Empirical asset pricing the cross section of stock returns

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 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 Lm 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 timeseries 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 through 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 through 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 through 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 through 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 and 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 call minus put implied volatility spread capital asset pricing model (CAPM) conditional intertemporal Center for Research in Security Prices (CRSP) CRSP sample (1963– 2012) definition DLRET file DLSTDT file market capitalization market factor, definition MKT portfolio msedelist file RET field risk model SIC industries stock exchange composition summary statistics U.S.-based common stocks value-weighted portfolio VWRETD portfolio WRDS correlations average cross-sectional correlations average pairwise correlations definition Pearson product– moment correlation see Pearson product– moment correlations co-skewness correlations Fama– MacBeth regressions measurement period and data frequency measuring persistence statistics univariate portfolio analysis cross-sectional standard deviation CRSP see Center for Research in Security Prices (CRSP) delta-hedged call returns delta-hedged put returns equal-weighted portfolios Fama and French (FF) three-factor model Fama and MacBeth (FM) regression analysis average cross-section book-to-market ratio and future stock returns co-skewness CRSP sample idiosyncratic skewness IdioVolFF Illiq and ln Illiq, Illiq12M and ln Illiq12M interpretation momentum and future stock returns periodic cross-section presentation price sample size sample total skewness total volatility Fama, French, and Carhart (FFC) four-factor model FM regression analysis see Fama and MacBeth (FM) regression analysis four-factor model (FFC) co-skewness idiosyncratic skewness total skewness four-factor model augmented with Pastor and Stambaugh (FFCPS) co-skewness idiosyncratic skewness total skewness idiosyncratic skewness correlations Fama– MacBeth regressions measurement period and data frequency measuring persistence statistics univariate portfolio analysis idiosyncratic volatility bivariate portfolio analysis see bivariate portfolio analysis CAPM and APT correlations cross-sectional relation cumulative returns, IdioVolFF,1M portfolio FM regression see Fama and MacBeth (FM) regression analysis measurement persistence prediction of positive relation total volatility see total volatility univariate portfolio analysis see univariate portfolio analysis implied volatility changes implied volatility skew Ken French's data library k-month-ahead stock returns, momentum effect liquid samples CRSP sample CRSP sample and price sample price sample price sample and size sample size sample liquidity Amihud measure correlations, Illiq, dependent-sort, control for Illiq12M dependent-sort, control for MktCap, dependent-sort, Illiq12M equal-weighted equal-weighted portfolio unadjusted returns Illiq and ln Illiq, Illiq12M and ln Illiq12M independent-sort, control for Illiq12M independent-sort, Illiq12M lliq12M-sorted persistence PS factor see Pastor and Stambaugh (PS) statistics value-weighted value-weighted Illiq1M-sorted logistic regression long-term reversal effect market capitalization (MktCap) definition summary statistics medium-term momentum effect momentum effect behavioral models behavioral phenomenon bivariate dependent-sort portfolio analysis bivariate independent-sort portfolio analysis conditional CAPM delayed stock-price reaction FFC risk model FM regression analysis intertemporal CAPM long-term reversal effect mutual fund Pearson product– moment correlations persistence analysis rational models Spearman rank correlations stock's momentum, measures of summary statistics univariate portfolio analysis see univariate portfolio analysis Mutual fund, momentum effect New York Stock Exchange (NYSE) Newey and West (1987) adjusted t-statistics Newey and West (1987)-adjusted standard errors option returns Fama– MacBeth regression analysis univariate portfolio analysis option-implied volatility ATM call options ATM put options bivariate dependent-sort portfolio analysis bivariate independent-sort portfolio analysis call minus put implied volatility spread correlations delta-hedged call return delta-hedged put returns excess returns firm characteristics implied volatility changes implied volatility skew OM database OM's implied volatility surface option returns option variables realized minus implied volatility spread stock characteristics stock returns straddle returns ordinary-least-squares (OLS) regression Pastor and Stambaugh (PS) PSL portfolio stock-level liquidity time-series plot of Lm, time-series plot of (Vm /V1)ŷ Pearson correlations book-to-market ratio co-skewness idiosyncratic skewness momentum effect total skewness Pearson product– moment correlations average cross-sectional correlations average pairwise correlations beta and size BM, book-to-market ratio definition momentum one-year-ahead excess return (rt+1) periodic cross-sectional correlations regression techniques periodic cross-section regression persistence analysis average cross-sectional persistence of β of BM, book-to-market ratio definition dual hypothesis problem momentum effect optimal calculation period periodic cross-sectional persistence of Size, size effects portfolio analysis application benefit bivariate dependent-sort portfolio analysis see bivariate dependent-sort portfolio analysis bivariate independent-sort analysis see bivariate independent-sort analysis disadvantage independent vs dependent sort objective trivariate-sort analysis univariate portfolio analysis see univariate portfolio analysis PRC field probit model realized minus implied volatility spread reversal phenomenon sample short-term reversal bivariate dependent-sort portfolio analysis bivariate independent-sort portfolio analysis correlations cumulative returns of STR portfolio equal-weighted portfolio returns lagged values of reversal lagged values of reversal with controls measuring Rev-sorted portfolio characteristics statistics, time-reversal, value-weighted portfolio returns short-term reversal effect SHROUT field SIC codes see standard industrial classification (SIC) codes size effects ALTPRC field bivariate portfolio analysis correlations CRSP definition Fama and French measure of Fama and French three-factor model Fama– MacBeth regression analysis inflation-adjusted values of issues in MktCap and MktCapFF persistence analysis in regression analyses size factor statistics timing of univariate portfolio analysis skewness beta (β) book-to-market ratio (BM) CAPM co-skewness see co-skewness equilibrium idiosyncratic skewness see idiosyncratic skewness illiquidity (Illiq) mean– variance paradigm momentum (Mom) reversal size total skewness see total skewness Spearman rank correlations average cross-sectional correlations average pairwise correlations beta and size BM, book-to-market ratio co-skewness definition idiosyncratic skewness momentum effect one-year-ahead excess return (rt+1) periodic cross-sectional correlations regression analysis total skewness standard errors standard industrial classification (SIC) codes statistics, size effects stock return predictors asset growth differences of opinion investor attention investor sentiment lottery demand profitability and investment stock returns bivariate dependent-sort portfolio analysis bivariate independent-sort portfolio analysis bivariate portfolio analysis equal-weighted portfolios Fama and MacBeth regression analysis univariate portfolio analysis, see also univariate portfolio analysis value-weighted portfolios stock-level liquidity summary statistics average cross-sectional summary statistics β book-to-market ratio MktCap, momentum variables, measures of objectives periodic cross-sectional summary statistics total skewness correlations Fama– MacBeth regressions measurement period and data frequency measuring persistence statistics univariate portfolio analysis total volatility correlations statistics trivariate-sort analysis truncation univariate portfolio analysis average values book-to-market ratio and stock returns breakpoints calculation β– sorted portfolios CAPM co-skewness CRSP sample different sort variables, same outcome variables equal-weighted excess returns equal-weighted liquidity equal-weighted portfolio returns equal-weighted portfolio unadjusted returns FF three-factor model FFC four-factor model idiosyncratic skewness IdioVolFF,1M k-month-ahead returns Illiq12M-sorted liquidity long-term reversal effect Mom-sorted portfolio characteristics objective portfolios formation price sample p-values short-term reversal effect single portfolio analysis size sample slope coefficient time-series means total skewness t-statistics unadjusted returns U.S stock market value premium book-to-market ratio see book-to-market ratio (BM) Fama and French three-factor (FF) risk model HML portfolio risk-based explanation value stocks definition higher long-run returns, generation of value-weighted portfolios Wharton Research Data Services (WRDS) winsorization WILEY END USER LICENSE AGREEMENT Go to www.wiley.com/go/eula to access Wiley's 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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... 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. .. 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