Empirical asset pricing the cross section of stock returns

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 parenthesesTab

ReferencesChapter 2: Summary Statistics

2.1 Implementation2.2 Presentation and Interpretation2.3 Summary

Chapter 3: Correlation

3.1 Implementation3.2 Interpreting Correlations3.3 Presenting Correlations3.4 Summary

ReferencesChapter 4: Persistence Analysis

4.1 Implementation4.2 Interpreting Persistence4.3 Presenting Persistence4.4 Summary

ReferencesChapter 5: Portfolio Analysis

5.1 Univariate Portfolio Analysis5.2 Bivariate Independent-Sort Analysis5.3 Bivariate Dependent-Sort Analysis

5.4 Independent Versus Dependent Sort

Part II: The Cross Section of Stock Returns

Chapter 7: The Crsp Sample and Market Factor7.1 The U.S Stock Market

7.2 Stock Returns and Excess Returns

7.3 The Market Factor

7.4 The Capm Risk Model

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

11.4 Momentum and Stock Returns

11.5 The Momentum Factor

11.6 The Fama, French, and Carhart Four-Factor Model11.7 Summary

References

Chapter 12: Short-Term Reversal

12.1 Measuring Short-Term Reversal

Chapter 15: Idiosyncratic Volatility

15.1 Measuring Total Volatility

15.2 Measuring Idiosyncratic Volatility

17.8 SummaryReferencesChapter 18: Other Stock Return Predictors

18.1 Asset Growth18.2 Investor Sentiment18.3 Investor Attention18.4 Differences of Opinion18.5 Profitability and Investment18.6 Lottery Demand

ReferencesIndex

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 100times the cumulative product of one plus the monthly return up to and includingthe given month The cumulate log excess return is calculated as the sum of themonthly 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 throughDecember 2012 The compounded excess return for month is calculated as 100

times the cumulative product of one plus the monthly return up to and includingthe given month The cumulate log excess return is calculated as the sum of themonthly 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 100times the cumulative product of one plus the monthly return up to and includingthe given month The cumulate log excess return is calculated as the sum of themonthly 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 100times the cumulative product of one plus the monthly return up to and includingthe given month The cumulate log excess return is calculated as the sum of themonthly 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 givenmonth The cumulate log excess return is calculated as the sum of monthly logexcess 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 statisticsfor 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 thedistribution 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 presentsthe 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 inthe sample The column labeled indicates the average number of observationsfor which a value of is available

Table 2.3 Summary Statistics for , , and This Table presents summarystatistics 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 Eachyear, 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 thecross-sectional distribution of each variable are calculated The Table presents thetime-series means for each cross-sectional value The column labeled indicatesthe 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 excessmarket returns using all available daily data during year is the marketcapitalization 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 ofequity to the market value of equity is the one-year-ahead excess stock returnChapter 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 thePearson or Spearman correlation for one pair of variables, indicated in the columnheader Each row represents results from a different year, indicated in the columnlabeled

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 series averages of the annual cross-sectional Pearson product–moment and

time-Spearman rank correlations between pairs of , , , and Below-diagonalentries present the average Pearson product–moment correlations Above-diagonalentries present the average Spearman rank correlation

Chapter 4: Persistence Analysis

Table 4.1 Annual Persistence of This Table presents the cross-sectional Pearsonproduct–moment correlations between measured in year and measured inyear for The first column presents the year The subsequentcolumns 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 ofthe cross-sectional Pearson product–moment correlations between measured inyear 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 variablemeasured at time is calculated The Table presents the time-series averages ofthe annual cross-sectional correlations The column labeled indicates the lag atwhich 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 subsequentcolumns 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 columnlabeled 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 presentsthe 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 columnlabeled 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 betweenbeta ( ) and future stock returns ( ) The row labeled Average presents the

equal-weighted average annual return for each of the portfolios The row labeledStandard error presents the standard error of the estimated mean portfolio return.Standard errors are adjusted following Newey and West (1987) using six lags Therow labeled -statistic presents the -statistic (in parentheses) for the test with nullhypothesis that the average portfolio excess return is equal to zero The row

labeled -value presents the two-sided -value for the test with null hypothesisthat the average portfolio excess return is equal to zero The columns labeled 1through 7 show the excess returns of the seven -sorted portfolios The columnlabeled 7-1 presents the results for the difference between the return of portfolioseven and that of portfolio one

Table 5.6 -Sorted Portfolio Excess Returns This Table presents the results of aunivariate portfolio analysis of the relation between beta ( ) and future stock

returns ( ) The Table shows that average excess return for each of the sevenportfolios as well as for the long–short zero-cost portfolio, that is, long stocks inthe seventh portfolio and short stocks in the first portfolio Newey and West (1987)-statistics, adjusted using six lags, testing the null hypothesis that the averageportfolio 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 Thecolumn labeled 7-1 presents the -statistic, adjusted following Newey and West(1987) using six lags, testing the null hypothesis that the average of the differenceportfolio 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 presentsthe average difference in return between portfolios 7 and 1 -statistics testing thenull 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 adjusted alphas and factor sensitivities for the -sorted portfolios Each year , allstocks 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 portfolioreturns 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 andFrench (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 threegroups (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 75thpercentiles of The column labeled indicates the year for which the

breakpoints are calculated The columns labeled and present the first andsecond 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 Tablepresents 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 3indicate the group The rows labeled 1, 2, 3, and 4indicate the groups

Table 5.12 Average Value for the Difference in Difference Portfolio This diagramdescribes how the difference in difference portfolio for a bivariate-sort portfolioanalysis 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 sortingindependently into three groups and four groups, as well as for the

difference and average portfolios The columns labeled indicate the year ofportfolio 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 ThisTable presents the average excess returns (rows labeled Excess Return) and FFCalphas (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 theaverage 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 alpharelative to the FFC model with corresponding -statistics in parentheses

