In these essays, I identify the effects of earnings seasons (i.e., the clustering of earnings releases), on stock market liquidity and asset pricing. In the first essay, I document strong seasonal regularities associated with aggregate earnings announcements. Applying the large body of literature linking earnings announcements to liquidity effects, I argue that these earnings seasons create market-wide liquidity shocks and I show that both liquidity betas and liquidity risk change during earnings seasons In the second essay, I test the impact of earnings seasons on commonality in liquidity as measured by both spreads and depths. I find that commonality significantly decreases during the four weeks of each calendar quarter when most companies release their earnings. These findings contribute to the literature by identifying and examining the clustering effect of firm-specific information on commonality in liquidity. In the third essay, I extend the study of the liquidity effects of earnings seasons to a sample of 20 countries. I find that the international data corroborate both hypotheses. I also find that the aggregate quality of accounting information, and the duration and frequency of interim reporting periods are important determinants of the liquidity effects (both liquidity betas and commonality in liquidity) during earnings seasons.

Mots-clés de l'auteur: Return Predictability ; Return Seasonality ; Asset Pricing Anomalies ; Intraday Returns ; Liquidity ; Infrequent Rebalancing.

Essay 3 investigates the relation between proxies for investor sentiment and stock market crises and recoveries on international indices. Using an Early-Warning-System (EWS) model, the essay examines whether investor sentiment is a useful predictor for the occurrence of stock market crises and early signs of recovery. Three alternative proxies are used to measure investor sentiment, including previously cited measures of stock market riskiness, investors' risk aversion and investors' optimism about stock markets. The results show that investor sentiment is overall a significant predictor of the occurrence of crises within a one year period, and that the addition of sentiment into early warning signal models of stock market crises can improve the predictive performance of the model (increases in investor sentiment increase the probability of occurrence of a crisis, which is in line with previous contributions finding a negative lead-lag relation between sentiment and stock returns). The extension of the model to early signs of recoveries also shows that sentiment is a reliable predictor. The measure of stock market riskiness (Baker and Wurgler, 2006) is found to be a better predictor than the Volatility Index (VIX) and the Put-to-Call Ratio (PCR). The cross-country comparison results confirms the literature findings that the link between sentiment and stock market returns varies across indices and cultures, as the predictive power of the variable appears strongest in the French and U.S. indices.

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