Motivated by existing evidence of a preference among investors for assets with lottery-like payoffs and that many investors are poorly diversified, we investigate the significance of extreme positive returns in the cross-sectional pricing of stocks. Portfolio-level analyses and firm-level...

People are more willing to bet on their own judgments when they feel skillful or knowledgeable (Heath and Tversky (1991)). We investigate whether this "competence effect" influences trading frequency and home bias. We find that investors who feel competent trade more often and have a more...

Traditional mean-variance efficient portfolios do not capture the potential wealth creation opportunities provided by predictability of asset returns. We propose a simple method for constructing optimally managed portfolios that exploits the possibility that asset returns are predictable. We...

We analyze the advice contained in a sample of 237 investment letters over the 1980-1992 period. Each newsletter recommends a mix of equity and cash. We construct portfolios based on these recommendations and find that only a small number of the newsletters appear to have higher average returns...

Within the context of conditional asset allocation strategies, this paper explores the implications of the low correlations of the emerging market returns with developed market returns and the relatively high degree predictability of emerging countries' returns. It is well known that low...

This paper empirically examines multifactor asset pricing models for the returns and expected returns on eighteen national equity markets. The factors are chosen to measure global economic risks. Although previous studies do not reject the unconditional mean- variance efficiency of a world...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...