Table 5.16 Bivariate Independent-Sort Portfolio Results—Differences This Tablepresents the average abnormal returns relative to the FFC model for long–shortzero-cost portfolios that are long stocks in the highest quartile of and shortstocks in the lowest quartile of The portfolios are formed by sorting all

stocks independently into groups based on and The breakpoints used toform the groups are the 30th and 70th percentiles of Table values indicate thealpha relative to the FFC model with the corresponding -statistics in parenthesesTable 5.17 Bivariate Independent-Sort Portfolio Results—Averages This Table

presents the average abnormal returns relative to the FFC model for portfoliosformed by sorting independently on and The Table shows the portfolioFFC alphas and the associated Newey and West (1987) adjusted -statistics

calculated using six lags (in parentheses) for the average group within each

the second sort variable ( or ) The first column indicates the second sortvariable The remaining columns correspond to different groups, as indicated inthe 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 theassociated Newey and West (1987) adjusted -statistics calculated using six lags (inparentheses), for the difference between the portfolios with high and low values ofthe second sort variable ( or ) The first column indicates the second sortvariable The remaining columns correspond to different groups, as indicated inthe header

Table 5.20 Bivariate Dependent-Sort Breakpoints This Table presents the

breakpoints for portfolios formed by sorting all stocks in the sample into threegroups 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 amongonly stocks in the given groups The columns labeled indicates the year of thebreakpoints The columns labeled and present the breakpoints Thecolumns labeled , , and indicate the th breakpoint for

stocks in the first, second, and third group, respectively, where is indicated inthe columns labeled

Table 5.21 Bivariate Dependent-Sort Number of Stocks per Portfolio This Tablepresents the number of stocks in each of the 12 portfolios formed by sorting

dependently into three groups and then into four groups The columnslabeled 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 weighted excess returns for each of the 12 portfolios formed by sorting all stocks inthe 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 SummaryThis Table presents the results of a bivariate dependent-sort portfolio analysis ofthe 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 75thpercentiles 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 Tablepresents the average abnormal returns relative to the FFC model for long–shortzero-cost portfolios that are long stocks in the highest quartile of and shortstocks in the lowest quartile of The portfolios are formed by sorting all

stocks independently into groups based on and The breakpoints used toform the groups are the 30th and 70th percentiles of Table values indicate thealpha relative to the FFC model with the corresponding -statistics in parenthesesTable 5.26 Bivariate Dependent-Sort Portfolio Results—Averages This Table

presents the average abnormal returns relative to the FFC model for portfoliosformed by sorting independently on and The Table shows the portfolioFFC 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 theassociated Newey and West (1987)-adjusted -statistics calculated using six lags (inparentheses), for the difference between the portfolios with high and low values ofthe second sort variable ( or ) The first column indicates the second sortvariable The remaining columns correspond to different groups, as indicated inthe 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 theassociated Newey and West (1987)-adjusted -statistics calculated using six lags (inparentheses), for the difference between the portfolios with high and low values ofthe second sort variable ( or ) The first column indicates the second sortvariable The remaining columns correspond to different groups, as indicated inthe header

Table 5.29 Bivariate Independent-Sort Portfolio Average This Table presentsthe 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 excessreturn ( ) 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 Allindependent variables are winsorized at the 0.5% level on an annual basis prior torunning the regressions The column labeled indicates the year during whichthe independent variables were calculated ( ) and the year from which the excessreturn, the dependent variable, is taken ( )

Table 6.2 Summarized FM Regression Results This Table presents summarizedresults of FM regressions of future stock excess returns ( ) on beta ( ), size (), and book-to-market ratio ( ) The columns labeled (1), (2), and (3) presentresults for univariate specifications using only , , and , respectively, as theindependent variable The column labeled (4) presents results from the

multivariate specification using all three variables as independent variables isthe intercept coefficient is the coefficient on is the coefficient on isthe coefficient on Standard errors, -statistics, and -values are calculatedusing 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-marketratio ( ) The columns labeled (1), (2), and (3) present results for univariatespecifications using only , , and , respectively, as the independent variable.The column labeled (4) presents results from the multivariate specification usingall three variables as independent variables -statistics, adjusted following Neweyand 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 themonths from June 1926 through November 2012 or return months fromJuly 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-seriesmeans for each cross-sectional value The column labeled indicates that averagenumber of stocks for which the given variable is available is the excess stockreturn, calculated as the stock's month return, adjusted following Shumway(1997) for delistings, minus the return on the risk-free security is the stockreturn in month , adjusted following Shumway (1997) for delistings is theunadjusted 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 themonths from June 1963 through November 2012 or return months fromJuly 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-seriesmeans for each cross-sectional value The column labeled indicates that averagenumber of stocks for which the given variable is available is the excess stockreturn, calculated as the stock's month return, adjusted following Shumway(1997) for delistings, minus the return on the risk-free security is the stockreturn in month , adjusted following Shumway (1997) for delistings is theunadjusted 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 variablesmeasuring market beta calculated using the CRSP sample for the months fromJune 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-seriesmeans for each cross-sectional value The column labeled indicates that averagenumber of stocks for which the given variable is available , , , , and are calculated as the slope coefficient from a time-series regression of thestock'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 arecalculated 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 dailyreturn data

Table 8.2 Correlations This Table presents the time-series averages of the annualcross-sectional Pearson product–moment (below-diagonal entries) and Spearmanrank (above-diagonal entries) correlations between pairs of variables measuringmarket beta

