Risk, Return, and Equilibrium: Empirical Tests

Abstract

This paper tests the relationship between average return and risk for New York Stock Exchange common stocks. T h e theoretical basis of the tests is the "two-parameter" portfolio model and models of market equilibrium derived from the two-parameter portfolio model. W e canno! reject the hypothesis of these models that the pricing of common stocks reflects the attempts of risk-averse investors to hold portfolios that are "efficient" in terms of expected value and dispersion of return. Moreover, the observed "fair game" properties of the coefficients and residuals of the risk-return regressions are consistent with an "efficient capital market1'-that is, a market where prices of securities fully reflect available information.

Do Investment Flows Drive the Disposition Effect on Fund Managers?

“…The independent variables are the lagged flows, QF(t-k), and the lagged fund's returns, R(t-h). Model (1) and (2) are cross-sectional regressions, which are run quarterly with the method presented in Fama and MacBeth (1973), and then obtain the time-series average of coefficients and the standard errors adjusted for heteroskedasticity and autocorrelations using GMM correction. Pooled Data regression is run by OLS.…”

“…Models (1), (2), (3) are analyzed by using quarterly data, models (4) and (5) are analyzed by using yearly frequencies. These cross-sectional regressions are run for each time period, as in Fama and MacBeth (1973). We obtain the time-series average of coefficients and the standard errors are adjusted for heteroskedasticity and autocorrelations using GMM correction.…”

“…We also include some control variables including the log of total net asset (LNTNA), front-end load fee (FRONTLOAD), rear-load fee (REARLOAD), fund expenses (EXPENS), and turnover ratio (TURN). These cross-sectional regressions are run quarterly, as in Fama and MacBeth (1973). We obtain the time-series average of coefficients and the standard errors are adjusted for heteroskedasticity and autocorrelations using GMM correction.…”

“…In our analysis of the determinants of DE, we use the regression model presented in Fama and MacBeth (1973) with adjustment of residual autocorrelation. The results show that concurrent and lagged flow ratios negatively correlate with DE, which indicates that the behavioral bias consistently destabilizes the trading strategy when there is outflow.…”

Have We Solved the Idiosyncratic Volatility Puzzle?

“…To summarize our methodology, we start from Fama and MacBeth (1973) cross-sectional regressions of individual month t stock returns on month t-1 idiosyncratic volatility. We find, as many papers do, that the estimated regression coefficient, which we denote as , is on average negative and highly statistically significant.…”