Roland Gillet
Roland Gillet holds a PhD in Economics from Catholic University of Louvain (UCL) conducted under the supervision of Alexis Jacquemin (Francqui Prize, 1983) and Franco Modigliani (Nobel Prize in Economics, 1985). He was major (ranked first) of the French national competitive examination for university higher education professors in Management sciences (1999). He is currently Professor of finance at University Paris 1 Panthéon-Sorbonne where he is Director of the Master’s degree “Financial Management and Taxation”. He is also Professor of financial economics at Solvay Brussels School of Economics and Management of Free University of Brussels (ULB).
He is/was visiting professor and/or research fellow in several universities worldwide: notably at University of Sherbrooke (Canada), Fudan University (China, Shangai), Harvard University and M.I.T. (USA).
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.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
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.