Details

Empirical Asset Pricing


Empirical Asset Pricing

The Cross Section of Stock Returns
1. Aufl.

von: Turan G. Bali, Robert F. Engle, Scott Murray

100,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 26.02.2016
ISBN/EAN: 9781118589472
Sprache: englisch
Anzahl Seiten: 512

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Beschreibungen

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

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