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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
“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. McCormick 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 PhD class in empirical asset pricing.”
Lubos Pastor, Charles P. McQuaid 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, PhD, 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 Mathematical Methods for Finance: Tools for Asset and Risk Management, also published by Wiley.
Robert F. Engle, PhD, 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, PhD, 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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Table of Contents
Cover
Title Page
Copyright
Dedication
Preface
References
Part I: Statistical Methodologies
Chapter 1: Preliminaries
1.1 Sample
1.2 Winsorization and Truncation
1.3 Newey and West (1987) Adjustment
1.4 Summary
References
Chapter 2: Summary Statistics
2.1 Implementation
2.2 Presentation and Interpretation
2.3 Summary
Chapter 3: Correlation
3.1 Implementation
3.2 Interpreting Correlations
3.3 Presenting Correlations
3.4 Summary
References
Chapter 4: Persistence Analysis
4.1 Implementation
4.2 Interpreting Persistence
4.3 Presenting Persistence
4.4 Summary
References
Chapter 5: Portfolio Analysis
5.1 Univariate Portfolio Analysis
5.2 Bivariate Independent-Sort Analysis
5.3 Bivariate Dependent-Sort Analysis
5.4 Independent Versus Dependent Sort
5.5 Trivariate-Sort Analysis
5.6 Summary
References
Chapter 6: Fama and Macbeth Regression Analysis
6.1 Implementation
6.2 Interpreting FM Regressions
6.3 Presenting FM Regressions
6.4 Summary
References
Part II: The Cross Section of Stock Returns
Chapter 7: The Crsp Sample and Market Factor
7.1 The U.S. Stock Market
7.2 Stock Returns and Excess Returns
7.3 The Market Factor
7.4 The Capm Risk Model
7.5 Summary
References
Chapter 8: Beta
8.1 Estimating Beta
8.2 Summary Statistics
8.3 Correlations
8.4 Persistence
8.5 Beta and Stock Returns
8.6 Summary
References
Chapter 9: The Size Effect
9.1 Calculating Market Capitalization
9.2 Summary Statistics
9.3 Correlations
9.4 Persistence
9.5 Size and Stock Returns
9.6 The Size Factor
9.7 Summary
References
Chapter 10: The Value Premium
10.1 Calculating Book-to-Market Ratio
10.2 Summary Statistics
10.3 Correlations
10.4 Persistence
10.5 Book-to-Market Ratio and Stock Returns
10.6 The Value Factor
10.7 The Fama and French Three-Factor Model
10.8 Summary
References
Chapter 11: The Momentum Effect
11.1 Measuring Momentum
11.2 Summary Statistics
11.3 Correlations
11.4 Momentum and Stock Returns
11.5 The Momentum Factor
11.6 The Fama, French, and Carhart Four-Factor Model
11.7 Summary
References
Chapter 12: Short-Term Reversal
12.1 Measuring Short-Term Reversal
12.2 Summary Statistics
12.3 Correlations
12.4 Reversal and Stock Returns
12.5 Fama–Macbeth Regressions
12.6 The Reversal Factor
12.7 Summary
References
Chapter 13: Liquidity
13.1 Measuring Liquidity
13.2 Summary Statistics
13.3 Correlations
13.4 Persistence
13.5 Liquidity and Stock Returns
13.6 Liquidity Factors
13.7 Summary
References
Chapter 14: Skewness
14.1 Measuring Skewness
14.2 Summary Statistics
14.3 Correlations
14.4 Persistence
14.5 Skewness and Stock Returns
14.6 Summary
References
Chapter 15: Idiosyncratic Volatility
15.1 Measuring Total Volatility
15.2 Measuring Idiosyncratic Volatility
15.3 Summary Statistics
15.4 Correlations
15.5 Persistence
15.6 Idiosyncratic Volatility and Stock Returns
15.7 Summary
References
Chapter 16: Liquid Samples
16.1 Samples
16.2 Summary Statistics
16.3 Correlations
16.4 Persistence
16.5 Expected Stock Returns
16.6 Summary
References
Chapter 17: Option-Implied Volatility
17.1 Options Sample
17.2 Option-Based Variables
17.3 Summary Statistics
17.4 Correlations
17.5 Persistence
17.6 Stock Returns
17.7 Option Returns
17.8 Summary
References
Chapter 18: Other Stock Return Predictors
18.1 Asset Growth
18.2 Investor Sentiment
18.3 Investor Attention
18.4 Differences of Opinion
18.5 Profitability and Investment
18.6 Lottery Demand
References
Index
End User License Agreement
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Guide
Cover
Table of Contents
Preface
Part I: Statistical Methodologies
Begin Reading
List of Illustrations
Chapter 7: The Crsp Sample and Market Factor
Figure 7.1 Number of Stocks in CRSP Sample by Exchange
Figure 7.2 Value of Stocks in CRSP Sample by Exchange
Figure 7.3 Number of Stocks in CRSP Sample by Industry
Figure 7.4 Value of Stocks in CRSP Sample by Industry
Figure 7.5 Cumulative Excess Returns of
Chapter 9: The Size Effect
Figure 9.1 Percent of Total Market Value Held by Largest Stocks
Figure 9.2 Cumulative Returns of Portfolio
Chapter 10: The Value Premium
Figure 10.1
Cumulative Returns of
HML
Portfolio
. This Figure plots the cumulate returns of the factor for the period from July 1926 through December 2012. The compounded excess return for month is calculated as 100 times the cumulative product of one plus the monthly return up to and including the given month. The cumulate log excess return is calculated as the sum of the monthly log excess returns up to and including the given month
Chapter 11: The Momentum Effect
Figure 11.1
Cumulative Returns of
MOM
Portfolio.
