This paper aims to test whether equity returns are predictable over various horizons. We propose a reliable and powerful nonparametric test to examine the predictability of equity returns, which can be interpreted as a signal-to-noise ratio test. Our comprehensive in-sample and out-of-sample...

In this paper, we extend Bossaerts' (2004) analysis of the implications of the efficient learning market hypothesis (ELM) for asset prices by reformulating it in a GMM setting. Our representation is more amenable to widespread application and allows the econometrician, in testing ELM, to make...

We present a framework for modeling and estimating dynamics of variance and skewness from time-series data using a maximum likelihood approach assuming that the errors from the mean have a non-central conditional t distribution. We parameterize conditional variance and conditional skewness in an...

If asset returns have systematic skewness, expected returns should include rewards for accepting this risk. We formalize this intuition with an asset pricing model which incorporates conditional skewness. Our results show that conditional skewness helps explain the cross-sectional variation of...

Much attention is paid to portfolio variance, but skewness is also important for both portfolio design and asset pricing. We revisit the empirical research on systematic skewness that we initiated 25 years ago. In an out-of-sample test, we find that the risk premium associated with skewness is...

We examine a sample of 8,313 cases, between 1951 and 2001, where firms unexpectedly increase their research and development expenditures (Ramp;D) by a significant amount. We find consistent evidence of a mis-reaction, as manifested in the significantly positive abnormal stock returns that our...