That's Interesting

  • Quadrophobia: Strategic Rounding of EPS Data

    Managers’ incentives to round up reported earnings per share (EPS) cause an underrepresentation of the number 4 in the first post-decimal digit of EPS, or “quadrophobia.” This article has developed a novel measure of aggressive financial reporting practices based on a firm’s history of quadrophobia. Quadrophobia is pervasive, persistent, and successfully predicts future restatements, Securities and Exchange Commission enforcement actions, and class action litigation.

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  • The Moderating Effect of CEO Power on the Board Composition–Firm Performance Relationship

    Prior studies of the relationship between the composition of boards of directors and firm performance offer equivocal results. Drawing on agency and power circulation theories, this article attempts to reduce this equivocality by asserting that CEO power moderates the relationship. Specifically, an outside director dominated board is needed to check a powerful CEO, but monitoring by other executives provides sufficient constraints on CEOs with low power.

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  • Drawing Up the Bill: Is ESG Related to Stock Returns Around the World?

    This article aims to provide the most comprehensive analysis to date of the relation between ESG ratings and stock returns, using 16,000+ stocks in 48 countries and seven different ESG rating providers. The article finds very little evidence that ESG ratings are related to global stock returns over 2001-2020.

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  • OECD Corporate Governance Factbook 2021

    Between 2005 and 2020, according to the OECD, almost 30,000 companies delisted from global markets via conventional takeovers, share buybacks and leverage buyouts. Over most of that period delistings were not matched by new issues so there was a net loss of listed companies, mainly in the US and Europe.

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  • All the Metals We Mined in One Visualization

    This infographic visualizes the 2.8 billion tonnes of metals mined in 2022, including technology metals, precious metals, and more.

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  • From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI

    This article explores the value of generative AI tools, such as ChatGPT, in helping investors uncover dimensions of corporate risk by developing and validating firm-level measures of risk exposure to political, climate, and AI-related risks. Using the GPT 3.5 model to generate risk summaries and assessments from the context provided by earnings call transcripts, it shows that GPT-based measures possess significant information content and outperform the existing risk measures in predicting (abnormal) firm-level volatility and firms’ choices such as investment and innovation.

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  • Mental Models of the Stock Market

    Investors’ return expectations are pivotal in stock markets, but the reasoning behind these expectations remains a black box for economists. This paper sheds light on economic agents’ mental models – their subjective understanding – of the stock market, drawing on surveys with the US general population, US retail investors, US financial professionals, and academic experts.

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  • Greenwashing: Do Investors, Markets and Boards Really Care?

    What are the financial repercussions of corporate greenwashing? To answer this question, this article focuses on the impact of such ethically flawed practices on corporate stock market performance.

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  • Behavioral Machine Learning? Computer Predictions of Corporate Earnings also Overreact

    There is considerable evidence that machine learning algorithms have better predictive abilities than humans in various financial settings. But, the literature has not tested whether these algorithmic predictions are more rational than human predictions.

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  • Not even the machines are rational

    For 50 years, behavioural economics has thrown the gauntlet at the rational expectation hypothesis and the concept of homo economicus. But now, rationality could fight back. AI and machine learning algorithms have become so powerful that their forecasts can compete with analyst forecasts (at least before transaction costs) and these algorithms certainly aren’t biased like humans are. Or are they?

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