuring Short-Term Reversal, 243
12.2 Summary Statistics, 243
12.3 Correlations, 243
12.4 Reversal and Stock Returns, 244
12.4.1 Univariate Portfolio Analysis, 244
12.4.2 Bivariate Portfolio Analyses, 249
12.5 Fama–Mac Beth Regressions, 263
12.6 The Reversal Factor, 268
12.7 Summary, 270
References, 271
13 Liquidity 272
13.1 Measuring Liquidity, 274
13.2 Summary Statistics, 276
13.3 Correlations, 277
13.4 Persistence, 280
13.5 Liquidity and Stock Returns, 281
13.5.1 Univariate Portfolio Analysis, 281
13.5.2 Bivariate Portfolio Analysis, 288
13.5.3 Fama–Mac Beth Regression Analysis, 300
13.6 Liquidity Factors, 308
13.6.1 Stock-Level Liquidity, 309
13.6.2 Aggregate Liquidity, 310
13.6.3 Liquidity Innovations, 312
13.6.4 Traded Liquidity Factor, 312
13.7 Summary, 316
References, 316
14 Skewness 319
14.1 Measuring Skewness, 321
14.2 Summary Statistics, 323
14.3 Correlations, 326
14.3.1 Total Skewness, 326
14.3.2 Co-Skewness, 329
14.3.3 Idiosyncratic Skewness, 330
14.3.4 Total Skewness, Co-Skewness, and Idiosyncratic Skewness, 331
14.3.5 Skewness and Other Variables, 333
14.4 Persistence, 336
14.4.1 Total Skewness, 336
14.4.2 Co-Skewness, 338
14.4.3 Idiosyncratic Skewness, 339
14.5 Skewness and Stock Returns, 341
14.5.1 Univariate Portfolio Analysis, 341
14.5.2 Fama–Mac Beth Regressions, 350
14.6 Summary, 359
References, 360
15 Idiosyncratic Volatility 363
15.1 Measuring Total Volatility, 365
15.2 Measuring Idiosyncratic Volatility, 366
15.3 Summary Statistics, 367
15.4 Correlations, 370
15.5 Persistence, 380
15.6 Idiosyncratic Volatility and Stock Returns, 381
15.6.1 Univariate Portfolio Analysis, 382
15.6.2 Bivariate Portfolio Analysis, 389
15.6.3 Fama–Mac Beth Regression Analysis, 402
15.6.4 Cumulative Returns of Idio Vol FF, 1M Portfolio, 407
15.7 Summary, 409
References, 410
16 Liquid Samples 412
16.1 Samples, 413
16.2 Summary Statistics, 414
16.3 Correlations, 418
16.3.1 CRSP Sample and Price Sample, 418
16.3.2 Price Sample and Size Sample, 420
16.4 Persistence, 421
16.5 Expected Stock Returns, 424
16.5.1 Univariate Portfolio Analysis, 425
16.5.2 Fama–Mac Beth Regression Analysis, 435
16.6 Summary, 438
References, 439
17 Option-Implied Volatility 441
17.1 Options Sample, 443
17.2 Option-Based Variables, 444
17.2.1 Predictive Variables, 444
17.2.2 Option Returns, 447
17.2.3 Additional Notes, 448
17.3 Summary Statistics, 449
17.4 Correlations, 451
17.5 Persistence, 453
17.6 Stock Returns, 455
17.6.1 IVol Spread, IVol Skew, and Vol 1M − IVol, 456
17.6.2 ΔIVol C and ΔIVol P, 460
17.7 Option Returns, 469
17.8 Summary, 474
References, 474
18 Other Stock Return Predictors 477
18.1 Asset Growth, 478
18.2 Investor Sentiment, 479
18.3 Investor Attention, 481
18.4 Differences of Opinion, 482
18.5 Profitability and Investment, 482
18.6 Lottery Demand, 483
References, 484
Index 489
About the author
Turan G. Bali, Ph D, is the Robert Parker Chair Professor of Finance in the Mc Donough School of Business at Georgetown University. The recipient of the 2014 Jack Treynor prize, he is the co-author of Mathematical Methods for Finance: Tools for Asset and Risk Management, also published by Wiley.
Robert F. Engle, Ph D, 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.
Scott Murray, Ph D, 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.
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