This Figure plots the cumulate returns of the factor for the period from January 1927 through December 2012. The compounded excess return for month is calculated as 100 times the cumulative product of one plus the monthly return up to and including the given month. The cumulate log excess return is calculated as the sum of the monthly log excess returns up to and including the given month
Chapter 12: Short-Term Reversal
Figure 12.1
Cumulative Returns of
STR
Portfolio
.This Figure plots the cumulate returns of the factor for the period from July 1926 through December 2012. The compounded excess return for month is calculated as 100 times the cumulative product of one plus the monthly return up to and including the given month. The cumulate log excess return is calculated as the sum of the monthly log excess returns up to and including the given month
Chapter 13: Liquidity
Figure 13.1
Time-Series Plot of
. This Figure plots the values of , a measure of aggregate stock market liquidity, for the period from August 1962 through December 2012
Figure 13.2
Time-Series Plot of
L
m
. This Figure plots the values of , a measure of aggregate stock market liquidity, for the period from August 1962 through December 2012
Figure 13.3
Cumulative Returns of
PSL
Portfolio
. This Figure plots the cumulate returns of the factor for the period from January 1968 through December 2012. The compounded excess return for month is calculated as 100 times the cumulative product of one plus the monthly return up to and including the given month. The cumulate log excess return is calculated as the sum of the monthly log excess returns up to and including the given month
Chapter 15: Idiosyncratic Volatility
Figure 15.1
Cumulative Returns of Low–High
Portfolio
. This Figure plots the cumulate returns of the decile one minus decile 10 value-weighted portfolio for the period from July 1963 through December 2012. The compounded excess return for month is calculated as 100 times the cumulative product of one plus the monthly return up to and including the given month. The cumulate log excess return is calculated as the sum of monthly log excess returns up to and including the given month.
List of Tables
Chapter 2: Summary Statistics
Table 2.1 Annual Summary Statistics for This Table presents summary statistics for for each year during the sample period. For each year , we calculate the mean (), standard deviation (), skewness (), excess kurtosis (), minimum (), fifth percentile (), 25th percentile (), median (), 75th percentile (), 95th percentile (), and maximum () values of the distribution of across all stocks in the sample. The sample consists of all U.S.-based common stocks in the Center for Research in Security Prices (CRSP) database as of the end of the given year and covers the years from 1988 through 2012. The column labeled indicates the number of observations for which a value of is available in the given year
Table 2.2 Average Cross-Sectional Summary Statistics for This Table presents the time-series averages of the annual cross-sectional summary statistics for . The Table presents the average mean (), standard deviation (), skewness (), excess kurtosis (), minimum (), fifth percentile (), 25th percentile (), median (), 75th percentile (), 95th percentile (), and maximum () values of the distribution of , where the average is taken across all years in the sample. The column labeled indicates the average number of observations for which a value of is available
Table 2.3 Summary Statistics for , , and This Table presents summary statistics for our sample. The sample covers the years from 1988 through 2012, inclusive, and includes all U.S.-based common stocks in the CRSP database. Each year, the mean (), standard deviation (), skewness (), excess kurtosis (), minimum (), fifth percentile (5%), 25th percentile (25%), median (), 75th percentile (75%), 95th percentile (95%), and maximum () values of the cross-sectional distribution of each variable are calculated. The Table presents the time-series means for each cross-sectional value. The column labeled indicates the average number of stocks for which the given variable is available. is the beta of a stock calculated from a regression of the excess stock returns on the excess market returns using all available daily data during year . is the market capitalization of the stock calculated on the last trading day of year and recorded in $millions. is the natural log of . is the ratio of the book value of equity to the market value of equity. is the one-year-ahead excess stock return
Chapter 3: Correlation
Table 3.1 Annual Correlations for , , , and This Table presents the cross-sectional Pearson product–moment () and Spearman rank () correlations between pairs of , , , and . Each column presents either the Pearson or Spearman correlation for one pair of variables, indicated in the column header. Each row represents results from a different year, indicated in the column labeled
Table 3.2 Average Correlations for , , , and This Table presents the time-series averages of the annual cross-sectional Pearson product–moment () and Spearman rank () correlations between pairs of , , , and . Each column presents either the Pearson or Spearman correlation for one pair of variables, indicated in the column header
Table 3.3 Correlations Between , , , and This Table presents the time-series averages of the annual cross-sectional Pearson product–moment and Spearman rank correlations between pairs of , , , and . Below-diagonal entries present the average Pearson product–moment correlations. Above-diagonal entries present the average Spearman rank correlation
Chapter 4: Persistence Analysis
Table 4.1 Annual Persistence of This Table presents the cross-sectional Pearson product–moment correlations between measured in year and measured in year for . The first column presents the year . The subsequent columns present the cross-sectional correlations between measured at time and measured at time , , , , and
Table 4.2 Average Persistence of This Table presents the time-series averages of the cross-sectional Pearson product–moment correlations between measured in year and measured in year for
Table 4.3 Persistence of , , and This Table presents the results of persistence analyses of , , and . For each year , the cross-sectional correlation between the given variable measured at time and the same variable measured at time is calculated. The Table presents the time-series averages of the annual cross-sectional correlations. The column labeled indicates the lag at which the persistence is measured
Chapter 5: Portfolio Analysis
Table 5.1 Univariate Breakpoints for -Sorted Portfolios This Table presents breakpoints for -sorted portfolios. Each year , the first (), second (), third (), fourth (), fifth (), and sixth () breakpoints for portfolios sorted on are calculated as the 10th, 20th, 40th, 60th, 80th, and 90th percentiles, respectively, of the cross-sectional distribution of . Each row in the Table presents the breakpoints for the year indicated in the first column. The subsequent columns present the values of the breakpoints indicated in the first row
Table 5.2 Number of Stocks per Portfolio This Table presents the number of stocks in each of the portfolios formed in each year during the sample period. The column labeled indicates the year. The subsequent columns, labeled for present the number of stocks in the th portfolio
Table 5.3 Univariate Portfolio Equal-Weighted Excess Returns This Table presents the one-year-ahead excess returns of the equal-weighted portfolios formed by sorting on . The column labeled indicates the portfolio formation year. The column labeled indicates the portfolio holding year. The columns labeled 1 through 7 show the excess returns of the seven -sorted portfolios. The column labeled 7-1 presents the difference between the return of portfolio seven and that of portfolio one
Table 5.4 Univariate Portfolio Value-Weighted Excess Returns This Table presents the one-year-ahead excess returns of the value-weighted portfolios formed by sorting on . The column labeled indicates the portfolio formation year. The column labeled indicates the portfolio holding year. The columns labeled 1 through 7 show the excess returns of the seven -sorted portfolios. The column labeled 7-1 presents the difference between the return of portfolio seven and that of portfolio one
