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We build a game-theoretic model to examine how better demand forecasting due to algorithms, machine learning and artificial intelligence affects the sustainability of collusion in an industry. We find that while better forecasting allows colluding firms to better tailor prices to demand...
Persistent link: https://www.econbiz.de/10012910026
We produce a social unrest risk index for 125 countries covering a period of 1996 to 2020. The risk of social unrest is based on the probability of unrest in the following year derived from a machine learning model drawing on over 340 indicators covering a wide range of macro-financial,...
Persistent link: https://www.econbiz.de/10013306728
Predictive AI is increasingly used to guide decisions on agents. I show that even a bias-neutral predictive AI can potentially amplify exogenous (human) bias in settings where the predictive AI represents a cost-adjusted precision gain to unbiased predictions, and the final judgments are made by...
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The paper provides a cost-based explanation for decision makers' reluctance to use fraud prediction models …, particularly as these models have nearly doubled their success at identifying fraud (true positive rates) when compared to the … initial models in Beneish (1997, 1999). We estimate the costs of fraud prediction errors from the perspective of auditors …
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We test the predictability of investment fraud using a panel of mandatory disclosures filed with the SEC. We find that … predict fraud. Avoiding the 5% of firms with the highest ex ante predicted fraud risk would allow an investor to avoid 29% of … fraud cases and over 40% of the total dollar losses from fraud. We find no evidence that investors receive compensation for …
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