Table 8.3 Persistence This Table presents the results of persistence analyses ofvariables measuring market beta Each month , the cross-sectional Pearson

product–moment correlation between the month and month values of thegiven variable is calculated The Table presents the time-series averages of the

monthly cross-sectional correlations The column labeled indicates the lag atwhich the persistence is measured

Table 8.4 Univariate Portfolio Analysis—Equal-Weighted This Table presents theresults of univariate portfolio analyses of the relation between each of measures ofmarket beta and future stock returns Monthly portfolios are formed by sorting allstocks 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 theaverage sort variable value, equal-weighted one-month-ahead excess return (inpercent per month), and the CAPM alpha (in percent per month) for each of the 10decile portfolios as well as for the long-short zero-cost portfolio that is long the10th 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 parenthesesTable 8.5 Univariate Portfolio Analysis—Value-Weighted This Table presents theresults of univariate portfolio analyses of the relation between each of measures ofmarket beta and future stock returns Monthly portfolios are formed by sorting allstocks 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 thevalue-weighted one-month-ahead excess return and CAPM alpha (in percent permonth) for each of the 10 decile portfolios as well as for the long–short zero-costportfolio 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 ofFama and MacBeth (1973) regression analyses of the relation between expectedstock returns and market beta Each column in the Table presents results for adifferent cross-sectional regression specification The dependent variable in allspecifications is the one-month-ahead excess stock return The independent

variable in each specification is indicated in the column header The independentvariable 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 ( ), standarddeviation ( ), 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 averagenumber of stocks for which the given variable is available is calculated asthe 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 thenatural 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 usingthe 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 Spearmanrank (above-diagonal entries) correlations between pairs of variables measuringfirm size

Table 9.3 Persistence This Table presents the results of persistence analyses of

, , , and values Each month , the cross-sectional Pearsonproduct–moment correlation between the month and month values of thegiven variable is calculated The Table presents the time-series averages of the

monthly cross-sectional correlations The column labeled indicates the lag atwhich 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 ofmeasures of market capitalization and future stock returns Monthly portfolios areformed 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 Ashows 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 forstocks 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 permonth) for each of the 10 decile portfolios as well as for the long–short zero-costportfolio 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 stockreturns Monthly portfolios are formed by sorting all stocks in the CRSP sampleinto portfolios using decile breakpoints calculated based on the given sort variableusing 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 theNew 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 islong 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 controllingfor the effect of Each month, all stocks in the CRSP sample are sorted into fivegroups based on an ascending sort of Within each group, all stocks are sortedinto five portfolios based on an ascending sort of The quintile breakpointsused to create the portfolios are calculated using only stocks that are listed on theNew 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 theaverage 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 thefifth 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 andfuture stock returns after controlling for the effect of Each month, all stocks inthe 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 arecalculated using all stocks in the CRSP sample The Table presents the averageone-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 cost portfolio that is long the fifth quintile portfolio and short the first

quintile portfolio in each quintile -statistics (in parentheses), adjustedfollowing Newey and West (1987) using six lags, testing the null hypothesis thatthe average return or alpha is equal to zero, are shown in parentheses Panel Apresents 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 aftercontrolling 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 Thequintile breakpoints used to create the groups are calculated using only stocks thatare listed on the New York Stock Exchange The intersections of the and

groups are used to form 25 portfolios The Table presents the average ahead excess return (in percent per month) for each of the 25 portfolios as well asfor the average quintile portfolio within each quintile of and the average

quintile within each quintile Also shown are the average return andCAPM 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-weightedportfolios 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 andfuture stock returns after controlling for the effect of Each month, all stocks inthe 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 allstocks in the CRSP sample The intersections of the and groups are used

to form 25 portfolios The Table presents the average one-month-ahead excessreturn (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 betweenexpected stock returns and firm size Each column in the Table presents results for

a different cross-sectional regression specification The dependent variable in allspecifications is the one-month-ahead excess stock return The independent

variables are indicated in the first column Independent variables are winsorized atthe 0.5% level on a monthly basis The Table presents average slope and interceptcoefficients along with -statistics (in parentheses), adjusted following Newey andWest (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 variablesmeasuring the ratio of a firm's book value of equity to its market value of equitycalculated 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 arecalculated The Table presents the time-series means for each cross-sectional

value The column labeled indicates that average number of stocks for which thegiven variable is available for months from June of year through May ofyear is calculated as the book value of common equity as of the end of thefiscal year ending in calendar year to the market value of common equity as ofthe end of December of year is the natural log of and are thebook value and market value, respectively, used to calculate , both adjusted toreflect 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 annualcross-sectional Pearson product–moment (below-diagonal entries) and Spearmanrank (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 iscalculated 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 andfuture stock returns Monthly portfolios are formed by sorting all stocks in theCRSP 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 portfoliothat is long the 10th decile portfolio and short the first decile portfolio Newey andWest (1987) -statistics, adjusted using six lags, testing the null hypothesis that theaverage 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 theresults 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 fivegroups 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 arecalculated using all stocks in the CRSP sample Panel A presents the average returnand CAPM alpha (in percent per month) of the long–short zero-cost portfolios thatare long the fifth quintile portfolio and short the first quintile portfolio ineach quintile, as well as for the average quintile, of the control variable Panel Bpresents the average return and CAPM alpha for the average control variable

quintile portfolio within each quintile, as well as for the difference between thefifth 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 Tablepresents the results of bivariate independent-sort portfolio analyses of the relationbetween and future stock returns after controlling for the effect of Eachmonth, 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 arecalculated 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 asfor 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 sixlags, 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 ThisTable presents the results of bivariate independent-sort portfolio analyses of therelation 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 fivegroups based on an ascending sort of The quintile breakpoints used to createthe groups are calculated using all stocks in the CRSP sample The intersections ofthe and groups are used to form 25 portfolios The Table presents theaverage one-month-ahead excess return (in percent per month) for each of the 25portfolios as well as for the average quintile portfolio within each quintile