Table 5.5 Univariate Portfolio Equal-Weighted Excess Returns Summary This Table presents the results of a univariate portfolio analysis of the relation between beta () and future stock returns (). The row labeled Average presents the equal-weighted average annual return for each of the portfolios. The row labeled Standard error presents the standard error of the estimated mean portfolio return. Standard errors are adjusted following Newey and West (1987) using six lags. The row labeled -statistic presents the -statistic (in parentheses) for the test with null hypothesis that the average portfolio excess return is equal to zero. The row labeled -value presents the two-sided -value for the test with null hypothesis that the average portfolio excess return is equal to zero. The columns labeled 1 through 7 show the excess returns of the seven -sorted portfolios. The column labeled 7-1 presents the results for the difference between the return of portfolio seven and that of portfolio one
Table 5.6 -Sorted Portfolio Excess Returns This Table presents the results of a univariate portfolio analysis of the relation between beta () and future stock returns (). The Table shows that average excess return for each of the seven portfolios as well as for the long–short zero-cost portfolio, that is, long stocks in the seventh portfolio and short stocks in the first portfolio. Newey and West (1987) -statistics, adjusted using six lags, testing the null hypothesis that the average portfolio excess return is equal to zero, are shown in parentheses
Table 5.7 Univariate Portfolio Average Values of , , and This Table presents the average values of , , and for each of the -sorted portfolios. The first column of the Table indicates the variable for which the average value is being calculated. The columns labeled 1 through 7 present the time-series average of annual portfolio mean values of the given variable. The column labeled 7-1 presents the average difference between portfolios 7 and 1. The column labeled 7-1 presents the -statistic, adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average of the difference portfolio is equal to zero
Table 5.8 Average Returns of Portfolios Sorted on , , and This Table presents the average excess returns of equal-weighted portfolios formed by sorting on each of , , and . The first column of the Table indicates the sort variable. The columns labeled 1 through 7 present the time-series average of annual one-year-ahead excess portfolio returns. The column labeled 7-1 presents the average difference in return between portfolios 7 and 1. -statistics testing the null hypothesis that the average portfolio return is equal to zero, adjusted following Newey and West (1987) using six lags, are presented in parentheses
Table 5.9 -Sorted Portfolio Risk-Adjusted Results This Table presents the risk-adjusted alphas and factor sensitivities for the -sorted portfolios. Each year , all stocks in the sample are sorted into seven portfolios based on an ascending sort of with breakpoints set to the 10th, 20th, 40th, 60th, 80th, and 90th percentiles of in the given year. The equal-weighted average one-year-ahead excess portfolio returns are then calculated. The Table presents the average excess returns (Model = Excess return) for each of the seven portfolios as well as for the zero-cost portfolio that is long the seventh portfolio and short the first portfolio. Also presented are the alphas (Coefficient = ) and factor sensitivities (Coefficient = , , , and ) for each of the portfolios using the CAPM (Model = CAPM), Fama and French (1993) three-factor model (Model = FF), and Fama and French (1993) and Carhart (1997) four-factor model (Model = FFC). -statistics, adjusted following Newey and West (1987) using six lags, are presented in parentheses
Table 5.10 Bivariate Independent-Sort Breakpoints This Table presents the breakpoints for a bivariate independent-sort portfolio analysis The first sort variable is and the second sort variable is . The sample is split into three groups (and thus two breakpoints) based on the 30th and 70th percentiles of , and four groups (and thus three breakpoints) based on the 25th, 50th, and 75th percentiles of . The column labeled indicates the year for which the breakpoints are calculated. The columns labeled and present the first and second breakpoints, respectively. The columns labeled , , and present the first, second, and third breakpoints, respectively
Table 5.11 Bivariate Independent-Sort Number of Stocks per Portfolio This Table presents the number of stocks in each of the 12 portfolios formed by sorting independently into three groups and four groups. The columns labeled indicate the year of portfolio formation. The columns labeled 1, 2, and 3 indicate the group. The rows labeled 1, 2, 3, and 4 indicate the groups
Table 5.12 Average Value for the Difference in Difference Portfolio This diagram describes how the difference in difference portfolio for a bivariate-sort portfolio analysis is constructed
Table 5.13 Bivariate Independent-Sort Portfolio Excess Returns This Table presents the equal-weighted excess returns for each of the 12 portfolios formed by sorting independently into three groups and four groups, as well as for the difference and average portfolios. The columns labeled indicate the year of portfolio formation () and the portfolio holding period (). The columns labeled 1, 2, 3, Diff, and Avg indicate the groups. The rows labeled 1, 2, 3, 4, Diff, and Avg indicate the groups
Table 5.14 Bivariate Independent-Sort Portfolio Excess and Abnormal Returns This Table presents the average excess returns (rows labeled Excess Return) and FFC alphas (rows labeled FFC ) for portfolios formed by grouping all stocks into three groups and four groups. The numbers in parentheses are -statistics, adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the time-series average of the portfolio's excess return or FFC alpha is equal to zero
Table 5.15 Bivariate Independent-Sort Portfolio Results This Table presents the average abnormal returns relative to the FFC model for portfolios sorted independently into three groups and four . The breakpoints for the portfolios are the 30th and 70th percentiles. The breakpoints for the portfolios are the 25th, 50th, and 75th percentiles. Table values indicate the alpha relative to the FFC model with corresponding -statistics in parentheses
Table 5.16 Bivariate Independent-Sort Portfolio Results—Differences This Table presents the average abnormal returns relative to the FFC model for long–short zero-cost portfolios that are long stocks in the highest quartile of and short stocks in the lowest quartile of . The portfolios are formed by sorting all stocks independently into groups based on and . The breakpoints used to form the groups are the 30th and 70th percentiles of . Table values indicate the alpha relative to the FFC model with the corresponding -statistics in parentheses
Table 5.17 Bivariate Independent-Sort Portfolio Results—Averages This Table presents the average abnormal returns relative to the FFC model for portfolios formed by sorting independently on and . The Table shows the portfolio FFC alphas and the associated Newey and West (1987) adjusted -statistics calculated using six lags (in parentheses) for the average group within each group of
Table 5.18 Bivariate Independent-Sort Portfolio Results—Differences This Table presents the average excess returns and FFC alphas for portfolios formed by sorting independently on and a second sort variable, which is either or . The Table shows the average excess returns and FFC alphas, along with the associated Newey and West (1987) adjusted -statistics calculated using six lags (in parentheses), for the difference between the portfolios with high and low values of the second sort variable ( or ). The first column indicates the second sort variable. The remaining columns correspond to different groups, as indicated in the header
