Publications

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This article provides a survey of the main results contained in my doctoral dissertation devoted to the informational efficiency of the stock market. The thesis deals not only with the empirical verification of the informational market efficiency hypothesis, but also addresses a limited number of issues related to theoretical implications of this hypothesis in the modelling context of stock behaviour and professional portfolio management.

We investigate the efficient market hypothesis at the intraday level by analyzing market reactions to negative tweets and reports published on the Internet by an activist short seller. Conducting event studies, we find that fast-moving traders can generate small, albeit significant, abnormal profit by trading on public information published on social media. The market reaction to tweets is stronger when a company is mentioned for the first time on Twitter, showing that investors can disentangle new information from noise in real time. We also find that traders who manage to identify the information on the short seller’s website before the dissemination of the same news on Twitter can generate much greater abnormal returns. As acquiring information on a website is more costly and difficult than acquiring the same information on Twitter, our findings provide empirical evidence supporting the Grossman–Stiglitz paradox at the intraday level. Very short-lived market anomalies do exist in the stock market to compensate investors who spent time and money in setting up bots and algorithms to trade on new information before the crowd.

This study investigates the informational content of extra-financial agency scoring by examining the relationship between firm beta and extra-financial performance score upgrades and downgrades. Specifically, we study the variations in the extra-financial score of 266 Canadian corporations between 2007 and 2012 with a conditional model. We find no evidence that changes in firm beta precede changes in extra-financial scores. Rather, our results suggest that a firm's systematic risk increases following a downgrade of its extra-financial performance. In terms of score upgrades, the overall effect is not significant. However, score upgrades for firms with already-high scores predict higher systematic risk, while score upgrades for firms with low scores predict lower systematic risk. These results suggest that extra-financial scores are informational and can be useful to portfolio managers, notably for their risk management strategies.

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Bayesian network theory is used to construct a novel probability-based measure for CEO overconfidence. This measure is estimated by studying the probabilistic correlation between CEO overconfidence and several CEO- and firm-specific determinants of overconfidence, that have been documented in the literature. Using S&P 500 firms over the period 2007–2017, we show that the established Bayesian network model has a high fitting and prediction accuracy of CEO overconfidence. This novel measure of CEO overconfidence can be used to conduct empirical studies in corporate and behavioral finance. It also provides a tool to improve decision-making in firms and corporate governance.