of and the average quintile within each quintile Also shown are theaverage return and CAPM alpha of a long–short zero-cost portfolio that is long thefifth ( ) 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 thatthe average return or alpha is equal to zero, are shown in parentheses Panel Apresents 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 ofFama and MacBeth (1973) regression analyses of the relation between expected

stock returns and book-to-market ratio Each column in the Table presents resultsfor a different cross-sectional regression specification The dependent variable inall specifications is the one-month-ahead excess stock return The independentvariables are indicated in the first column Independent variables are winsorized atthe 0.5% level on a monthly basis The Table presents average slope and interceptcoefficients along with -statistics (in parentheses), adjusted following Newey andWest (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 variablesmeasuring momentum using the CRSP sample for the months from June 1963through 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 sectional value The column labeled indicates the average number of stocks forwhich the given variable is available in month is the return of the stockduring the 11-month period including months through is the

cross-return of the stock during months through is the return of thestock 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 annualcross-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 allstocks 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 theaverage values of , , , and for stocks in each decile portfolio Panel

B (Panel C) shows the average value-weighted (equal-weighted) one-month-aheadexcess return (in percent per month) for each of the 10 decile portfolios The Tablealso shows the average return of the portfolio that is long the 10th decile portfolioand 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 thenull hypothesis that the average 10-1 portfolio return or alpha is equal to zero, areshown 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 ofmeasures of momentum and future stock returns Monthly portfolios are formed

by sorting all stocks in the CRSP sample into portfolios using decile breakpointscalculated 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 inthe column header), in percent per month, along with the associated FF alpha, ofthe 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 thenull 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 ThisTable 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 CRSPsample The Table presents the average one-month-ahead excess return (in percentper month) for each of the 25 portfolios as well as for the average quintileportfolio within each quintile of Also shown are the average return, CAPMalpha, 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 sixlags, 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 Tablepresents the results of bivariate dependent-sort portfolio analyses of the relationbetween 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 intofive portfolios based on an ascending sort of The Table presents the averageone-month-ahead excess return (in percent per month) for each of the 20

portfolios as well as for the average portfolio within each group Alsoshown 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 thatthe average return or alpha is equal to zero, are shown in parentheses Panel Apresents 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 fivegroups 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 arecalculated using all stocks in the CRSP sample The Table presents the averagereturn, 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 controlvariable 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 averagereturn or alpha is equal to zero, are shown in parentheses

Table 11.8 Bivariate Independent-Sort Portfolio Analysis This Table presents theresults 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 sortedinto five groups based on an ascending sort of the control variable All stocks areindependently sorted into five groups based on an ascending sort of The

quintile breakpoints used to create the groups are calculated using all stocks in theCRSP sample The intersections of the control variable and groups are used toform 25 portfolios The Table presents the average return, CAPM alpha, and FFalpha (in percent per month) of the long–short zero-cost portfolios that are longthe fifth quintile portfolio and short the first quintile portfolio in eachquintile, as well as for the average quintile, of the control variable Results for

value-weighted (Weights = VW) and equal-weighted (Weights = EW) portfolios areshown -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 Tablepresents the results of bivariate independent-sort portfolio analyses of the relationbetween 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 thesecond sort variables The quintile breakpoints used to create the groups are

calculated using all stocks in the CRSP sample The intersections of the andsecond sort variable groups are used to form 25 portfolios The Table presents theaverage 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 thefirst quintile portfolio for the second sort variable in each quintile, as well as forthe 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 ThisTable presents the results of bivariate independent-sort portfolio analyses of therelation between future stock returns and each of , , and (second sortvariables) 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 Allstocks 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 arecalculated using all stocks in the CRSP sample The intersections of the andsecond sort variable groups are used to form 25 portfolios The Table presents theaverage 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 thefirst quintile portfolio for the second sort variable in each quintile, as well as forthe 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 11.11 Fama–MacBeth Regressions This Table presents the results of Famaand MacBeth (1973) regression analyses of the relation between expected stockreturns and momentum Each column in the Table presents results for a differentcross-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 atthe 0.5% level on a monthly basis The Table presents average slope and interceptcoefficients along with -statistics (in parentheses), adjusted following Newey andWest (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 12: Short-Term Reversal

Table 12.1 Summary Statistics This Table presents summary statistics for reversal (), measured as the stock return in month , calculated using the CRSP samplefor 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-sectionaldistribution of each variable is calculated The Table presents the time-series

means for each cross-sectional value The column labeled indicates that averagenumber of stocks for is available

Table 12.2 Correlations This Table presents the time-series averages of the annualcross-sectional Pearson product–moment and Spearman rank correlations

between and each of , , , and

Table 12.3 Univariate Portfolio Analysis This Table presents the results of

univariate portfolio analyses of the relation between reversal and future stock

returns Monthly portfolios are formed by sorting all stocks in the CRSP sampleinto portfolios using decile breakpoints calculated using all stocks in the CRSPsample Panel A shows the average values of , , , , and for

stocks in each decile portfolio Panel B (Panel C) shows the average equal-weighted(value-weighted) one-month-ahead excess return (in percent per month), CAPMalpha, FF alpha, and FFC alpha for each of the 10 decile portfolios as well as for thelong–short zero-cost portfolio that is long the 10th decile portfolio and short thefirst 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 12.4 Univariate Portfolio Analysis—Lagged Values of Reversal This Tablepresents the results of univariate portfolio analyses of the relation between

previous values of reversal and future stock returns Monthly portfolios are formed

by sorting all stocks in the CRSP sample into portfolios using decile breakpointscalculated based on the given sort variable using all stocks in the CRSP sample.The variables used to form portfolios at the end of month are the values of

reversal (the stock return) measured in month ( ) or month ( ) Panel

A (Panel B) shows the average value-weighted (equal-weighted) month excessreturn (in percent per month) for each of the 10 decile portfolios The Table alsoshows the average month return of the portfolio that is long the 10th decileportfolio and short the first decile portfolio, as well as the CAPM, FF, and FFC

alphas for this portfolio Newey and West (1987) -statistics, adjusted using sixlags, testing the null hypothesis that the average 10-1 portfolio return or alpha isequal to zero, are shown in parentheses