Table 5.19 Bivariate Independent-Sort Portfolio Results—Averages This Table presents the average excess returns and FFC alphas for portfolios formed by sorting independently on and a second sort variable, which is either or . The Table shows the average excess returns and FFC alphas, along with the associated Newey and West (1987) adjusted -statistics calculated using six lags (in parentheses), for the difference between the portfolios with high and low values of the second sort variable ( or ). The first column indicates the second sort variable. The remaining columns correspond to different groups, as indicated in the header
Table 5.20 Bivariate Dependent-Sort Breakpoints This Table presents the breakpoints for portfolios formed by sorting all stocks in the sample into three groups based on the 30th and 70th percentiles of , and then, within each group, into four groups based on the 25th, 50th, and 75th percentiles of among only stocks in the given groups. The columns labeled indicates the year of the breakpoints. The columns labeled and present the breakpoints. The columns labeled , , and indicate the th breakpoint for stocks in the first, second, and third group, respectively, where is indicated in the columns labeled
Table 5.21 Bivariate Dependent-Sort Number of Stocks per Portfolio This Table presents the number of stocks in each of the 12 portfolios formed by sorting dependently into three groups and then into four groups. The columns labeled indicate the year of portfolio formation. The columns labeled 1, 2, and 3 indicate the group. The rows labeled 1, 2, 3, and 4 indicate the groups
Table 5.22 Bivariate Dependent-Sort Mean Values This Table presents the equal-weighted excess returns for each of the 12 portfolios formed by sorting all stocks in the sample into three groups and then, within each of the groups, into four groups. The columns labeled indicate the year of portfolio formation () and the portfolio holding period (). The columns labeled 1, 2, 3, and Avg indicate the groups. The rows labeled 1, 2, 3, 4, and Diff indicate the groups
Table 5.23 Bivariate Dependent-Sort Portfolio Results Risk-Adjusted Summary This Table presents the results of a bivariate dependent-sort portfolio analysis of the relation between and future stock returns after controlling for
Table 5.24 Bivariate Dependent-Sort Portfolio Results This Table presents the average abnormal returns relative to the FFC model for portfolios sorted dependently into three groups and then, within each of the groups, into four groups. The breakpoints for the portfolios are the 30th and 70th percentiles. The breakpoints for the portfolios are the 25th, 50th, and 75th percentiles. Table values indicate the alpha relative to the FFC model with the corresponding -statistics in parentheses
Table 5.25 Bivariate Dependent-Sort Portfolio Results—Differences This Table presents the average abnormal returns relative to the FFC model for long–short zero-cost portfolios that are long stocks in the highest quartile of and short stocks in the lowest quartile of . The portfolios are formed by sorting all stocks independently into groups based on and . The breakpoints used to form the groups are the 30th and 70th percentiles of . Table values indicate the alpha relative to the FFC model with the corresponding -statistics in parentheses
Table 5.26 Bivariate Dependent-Sort Portfolio Results—Averages This Table presents the average abnormal returns relative to the FFC model for portfolios formed by sorting independently on and . The Table shows the portfolio FFC alphas and the associated Newey and West (1987)-adjusted -statistics calculated using six lags (in parentheses) for the average group within each group of
Table 5.27 Bivariate Dependent-Sort Portfolio Results—Differences This Table presents the average excess returns and FFC alphas for portfolios formed by sorting independently on and a second sort variable, which is either or . The Table shows the average excess returns and FFC alphas, along with the associated Newey and West (1987)-adjusted -statistics calculated using six lags (in parentheses), for the difference between the portfolios with high and low values of the second sort variable ( or ). The first column indicates the second sort variable. The remaining columns correspond to different groups, as indicated in the header
Table 5.28 Bivariate Dependent-Sort Portfolio Results—Averages This Table presents the average excess returns and FFC alphas for portfolios formed by sorting independently on and a second sort variable, which is either or . The Table shows the average excess returns and FFC alphas, along with the associated Newey and West (1987)-adjusted -statistics calculated using six lags (in parentheses), for the difference between the portfolios with high and low values of the second sort variable ( or ). The first column indicates the second sort variable. The remaining columns correspond to different groups, as indicated in the header
Table 5.29 Bivariate Independent-Sort Portfolio Average This Table presents the average for portfolios formed by sorting independently on and
Table 5.30 Bivariate Dependent-Sort Portfolio Average This Table presents the average for portfolios formed by sorting dependently on and then on
Chapter 6: Fama and Macbeth Regression Analysis
Table 6.1 Periodic FM Regression Results This Table presents the estimated intercept () and slope (, , ) coefficients, as well as the values of -squared (), adjusted -squared (Adj. ), and the number of observations () from annual cross-sectional regressions of one-year-ahead future stock excess return () on beta (), size (), and book-to-market ratio (). Panels A, B, and C present results for univariate specifications using only , , and , respectively, as the independent variable. Panel D presents results from the multivariate specification using all three variables as independent variables. All independent variables are winsorized at the 0.5% level on an annual basis prior to running the regressions. The column labeled indicates the year during which the independent variables were calculated () and the year from which the excess return, the dependent variable, is taken ()
Table 6.2 Summarized FM Regression Results This Table presents summarized results of FM regressions of future stock excess returns () on beta (), size (), and book-to-market ratio (). The columns labeled (1), (2), and (3) present results for univariate specifications using only , , and , respectively, as the independent variable. The column labeled (4) presents results from the multivariate specification using all three variables as independent variables. is the intercept coefficient. is the coefficient on . is the coefficient on . is the coefficient on . Standard errors, -statistics, and -values are calculated using the Newey and West (1987) adjustment with six lags
Table 6.3 FM Regression Results This Table presents the results of FM regressions of future stock excess returns () on beta (), size (), and book-to-market ratio (). The columns labeled (1), (2), and (3) present results for univariate specifications using only , , and , respectively, as the independent variable. The column labeled (4) presents results from the multivariate specification using all three variables as independent variables. -statistics, adjusted following Newey and West (1987) using six lags, are presented in parentheses
Chapter 7: The Crsp Sample and Market Factor
Table 7.1 SIC Industry Code Divisions This Table lists the industries corresponding to different SIC industry codes
Table 7.2 Summary Statistics for Returns (1926–2012) This Table presents summary statistics for return variables calculated using the CRSP sample for the months from June 1926 through November 2012 or return months from July 1926 through December 2012. Each month, the mean (), standard deviation (), skewness (), excess kurtosis (), minimum (), fifth percentile (5%), 25th percentile (25%), median (), 75th percentile (75%), 95th percentile (95%), and maximum () values of the cross-sectional distribution of each variable are calculated. The Table presents the time-series means for each cross-sectional value. The column labeled indicates that average number of stocks for which the given variable is available. is the excess stock return, calculated as the stock's month return, adjusted following Shumway (1997) for delistings, minus the return on the risk-free security. is the stock return in month , adjusted following Shumway (1997) for delistings. is the unadjusted excess stock return in month . is the unadjusted stock return in month . All returns are calculated in percent
