This study evaluates the impact of investor mood and mood contagion on the cross-section of Australian stock returns . While we document a day-of-the-week effect in Australia, there are noticeable differences to the US (Birru, 2018). Australian stock returns on Tuesday are significantly lower than other weekdays. This ‘Tuesday Blues’ effect is most prominent amongst speculative stocks which, being more difficult to value, are more likely to be influenced by investor mood. We further report that Australian speculative stock returns are more correlated with factors indicative of same-day domestic mood and lagged US mood. Exploiting the 14-hour difference in time zones, we find that Tuesday returns to Australian speculative stocks are lower following extremely negative Monday mood or high risk aversion in the US. Overall, our results are consistent with mood contagion across international markets, which contributes to the observed daily patterns in stock returns after recognizing differences in time zones.

This paper studies the role that risk and mispricing play in the negative relation between extreme positive returns and future returns. We document a strong ‘MAX effect’ in Australian equities over 1991–2013 that is robust to risk adjustment, controlling for other influential stock characteristics and, importantly, manifests in a partition of the 500 largest stocks. While there is no evidence that MAX proxies for sensitivity to risk, the findings are highly consistent with a mispricing explanation. Adapting the recent methodological innovation of Stambaugh et al. (2015) to classify stocks by their degree of mispricing, we show that the MAX effect concentrates amongst the most-overpriced stocks but actually reverses amongst the most-underpriced stocks. Consistent with arbitrage asymmetry, the magnitude of the MAX effect amongst overpriced stocks exceeds that amongst underpriced stocks, leading to the overall negative relation that has been well documented.

This study employs a broad set of machine learning (ML) methods to examine cross-sectional variation in corporate bond returns in China. Using macroeconomic indicators together with bond- and issuer-specific characteristics, we find that ML techniques outperform traditional linear models in both statistical and economic terms. These models are particularly effective at capturing distinctive features of the Chinese market, including the dominance of state-owned enterprises, implicit government guarantees, and rapid market evolution. We compare long-short and long-only portfolio strategies to account for practical constraints on short selling. The results indicate that ML methods are effective in markets where institutional features and information asymmetries play a central role in asset pricing.

The first 100 days of a newly-elected President's administration are often a period of substantial and concentrated policy change. This paper shows that measures of uncertainty and risk aversion rise sharply during Presidential honeymoons. Consistent with theoretical models that suggest that investors demand compensation for bearing heightened political risk, we document striking spread returns to value, investment and profitability anomalies during honeymoons. For example, the book-to-market value premium averages 3.51% per month during Presidential honeymoons, yet only 0.27% per month at other times. These findings survive numerous robustness checks. Nonetheless, establishing a direct link between escalating political risk and equity returns proves challenging.