Table 12.5 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 CRSPsample The Table presents the average one-month-ahead excess return (in percentper month) for each of the 25 portfolios as well as for the average quintileportfolio within each quintile of Also shown are the average return, CAPMalpha, FF alpha, and FFC alpha of a long–short zero-cost portfolio that is long thefifth 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 12.6 Bivariate Dependent-Sort Portfolio Analysis This Table presents theresults 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 fivegroups 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 arecalculated using all stocks in the CRSP sample The Table presents the averagereturn, CAPM alpha, FF alpha, and FFC alpha (in percent per month) of the long–short zero-cost portfolios that are long the fifth quintile portfolio and short thefirst quintile portfolio in each quintile, as well as for the average quintile, ofthe control variable 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 12.7 Bivariate Independent-Sort Portfolio Analysis This Table presents theresults 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 sortedinto five groups based on an ascending sort of the control variable All stocks areindependently sorted into five groups based on an ascending sort of The

quintile breakpoints used to create the groups are calculated using all stocks in theCRSP sample The intersections of the control variable and groups are used toform 25 portfolios The Table presents the average return and FFC 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, aswell as for the average quintile, of the control variable Results for equal-weighted(Weights = EW) and value-weighted (Weights = VW) portfolios are shown -

statistics (in parentheses), adjusted following Newey and West (1987) using sixlags, testing the null hypothesis that the average return or alpha is equal to zero,are shown in parentheses

Table 12.8 Bivariate Dependent-Sort Portfolio Analysis—Control for This Tablepresents the results of bivariate independent-sort portfolio analyses of the relationbetween future stock returns and each of , , , and (second sortvariables) after controlling for the effect of Each month, all stocks in the CRSPsample are sorted into five groups based on an ascending sort of All stocks areindependently 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 andsecond sort variable groups are used to form 25 portfolios The Table presents theaverage return and FFC alpha (in percent per month) of the long–short zero-costportfolios 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 averagequintile, of 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 thatthe average return or alpha is equal to zero, are shown in parentheses

Table 12.9 Bivariate Independent-Sort Portfolio Analysis—Control for ThisTable presents the results of bivariate independent-sort portfolio analyses of therelation between future stock returns and each of , , , and (secondsort variables) after controlling for the effect of Each month, all stocks in theCRSP sample are sorted into five groups based on an ascending sort of Allstocks 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 arecalculated using all stocks in the CRSP sample The intersections of the andsecond sort variable groups are used to form 25 portfolios The Table presents theaverage return and FFC alpha (in percent per month) of the long–short zero-costportfolios 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 averagequintile, of 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 thatthe average return or alpha is equal to zero, are shown in parentheses

Table 12.10 Fama–MacBeth Regressions Analysis This Table presents the results ofFama and MacBeth (1973) regression analyses of the relation between expectedstock returns and reversal Each column in the Table presents results for a

different cross-sectional regression specification The dependent variable in allspecifications is the one-month-ahead excess stock return The independent

variables are indicated in the first column Independent variables are winsorized atthe 0.5% level on a monthly basis The Table presents average slope and interceptcoefficients along with -statistics (in parentheses), adjusted following Newey andWest (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

Table 12.11 Fama–MacBeth Regression Analysis—Lagged Values of Reversal ThisTable presents the results of Fama and MacBeth (1973) regression analyses of therelation between expected stock returns and previous values of reversal Each

column in the Table presents results for a different cross-sectional regression

specification The dependent variable in all specifications is the one-month-aheadexcess stock return The independent variables are indicated in the first column

is the stock return in month Independent variables are winsorized at the0.5% level on a monthly basis The Table presents average slope and intercept

coefficients along with -statistics (in parentheses), adjusted following Newey andWest (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

Table 12.12 Fama–MacBeth Regression Analysis—Lagged Values of Reversal withControls This Table presents the results of Fama and MacBeth (1973) regressionanalyses of the relation between expected stock returns and previous values ofreversal Each column in the Table presents results for a different cross-sectionalregression specification The dependent variable in all specifications is the one-month-ahead excess stock return The independent variables are indicated in thefirst column is the stock return in month Independent variables arewinsorized at the 0.5% level on a monthly basis The Table presents average slopeand intercept coefficients along with -statistics (in parentheses), adjusted

following Newey and West (1987) using six lags, testing the null hypothesis thatthe average coefficient is equal to zero The rows labeled Adj and present theaverage adjusted -squared and the number of data points, respectively, for thecross-sectional regressions

Chapter 13: Liquidity

Table 13.1 Summary Statistics This Table presents summary statistics for variablesmeasuring illiquidity using the CRSP sample for the months from June 1963through 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 is calculated The Table presents the time-series means for each sectional value The column labeled indicates the average number of stocks forwhich the given variable is available , , , and are the ratiodaily stock return, measured as a decimal, divided by the daily dollar trading

cross-volume, measured in millions of dollars, averaged over one, three, six, and 12

months, respectively , , ln Illiq 6M , and ln Illiq 12M are the natural

logs of Illiq 1M , Illiq 3M , Illiq 6M , and Illiq 12M, respectively

Table 13.2 Correlations This Table presents the time-series averages of the annualcross-sectional Pearson product–moment (below-diagonal entries) and Spearmanrank (above-diagonal entries) correlations between pairs of variables measuringilliquidity as well as , , , , and