Table 7.3 Summary Statistics for Returns (1963–2012) This Table presents summary statistics for return variables calculated using the CRSP sample for the months from June 1963 through November 2012 or return months from July 1963 through December 2012. Each month, the mean (), standard deviation (), skewness (), excess kurtosis (), minimum (), fifth percentile (5%), 25th percentile (25%), median (), 75th percentile (75%), 95th percentile (95%), and maximum () values of the cross-sectional distribution of each variable are calculated. The Table presents the time-series means for each cross-sectional value. The column labeled indicates that average number of stocks for which the given variable is available. is the excess stock return, calculated as the stock's month return, adjusted following Shumway (1997) for delistings, minus the return on the risk-free security. is the stock return in month , adjusted following Shumway (1997) for delistings. is the unadjusted excess stock return in month . is the unadjusted stock return in month . All returns are calculated in percent
Chapter 8: Beta
Table 8.1 Summary Statistics This Table presents summary statistics for variables measuring market beta calculated using the CRSP sample for the months from June 1963 through November 2012. Each month, the mean (), standard deviation (), skewness (), excess kurtosis (), minimum (), fifth percentile (5%), 25th percentile (25%), median (), 75th percentile (75%), 95th percentile (95%), and maximum () values of the cross-sectional distribution of each variable are calculated. The Table presents the time-series means for each cross-sectional value. The column labeled indicates that average number of stocks for which the given variable is available. , , , , and are calculated as the slope coefficient from a time-series regression of the stock's excess return on the excess return of the market portfolio using one, three, six, 12, and 24 months of daily return data, respectively. , , , and are calculated similarly using one, two, three, and five years of monthly return data. is calculated following Scholes and Williams (1977) using 12 months of daily return data. is calculated following Dimson (1979) using 12 months of daily return data
Table 8.2 Correlations This Table presents the time-series averages of the annual cross-sectional Pearson product–moment (below-diagonal entries) and Spearman rank (above-diagonal entries) correlations between pairs of variables measuring market beta
Table 8.3 Persistence This Table presents the results of persistence analyses of variables measuring market beta. Each month , the cross-sectional Pearson product–moment correlation between the month and month values of the given variable is calculated. The Table presents the time-series averages of the monthly cross-sectional correlations. The column labeled indicates the lag at which the persistence is measured
Table 8.4 Univariate Portfolio Analysis—Equal-Weighted This Table presents the results of univariate portfolio analyses of the relation between each of measures of market beta and future stock returns. Monthly portfolios are formed by sorting all stocks in the CRSP sample into portfolios using decile breakpoints calculated based on the given sort variable using all stocks in the CRSP sample. The Table shows the average sort variable value, equal-weighted one-month-ahead excess return (in percent per month), and the CAPM alpha (in percent per month) for each of the 10 decile portfolios as well as for the long-short zero-cost portfolio that is long the 10th decile portfolio and short the first decile portfolio. Newey and West (1987) -statistics, adjusted using six lags, testing the null hypothesis that the average portfolio excess return or CAPM alpha is equal to zero, are shown in parentheses
Table 8.5 Univariate Portfolio Analysis—Value-Weighted This Table presents the results of univariate portfolio analyses of the relation between each of measures of market beta and future stock returns. Monthly portfolios are formed by sorting all stocks in the CRSP sample into portfolios using decile breakpoints calculated based on the given sort variable using all stocks in the CRSP sample. The Table shows the value-weighted one-month-ahead excess return and CAPM alpha (in percent per month) for each of the 10 decile portfolios as well as for the long–short zero-cost portfolio that is long the 10th decile portfolio and short the first decile portfolio. Newey and West (1987) -statistics, adjusted using six lags, testing the null hypothesis that the average portfolio excess return or CAPM alpha is equal to zero, are shown in parentheses
Table 8.6 Fama–MacBeth Regression Analysis This Table presents the results of Fama and MacBeth (1973) regression analyses of the relation between expected stock returns and market beta. Each column in the Table presents results for a different cross-sectional regression specification. The dependent variable in all specifications is the one-month-ahead excess stock return. The independent variable in each specification is indicated in the column header. The independent variable is winsorized at the 0.5% level on a monthly basis. The Table presents average slope and intercept coefficients along with -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average coefficient is equal to zero. The rows labeled Adj. and present the average adjusted -squared and number of data points, respectively, for the cross-sectional regressions
Chapter 9: The Size Effect
Table 9.1
Summary Statistics
This Table presents summary statistics for variables measuring firm size calculated using the CRSP sample for the months from June 1963 through November 2012. Each month, the mean (), standard deviation (), skewness (), excess kurtosis (), minimum (), fifth percentile (5%), 25th percentile (25%), median (), 75th percentile (75%), 95th percentile (95%), and maximum () values of the cross-sectional distribution of each variable are calculated. The Table presents the time-series means for each cross-sectional value. The column labeled indicates the average number of stocks for which the given variable is available. is calculated as the share price times the number of shares outstanding as of the end of month , measured in millions of dollars. is the natural log of . is adjusted using the consumer price index to reflect 2012 dollars and is the natural log of . is the share price times the number of shares outstanding calculated as of the end of the most recent June, measured in millions of dollars. is the natural log of . is adjusted using the consumer price index to reflect 2012 dollars, and is the natural log of
Table 9.2
Correlations
This Table presents the time-series averages of the annual cross-sectional Pearson product moment (below-diagonal entries) and Spearman rank (above-diagonal entries) correlations between pairs of variables measuring firm size
Table 9.3
Persistence
This Table presents the results of persistence analyses of , , , and values. Each month , the cross-sectional Pearson product–moment correlation between the month and month values of the given variable is calculated. The Table presents the time-series averages of the monthly cross-sectional correlations. The column labeled indicates the lag at which the persistence is measured
Table 9.4
Univariate Portfolio Analysis—NYSE Breakpoints
