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Research summary:We provide evidence that founder chief executive officers (CEOs) of large S&P 1500 companies are more overconfident than their nonfounder counterparts ("professional CEOs"). We measure overconfidence via tone of CEO tweets, tone of CEO statements during earnings conference calls, management earnings forecasts, and CEO option-exercise behavior. Compared with professional CEOs, founder CEOs use more optimistic language on Twitter and during earnings conference calls. In addition, founder CEOs are more likely to issue earnings forecasts that are too high; they are also more likely to perceive their firms to be undervalued, as implied by their option-exercise behavior. We provide evidence that, to date, investors appear unaware of this "overconfidence bias" among founders. Managerial summary:This article helps to explain why firms managed by founder chief executive officers (CEOs) behave differently from those managed by professional CEOs. We study a sample of S&P 1500 firms and find strong evidence that founder CEOs are more overconfident than professional CEOs. To date, investors appear unaware of this overconfidence bias among founders. Our study should help firm stakeholders, including investors, employees, suppliers, and customers, put the statements and actions of founder CEOs in perspective. Our study should also help members of corporate boards make more informed decisions about whether to retain (or bring back) founder CEOs or hire professional CEOs.

Does fake news in financial markets attract more investor attention and have a significant impact on stock prices? The authors use the SEC crackdown of stock promotion schemes in April 2017 to examine investor attention and the stock price reaction to fake news articles. Using data from Seeking Alpha, the authors find that fake news stories generate significantly more attention than a control sample of legitimate articles. The authors find no evidence that article commenters can detect fake news, and they find that Seeking Alpha editors have only modest ability to detect fake news. However, the authors implement several well-known machine learning algorithms based on linguistic characteristics and show that machine learning algorithms can successfully identify fake news. In addition, the stock market appears to price fake news correctly. While abnormal trading volume increases around the release of fake news, the increase is less than that observed for legitimate news. The stock price reaction to fake news is discounted when compared with legitimate news articles.

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