Table 13.3 Persistence This Table presents the results of persistence analyses ofvariables measuring illiquidity Each month , the cross-sectional Pearson

product–moment correlation between the month and month values of thegiven variable is calculated The Table presents the time-series averages of themonthly cross-sectional correlations The column labeled indicates the lag atwhich the persistence is measured

Table 13.4 Univariate Portfolio Analysis This Table presents the results of

univariate portfolio analyses of the relation between illiquidity and future stockreturns Monthly portfolios are formed by sorting all stocks in the CRSP sampleinto portfolios using decile breakpoints calculated based on the given sort variableusing all stocks in the CRSP sample Panel A shows the average values of ,

, , , , , and for stocks in each decile portfolio.Panel B (Panel D) shows the average equal-weighted (value-weighted) one-month-ahead excess return (in percent per month) for each of the 10 decile portfoliosformed using different measures of illiquidity as the sort variable Panel C showsthe average equal-weighted one-month-ahead non-delisting-adjusted excess return(in percent per month), for each of the 10 decile portfolios formed using differentmeasures of illiquidity as the sort variable Panels B–D also show the average

return of the portfolio that is long the 10th decile portfolio and short the first

decile portfolio, as well as the FFC and FFCSTR alphas for this portfolio Panel Eshows the FFCSTR alpha as well as factor sensitivities for decile portfolios formed

by sorting on Newey and West (1987) -statistics, adjusted using six lags,testing the null hypothesis that the average 10-1 portfolio return, alpha, or factorsensitivity is equal to zero, are shown in parentheses

Table 13.5 Bivariate Dependent-Sort Portfolio Analysis—Control for ThisTable presents the results of bivariate dependent-sort portfolio analyses of therelation 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 CRSPsample Panel A presents the average one-month-ahead excess return (in percentper month) for each of the 25 equal-weighted portfolios as well as for the average

quintile portfolio within each quintile of Also shown are the averagereturn, FFC alpha, and FFCSTR alpha of a long–short zero-cost portfolio that islong the fifth quintile portfolio and short the first quintile portfolio ineach quintile -statistics (in parentheses), adjusted following Newey andWest (1987) using six lags, testing the null hypothesis that the average return oralpha is equal to zero, are shown in parentheses Panel B presents the average

values of for stocks in each of the portfolios

Table 13.6 Bivariate Dependent-Sort Portfolio Analysis This Table presents theresults 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 aresorted 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 presentsthe average return, FFC alpha, and FFCSTRL alpha (in percent per month) of thelong–short zero-cost portfolios that are long the fifth quintile portfolio andshort the first quintile portfolio in each quintile, as well as for the averagequintile, of the control variable Results for equal-weighted (Weights = EW) andvalue-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

stocks are independently sorted into five groups based on an ascending sort of

The quintile breakpoints used to create the groups are calculated using allstocks in the CRSP sample The intersections of the control variable and

groups are used to form 25 portfolios The Table presents the average return, FFCalpha, and FFCSTR 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 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 averagereturn or alpha is equal to zero, are shown in parentheses

Table 13.8 Bivariate Dependent-Sort Portfolio Analysis—Control for ThisTable presents the results of bivariate independent-sort portfolio analyses of therelation between future stock returns and each of , , , and (secondsort variables) after controlling for the effect of Each month, all stocks inthe 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 ofone of the second sort variables The quintile breakpoints used to create the groupsare 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 presentsthe average return, FFC alpha, and FFCSTR alpha (in percent per month) of thelong–short zero-cost portfolios that are long the fifth quintile portfolio and shortthe first quintile portfolio for the second sort variable in each quintile, as well asfor the average quintile, of Results for equal-weighted (Weights = EW) andvalue-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 13.9 Bivariate Independent-Sort Portfolio Analysis—Control for ThisTable presents the results of bivariate independent-sort portfolio analyses of therelation between future stock returns and each of , , , and (secondsort variables) after controlling for the effect of Each month, all stocks inthe 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 ofone of the second sort variables The quintile breakpoints used to create the groupsare 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 presentsthe average return, FFC alpha, and FFCSTR alpha (in percent per month) of thelong–short zero-cost portfolios that are long the fifth quintile portfolio and shortthe first quintile portfolio for the second sort variable in each quintile, as well asfor the average quintile, of Results for equal-weighted (Weights = EW) andvalue-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 13.10 Fama–MacBeth Regression Analysis— and This Tablepresents the results of Fama and MacBeth (1973) regression analyses of the

relation between expected stock returns and each of (Panel A) and

(Panel B) Each column in the Table presents results for a different cross-sectionalregression specification The dependent variable in all specifications is the one-month-ahead excess stock return The independent variables are indicated in thefirst column Independent variables are winsorized at the 0.5% level on a monthlybasis The Table presents average slope and intercept coefficients along with -statistics (in parentheses), adjusted following Newey and West (1987) using sixlags, testing the null hypothesis that the average coefficient is equal to zero Therows labeled Adj and present the average adjusted -squared and the number

of data points, respectively, for the cross-sectional regressions

Table 13.11 Fama–MacBeth Regression Analysis— and This Table

presents the results of Fama and MacBeth (1973) regression analyses of the

relation between expected stock returns and illiquidity Each column in the Tablepresents 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 Independentvariables are winsorized at the 0.5% level on a monthly basis The Table presentsaverage 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 14: Skewness