This Table presents the results of univariate portfolio analyses of the relation between each of measures of market capitalization and future stock returns. Monthly portfolios are formed by sorting all stocks in the CRSP sample into portfolios using decile breakpoints calculated based on the given sort variable using the subset of the stocks in the CRSP sample that are listed on the New York Stock Exchange. Panel A shows the average market capitalization (in $millions), CPI-adjusted (2012 dollars) market capitalization, percentage of total market capitalization, percentage of stocks that are listed on the New York Stock Exchange, number of stocks, and for stocks in each decile portfolio. Panel B (Panel C) shows the average equal-weighted (value-weighted) one-month-ahead excess return and CAPM alpha (in percent per month) for each of the 10 decile portfolios as well as for the long–short zero-cost portfolio that is long the 10th decile portfolio and short the first decile portfolio. Newey and West (1987) -statistics, adjusted using six lags, testing the null hypothesis that the average portfolio excess return or CAPM alpha is equal to zero, are shown in parentheses
Table 9.5
Univariate Portfolio Analysis—NYSE/AMEX/NASDAQ Breakpoints
This Table presents the results of univariate portfolio analyses of the relation between each of measures of market capitalization and future stock returns. Monthly portfolios are formed by sorting all stocks in the CRSP sample into portfolios using decile breakpoints calculated based on the given sort variable using all stocks in the CRSP sample. Panel A shows the average market capitalization (in $millions), CPI-adjusted (2012 dollars) market capitalization, percentage of total market capitalization, percentage of stocks that are listed on the New York Stock Exchange, number of stocks, and for stocks in each decile portfolio. Panel B (Panel C) shows the average equal-weighted (value-weighted) one-month-ahead excess return and CAPM alpha (in percent per month) for each of the 10 decile portfolios as well as for the long–short zero-cost portfolio that is long the 10th decile portfolio and short the first decile portfolio. Newey and West (1987) -statistics, adjusted using six lags, testing the null hypothesis that the average portfolio excess return or CAPM alpha is equal to zero, are shown in parentheses
Table 9.6
Bivariate Dependent-Sort Portfolio Analysis—NYSE Breakpoints
This Table presents the results of bivariate dependent-sort portfolio analyses of the relation between and future stock returns after controlling for the effect of . Each month, all stocks in the CRSP sample are sorted into five groups based on an ascending sort of . Within each group, all stocks are sorted into five portfolios based on an ascending sort of . The quintile breakpoints used to create the portfolios are calculated using only stocks that are listed on the New York Stock Exchange. The Table presents the average one-month-ahead excess return (in percent per month) for each of the 25 portfolios as well as for the average quintile portfolio within each quintile of . Also shown are the average return and CAPM alpha of a long–short zero-cost portfolio that is long the fifth quintile portfolio and short the first quintile portfolio in each quintile. -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average return or alpha is equal to zero, are shown in parentheses. Panel A presents results for equal-weighted portfolios. Panel B presents results for value-weighted portfolios
Table 9.7
Bivariate Dependent-Sort Portfolio Analysis –NYSE/AMEX/NASDAQ Breakpoints
This Table presents the results of bivariate dependent-sort portfolio analyses of the relation between and future stock returns after controlling for the effect of . Each month, all stocks in the CRSP sample are sorted into five groups based on an ascending sort of . Within each group, all stocks are sorted into five portfolios based on an ascending sort of . The quintile breakpoints used to create the portfolios are calculated using all stocks in the CRSP sample. The Table presents the average one-month-ahead excess return (in percent per month) for each of the 25 portfolios as well as for the average quintile portfolio within each quintile of . Also shown are the average return and CAPM alpha of a long–short zero-cost portfolio that is long the fifth quintile portfolio and short the first quintile portfolio in each quintile. -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average return or alpha is equal to zero, are shown in parentheses. Panel A presents results for equal-weighted portfolios. Panel B presents results for value-weighted portfolios
Table 9.8
Bivariate Independent-Sort Portfolio Analysis—NYSE Breakpoints
This Table presents the results of bivariate independent-sort portfolio analyses of the relation between and future stock returns after controlling for the effect of . Each month, all stocks in the CRSP sample are sorted into five groups based on an ascending sort of . All stocks are independently sorted into five groups based on an ascending sort of . The quintile breakpoints used to create the groups are calculated using only stocks that are listed on the New York Stock Exchange. The intersections of the and groups are used to form 25 portfolios. The Table presents the average one-month-ahead excess return (in percent per month) for each of the 25 portfolios as well as for the average quintile portfolio within each quintile of and the average quintile within each quintile. Also shown are the average return and CAPM alpha of a long–short zero-cost portfolio that is long the fifth () quintile portfolio and short the first () quintile portfolio in each () quintile. -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average return or alpha is equal to zero, are shown in parentheses. Panel A presents results for equal-weighted portfolios. Panel B presents results for value-weighted portfolios
Table 9.9
Bivariate Independent-Sort Portfolio Analysis – NYSE/AMEX/NASDAQ Breakpoints
This Table presents the results of bivariate independent-sort portfolio analyses of the relation between and future stock returns after controlling for the effect of . Each month, all stocks in the CRSP sample are sorted into five groups based on an ascending sort of . All stocks are independently sorted into five groups based on an ascending sort of . The quintile breakpoints used to create the groups are calculated using all stocks in the CRSP sample. The intersections of the and groups are used to form 25 portfolios. The Table presents the average one-month-ahead excess return (in percent per month) for each of the 25 portfolios as well as for the average quintile portfolio within each quintile of and the average quintile within each quintile. Also shown are the average return and CAPM alpha of a long–short zero-cost portfolio that is long the fifth () quintile portfolio and short the first () quintile portfolio in each () quintile. -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average return or alpha is equal to zero, are shown in parentheses. Panel A presents results for equal-weighted portfolios. Panel B presents results for value-weighted portfolios
Table 9.10
Fama–MacBeth Regression Analysis
This Table presents the results of Fama and MacBeth (1973) regression analyses of the relation between expected stock returns and firm size. Each column in the Table presents results for a different cross-sectional regression specification. The dependent variable in all specifications is the one-month-ahead excess stock return. The independent variables are indicated in the first column. Independent variables are winsorized at the 0.5% level on a monthly basis. The Table presents average slope and intercept coefficients along with -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average coefficient is equal to zero. The rows labeled Adj. and present the average adjusted -squared and the number of data points, respectively, for the cross-sectional regressions
Chapter 10: The Value Premium
Table 10.1 Summary Statistics This Table presents summary statistics for variables measuring the ratio of a firm's book value of equity to its market value of equity calculated using the CRSP sample for the months from June 1963 through November 2012. Each month, the mean (), standard deviation (), skewness (), excess kurtosis (), minimum (), fifth percentile (5%), 25th percentile (25%), median (), 75th percentile (75%), 95th percentile (95%), and maximum () values of the cross-sectional distribution of each variable are calculated. The Table presents the time-series means for each cross-sectional value. The column labeled indicates that average number of stocks for which the given variable is available. for months from June of year through May of year is calculated as the book value of common equity as of the end of the fiscal year ending in calendar year to the market value of common equity as of the end of December of year . is the natural log of . and are the book value and market value, respectively, used to calculate , both adjusted to reflect 2012 dollar using the consumer price index and recorded in millions of dollars. is the share price times the number of shares outstanding
Table 10.2 Correlations This Table presents the time-series averages of the annual cross-sectional Pearson product–moment (below-diagonal entries) and Spearman rank (above-diagonal entries) correlations between pairs , , , and
Table 10.3 Persistence This Table presents the results of persistence analyses of and . Each month , the cross-sectional Pearson product–moment correlation between the month and month values of the given variable is calculated. The Table presents the time-series averages of the monthly cross-sectional correlations. The column labeled indicates the lag at which the persistence is measured
Table 10.4 Univariate Portfolio Analysis This Table presents the results of univariate portfolio analyses of the relation between the book-to-market ratio and future stock returns. Monthly portfolios are formed by sorting all stocks in the CRSP sample into portfolios using decile breakpoints calculated using all stocks in the CRSP sample (Panel A) or the subset of the stocks in the CRSP sample that are listed on the New York Stock Exchange (Panel B). The Characteristics section of each panel shows the average values of , , , and , the percentage of stocks that are listed on the New York Stock Exchange, and the number of stocks for each decile portfolio. The EW portfolios (VW portfolios) section in each panel shows the average equal-weighted (value-weighted) one-month-ahead excess return and CAPM alpha (in percent per month) for each of the 10 decile portfolios as well as for the long–short zero-cost portfolio that is long the 10th decile portfolio and short the first decile portfolio. Newey and West (1987) -statistics, adjusted using six lags, testing the null hypothesis that the average portfolio excess return or CAPM alpha is equal to zero, are shown in parentheses
Table 10.5 Bivariate Dependent-Sort Portfolio Analysis This Table presents the results of bivariate dependent-sort portfolio analyses of the relation between and future stock returns after controlling for the effect of each of and (control variables). Each month, all stocks in the CRSP sample are sorted into five groups based on an ascending sort of one of the control variables. Within each control variable group, all stocks are sorted into five portfolios based on an ascending sort of . The quintile breakpoints used to create the portfolios are calculated using all stocks in the CRSP sample. Panel A presents the average return and CAPM alpha (in percent per month) of the long–short zero-cost portfolios that are long the fifth quintile portfolio and short the first quintile portfolio in each quintile, as well as for the average quintile, of the control variable. Panel B presents the average return and CAPM alpha for the average control variable quintile portfolio within each quintile, as well as for the difference between the fifth and first quintiles. Results for equal-weighted (Weights = EW) and value-weighted (Weights = VW) portfolios are shown. -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average return or alpha is equal to zero, are shown in parentheses
Table 10.6 Bivariate Independent-Sort Portfolio Analysis—Control for This Table presents the results of bivariate independent-sort portfolio analyses of the relation between and future stock returns after controlling for the effect of . Each month, all stocks in the CRSP sample are sorted into five groups based on an ascending sort of . All stocks are independently sorted into five groups based on an ascending sort of . The quintile breakpoints used to create the groups are calculated using all stocks in the CRSP sample. The intersections of the and groups are used to form 25 portfolios. The Table presents the average one-month-ahead excess return (in percent per month) for each of the 25 portfolios as well as for the average quintile portfolio within each quintile of and the average quintile within each quintile. Also shown are the average return and CAPM alpha of a long–short zero-cost portfolio that is long the fifth () quintile portfolio and short the first () quintile portfolio in each () quintile. -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average return or alpha is equal to zero, are shown in parentheses. Panel A presents results for equal-weighted portfolios. Panel B presents results for value-weighted portfolios
Table 10.7 Bivariate Independent-Sort Portfolio Analysis—Control for This Table presents the results of bivariate independent-sort portfolio analyses of the relation between and future stock returns after controlling for the effect of . Each month, all stocks in the CRSP sample are sorted into five groups based on an ascending sort of . All stocks are independently sorted into five groups based on an ascending sort of . The quintile breakpoints used to create the groups are calculated using all stocks in the CRSP sample. The intersections of the and groups are used to form 25 portfolios. The Table presents the average one-month-ahead excess return (in percent per month) for each of the 25 portfolios as well as for the average quintile portfolio within each quintile of and the average quintile within each quintile. Also shown are the average return and CAPM alpha of a long–short zero-cost portfolio that is long the fifth () quintile portfolio and short the first () quintile portfolio in each () quintile. -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average return or alpha is equal to zero, are shown in parentheses. Panel A presents results for equal-weighted portfolios. Panel B presents results for value-weighted portfolios
Table 10.8 Fama–MacBeth Regression Analysis This Table presents the results of Fama and MacBeth (1973) regression analyses of the relation between expected stock returns and book-to-market ratio. Each column in the Table presents results for a different cross-sectional regression specification. The dependent variable in all specifications is the one-month-ahead excess stock return. The independent variables are indicated in the first column. Independent variables are winsorized at the 0.5% level on a monthly basis. The Table presents average slope and intercept coefficients along with -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average coefficient is equal to zero. The rows labeled Adj. and present the average adjusted -squared and the number of data points, respectively, for the cross-sectional regressions
Chapter 11: The Momentum Effect
Table 11.1 Summary Statistics This Table presents summary statistics for variables measuring momentum using the CRSP sample for the months from June 1963 through November 2012. Each month, the mean (), standard deviation (), skewness (), excess kurtosis (), minimum (), fifth percentile (5%), 25th percentile (25%), median (), 75th percentile (75%), 95th percentile (95%), and maximum () values of the cross-sectional distribution of each variable are calculated. The Table presents the time-series means for each cross-sectional value. The column labeled indicates the average number of stocks for which the given variable is available. in month is the return of the stock during the 11-month period including months through . is the return of the stock during months through . is the return of the stock during months through . is the return of the stock during months through month . is the return of the stock during months through month . is the return of the stock during months through month . is the return of the stock during months through month