Table 14.1 Summary Statistics This Table presents summary statistics for variablesmeasuring total skewness (Panel A), co-skewness (Panel B), and idiosyncratic

skewness (Panel C), 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 is calculated The Table presents the time-series means for each sectional value The column labeled indicates the average number of stocks forwhich the given variable is available , , , and are theskewness of daily stock returns calculated using one, three, six, and 12 months ofdaily return data , , , and are the skewness of monthlystock returns calculated using one, two, three, and five years worth of monthlyreturn data , , , and are calculated as the slopecoefficient on the excess market return squared term from a regression of excessstock returns on the excess market return and the excess market return squaredusing one, three, six, and 12 months of daily return data , ,

cross-, and are calculated as the slope coefficient on the excess market

return squared term from a regression of excess stock returns on the excess

market return and the excess market return squared using one, two, three, and five

calculated as the skewness of the residuals from a regression of excess stock

returns on the excess market return, the return of the size ( ) factor, and thereturn of the value ( ) factor using one, three, six, and 12 months of daily

skewness of the residuals from a regression of excess stock returns on the excessmarket return, the return of the size ( ) factor, and the return of the value () factor using one, two, three, and five years of monthly return data

Table 14.2 Correlations—Total Skewness This Table presents the time-series

averages of the annual cross-sectional Pearson product–moment (below-diagonalentries) and Spearman rank (above-diagonal entries) correlations between pairs ofvariables measuring total skewness

Table 14.3 Correlations—Co-Skewness 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 co-skewness

Table 14.4 Correlations—Idiosyncratic Skewness This Table presents the

time-series averages of the annual cross-sectional Pearson product–moment

(below-diagonal entries) and Spearman rank (above-diagonal entries) correlationsbetween pairs of variables measuring idiosyncratic skewness

Table 14.5 Correlations—Total, Co-, and Idiosyncratic Skewness This Table

presents the time-series averages of the annual cross-sectional Pearson product–moment (Panel A) and Spearman rank (Panel B) correlations between pairs ofvariables measuring total skewness ( ), co-skewness ( ), and

idiosyncratic skewness ( ) calculated using different data frequencies andmeasurement period lengths Correlations between variables calculated from one,three, six, and 12 months of daily return data are shown in the columns , , , and , respectively Correlations between variables calculated from one,two, three, and five months of monthly return data are shown in the columns , , , and , respectively

Table 14.6 Correlations—Skewness and Other Variables This Table presents thetime-series averages of the annual cross-sectional Pearson product-moment

(below-diagonal entries) and Spearman rank (above-diagonal entries) correlationsbetween pairs of variables measuring idiosyncratic volatility

Table 14.7 Persistence—Co-Skewness This Table presents the results of persistenceanalyses of variables measuring co-skewness Each month , the cross-sectionalPearson product–moment correlation between the month and month values

of the given variable measured 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 14.8 Persistence—Idiosyncratic Skewness This Table presents the results ofpersistence analyses of variables measuring idiosyncratic skewness Each month ,the cross-sectional Pearson product–moment correlation between the month andmonth values of the given variable measured is calculated The Table presentsthe time-series averages of the monthly cross-sectional correlations The columnlabeled indicates the lag at which the persistence is measured

Table 14.9 Persistence—Total Skewness This Table presents the results of

persistence analyses of variables measuring total skewness Each month , thecross-sectional Pearson product–moment correlation between the month andmonth values of the given variable measured is calculated The Table presentsthe time-series averages of the monthly cross-sectional correlations The columnlabeled indicates the lag at which the persistence is measured

Table 14.10 Univariate Portfolio Analysis—Total Skewness This Table presents theresults of univariate portfolio analyses of the relation between total skewness andfuture stock returns Monthly portfolios are formed by sorting all stocks in theCRSP sample into portfolios using decile breakpoints calculated based on the givensort variable using all stocks in the CRSP sample Panel A (Panel B) shows the

average equal-weighted (value-weighted) one-month-ahead excess return (in

percent per month) for each of the 10 decile portfolios formed using different

measures of total skewness as the sort variable The Table also shows the averagereturn of the portfolio that is long the 10th decile portfolio and short the first

decile portfolio, as well as the FFC and FFCPS alphas for this portfolio Newey andWest (1987) -statistics, adjusted using six lags, testing the null hypothesis that theaverage 10-1 portfolio return or alpha is equal to zero, are shown in parenthesesTable 14.11 Univariate Portfolio Analysis—Co-Skewness This Table presents theresults of univariate portfolio analyses of the relation between co-skewness andfuture stock returns Monthly portfolios are formed by sorting all stocks in theCRSP sample into portfolios using decile breakpoints calculated based on the givensort variable using all stocks in the CRSP sample Panel A (Panel B) shows the

average equal-weighted (value-weighted) one-month-ahead excess return (in

percent per month) for each of the 10 decile portfolios formed using different

measures of co-skewness as the sort variable The Table also shows the averagereturn of the portfolio that is long the 10th decile portfolio and short the first

decile portfolio, as well as the FFC and FFCPS alphas for this portfolio Newey andWest (1987) -statistics, adjusted using six lags, testing the null hypothesis that theaverage 10-1 portfolio return or alpha is equal to zero, are shown in parenthesesTable 14.12 Univariate Portfolio Analysis—Idiosyncratic Skewness This Table

presents the results of univariate portfolio analyses of the relation between

idiosyncratic skewness 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 (Panel B) shows the average equal-weighted (value-weighted) one-month-ahead excess return (in percent per month) for each of the 10 decile portfolios

formed using different measures of idiosyncratic skewness as the sort variable TheTable also shows the average return of the portfolio that is long the 10th decileportfolio and short the first decile portfolio, as well as the FFC and FFCPS alphasfor 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 14.13 Fama–MacBeth Regression Analysis— This Table presents theresults of Fama and MacBeth (1973) regression analyses of the relation betweenexpected stock returns and total skewness 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 arewinsorized at the 0.5% level on a monthly basis The Table presents average slopeand intercept coefficients along with -statistics (in parentheses), adjusted

following Newey and West (1987) using six lags, testing the null hypothesis thatthe average coefficient is equal to zero The rows labeled Adj and present theaverage adjusted -squared and the number of data points, respectively, for thecross-sectional regressions Results from univariate (multivariate) specificationsare shown in Panel A (Panel B)