Table 11.2 Correlations This Table presents the time-series averages of the annual cross-sectional Pearson product–moment (Panel A) and Spearman rank (Panel B) correlations between different measures of momentum and each of , , and
Table 11.3 Univariate Portfolio Analysis This Table presents the results of univariate portfolio analyses of the relation between each of the measures of momentum and future stock returns. Monthly portfolios are formed by sorting all stocks in the CRSP sample into portfolios using decile breakpoints calculated based on the given sort variable using all stocks in the CRSP sample. Panel A shows the average values of , , , and for stocks in each decile portfolio. Panel B (Panel C) shows the average value-weighted (equal-weighted) one-month-ahead excess return (in percent per month) for each of the 10 decile portfolios. The Table also shows the average return of the portfolio that is long the 10th decile portfolio and short the first decile portfolio, as well as the CAPM and FF alpha for this portfolio. Newey and West (1987) -statistics, adjusted using six lags, testing the null hypothesis that the average 10-1 portfolio return or alpha is equal to zero, are shown in parentheses
Table 11.4 Univariate Portfolio Analysis—-Month-Ahead Returns This Table presents the results of univariate portfolio analyses of the relation between each of measures of momentum and future stock returns. Monthly portfolios are formed by sorting all stocks in the CRSP sample into portfolios using decile breakpoints calculated based on the given sort variable using all stocks in the CRSP sample. Each panel in the Table shows that average -month-ahead return (as indicated in the column header), in percent per month, along with the associated FF alpha, of the portfolio, that is, long the 10th decile portfolio and short the first decile portfolio. Panel A (Panel B) shows results for value-weighted (equal-weighted) portfolios. Newey and West (1987) -statistics, adjusted using six lags, testing the null hypothesis that the average portfolio return or alpha is equal to zero are shown in parentheses
Table 11.5 Bivariate Dependent-Sort Portfolio Analysis—Control for This Table presents the results of bivariate dependent-sort portfolio analyses of the relation between and future stock returns after controlling for the effect of . Each month, all stocks in the CRSP sample are sorted into five groups based on an ascending sort of . Within each group, all stocks are sorted into five portfolios based on an ascending sort of . The quintile breakpoints used to create the portfolios are calculated using all stocks in the CRSP sample. The Table presents the average one-month-ahead excess return (in percent per month) for each of the 25 portfolios as well as for the average quintile portfolio within each quintile of . Also shown are the average return, CAPM alpha, and FF alpha of a long–short zero-cost portfolio, that is, long the fifth quintile portfolio and short the first quintile portfolio in each quintile. -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average return or alpha is equal to zero, are shown in parentheses. Panel A presents results for value-weighted portfolios. Panel B presents results for equal-weighted portfolios
Table 11.6 Bivariate Dependent-Sort Portfolio Analysis—Small Stocks This Table presents the results of bivariate dependent-sort portfolio analyses of the relation between and future stock returns after controlling for the effect of using only small stocks. Each month, all stocks with values below the 20th percentile value of in the CRSP sample are sorted into four groups based on an ascending sort of . Within each group, all stocks are sorted into five portfolios based on an ascending sort of . The Table presents the average one-month-ahead excess return (in percent per month) for each of the 20 portfolios as well as for the average portfolio within each group. Also shown are the average return, CAPM alpha, and FF alpha of a long–short zero-cost portfolio, that is, long the fifth quintile portfolio and short the first quintile portfolio in each group. -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average return or alpha is equal to zero, are shown in parentheses. Panel A presents results for value-weighted portfolios. Panel B presents results for equal-weighted portfolios
Table 11.7 Bivariate Dependent-Sort Portfolio Analysis This Table presents the results of bivariate dependent-sort portfolio analyses of the relation between and future stock returns after controlling for the effect of each of and (control variables). Each month, all stocks in the CRSP sample are sorted into five groups based on an ascending sort of one of the control variables. Within each control variable group, all stocks are sorted into five portfolios based on an ascending sort of . The quintile breakpoints used to create the portfolios are calculated using all stocks in the CRSP sample. The Table presents the average return, CAPM alpha, and FF alpha (in percent per month) of the long–short zero-cost portfolios that are long the fifth quintile portfolio and short the first quintile portfolio in each quintile, as well as for the average quintile, of the control variable. Results for value-weighted (Weights = VW) and equal-weighted (Weights = EW) portfolios are shown. -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average return or alpha is equal to zero, are shown in parentheses
Table 11.8 Bivariate Independent-Sort Portfolio Analysis This Table presents the results of bivariate independent-sort portfolio analyses of the relation between and future stock returns after controlling for the effect of each of , , and (control variables). Each month, all stocks in the CRSP sample are sorted into five groups based on an ascending sort of the control variable. All stocks are independently sorted into five groups based on an ascending sort of . The quintile breakpoints used to create the groups are calculated using all stocks in the CRSP sample. The intersections of the control variable and groups are used to form 25 portfolios. The Table presents the average return, CAPM alpha, and FF alpha (in percent per month) of the long–short zero-cost portfolios that are long the fifth quintile portfolio and short the first quintile portfolio in each quintile, as well as for the average quintile, of the control variable. Results for value-weighted (Weights = VW) and equal-weighted (Weights = EW) portfolios are shown. -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average return or alpha is equal to zero, are shown in parentheses
Table 11.9 Bivariate Dependent-Sort Portfolio Analysis—Control for This Table presents the results of bivariate independent-sort portfolio analyses of the relation between future stock returns and each of , , and (second sort variables) after controlling for the effect of . Each month, all stocks in the CRSP sample are sorted into five groups based on an ascending sort of . All stocks are independently sorted into five groups based on an ascending sort of one of the second sort variables. The quintile breakpoints used to create the groups are calculated using all stocks in the CRSP sample. The intersections of the and second sort variable groups are used to form 25 portfolios. The Table presents the average return, CAPM alpha, and FF alpha (in percent per month) of the long–short zero-cost portfolios that are long the fifth quintile portfolio and short the first quintile portfolio for the second sort variable in each quintile, as well as for the average quintile, of . Results for value-weighted (Weights = VW) and equal-weighted (Weights = EW) portfolios are shown. -statistics (in parentheses), adjusted following Newey and West (1987) using six lags, testing the null hypothesis that the average return or alpha is equal to zero, are shown in parentheses
Table 11.10 Bivariate Independent-Sort Portfolio Analysis—Control for This Table presents the results of bivariate independent-sort portfolio analyses of the relation between future stock returns and each of , , and