Table 14.14 Fama–MacBeth Regression Analysis— This Table presents theresults of Fama and MacBeth (1973) regression analyses of the relation betweenexpected stock returns and co-skewness Each column in the Table presents resultsfor a different cross-sectional regression specification The dependent variable inall specifications is the one-month-ahead excess stock return The independentvariables are indicated in the first column Independent variables are winsorized atthe 0.5% level on a monthly basis The Table presents average slope and interceptcoefficients along with -statistics (in parentheses), adjusted following Newey andWest (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 Results from univariate (multivariate) specifications are shown inPanel A (Panel B)

Table 14.15 Fama–MacBeth Regression Analysis— This Table presents theresults of Fama and MacBeth (1973) regression analyses of the relation betweenexpected stock returns and idiosyncratic skewness 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 Independentvariables are winsorized at the 0.5% level on a monthly basis The Table presentsaverage 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 Results from univariate

(multivariate) specifications are shown in Panel A (Panel B)

Chapter 15: Idiosyncratic Volatility

Table 15.1 Summary Statistics This Table presents summary statistics for

variables measuring total and idiosyncratic volatility using the CRSP sample forthe months from June 1963 through November 2012 Each month, the mean (), standard deviation ( ), skewness ( ), excess kurtosis ( ), minimum( ), 5th percentile (5%), 25th percentile (25%), median ( ), 75th percentile(75%), 95th percentile (95%), and maximum ( ) values of the cross-sectionaldistribution of each variable is calculated The Table presents the time-series

means for each cross-sectional value The column labeled indicates the averagenumber of stocks for which the given variable is available is the annualizedstandard deviation of periodic stock returns is the annualized sum of the

squared periodic stock returns is the annualized standard deviation ofthe residuals from a regression of excess stock returns on the market factor

is the annualized standard deviation of the residuals from a regression ofexcess stock returns on the market factor, the size factor ( ), and the value

factor ( ) is the annualized standard deviation of the residuals from aregression of excess stock returns on the market factor, the size factor ( ), thevalue factor ( ), and the momentum factor ( ) Variables denoted , , , and are calculated from one, three, six, and 12 months of daily return data,respectively Variables denoted , , , and are calculated from one, two,three, and five years of monthly return data, respectively

Table 15.2 Correlations—Total Volatility This Table presents the time-series

averages of the annual cross-sectional Pearson product–moment (below-diagonalentries) and Spearman rank (above-diagonal entries) correlations between pairs ofvariables measuring total volatility Panel A presents correlations between values

of calculated using different data frequencies and measurement period lengths.Panel B presents correlations between values of calculated using differentdata frequencies and measurement period lengths

Table 15.3 Correlations—Idiosyncratic Volatility This Table presents the

time-series averages of the annual cross-sectional Pearson product–moment

(below-diagonal entries) and Spearman rank (above-diagonal entries) correlationsbetween pairs of variables measuring idiosyncratic volatility Panel A presents

correlations between values of calculated using different data

frequencies and measurement period lengths Panel B presents correlations

between values of calculated using different data frequencies and

measurement period lengths Panel C presents correlations between values of

calculated using different data frequencies and measurement period

lengths

Table 15.4 Correlations—Total and Idiosyncratic Volatility This Table

presents the time-series averages of the annual cross-sectional Pearson product–moment (Panel A) and Spearman rank (Panel B) correlations between pairs ofvariables measuring total volatility ( and ) and idiosyncratic volatility (

, , and ) calculated using different data frequencies andmeasurement period lengths Correlations between variables calculated from one,three, six, and 12 months of daily return data are shown in the columns , , , and , respectively Correlations between variables calculated from one,two, three, and five months of monthly return data are shown in the columns , , , and , respectively

Table 15.5 Correlations—Idiosyncratic Volatility and Other Variables This

Table presents the time-series averages of the annual cross-sectional Pearson

product–moment (Panel A) and Spearman rank (Panel B) correlations betweenpairs of variables measuring idiosyncratic volatility and each of , , , , , , , and

Table 15.6 Persistence This Table presents the results of persistence analyses of

variables measuring idiosyncratic volatility Each month , the cross-sectional

Pearson product–moment correlation between the month and month values

of the given variable measured is calculated The Table presents the time-seriesaverages of the monthly cross-sectional correlations The column labeled

indicates the lag at which the persistence is measured

Table 15.7 Univariate Portfolio Analysis This Table presents the results of

univariate portfolio analyses of the relation between idiosyncratic volatility andfuture stock returns Monthly portfolios are formed by sorting all stocks in theCRSP sample into portfolios decile breakpoints calculated based on the given sortvariable using all stocks in the CRSP sample Panel A (Panel B) shows the averagevalue-weighted (equal-weighted) one-month-ahead excess return (in percent permonth) for each of the 10 decile portfolios formed using different measures ofidiosyncratic volatility as the sort variable 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 FFC and FFCPS alphas 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 15.8 Univariate Portfolio Analysis—Unadjusted Returns This Table