Using Explainable AI to Understand Bond Excess Returns

Recent empirical evidence indicates that bond excess returns can be predicted using machine learning models. While the predictive power of machine learning models is intriguing, they typically lack transparency. We introduce SHapley Additive exPlanations (SHAP), a state-of-the-art explainable artificial technique, to open the black box of these models. Our analysis identifies the key determinants that drive the predictions of bond excess returns in machine learning models and how these determinants are related to bond excess returns. Thereby, our approach facilitates an in-depth interpretation of the predictions of bond excess returns made by machine learning models

An Institutional Perspective on Digital Transformation

In this study, we argue and show that firms’ and industries’ digital transformations follow an institutional logic. In particular, we suggest that the information technology portfolios of firms within an industry and among industries will become more similar over time, as firms seek to secure legitimacy among stakeholders. We introduce institutional intuition as the critical attribute of firms that enables them to successfully depart from the industry’s information technology portfolio. To test our theorizing, we conducted a textual analysis of the 10k filings of all S&P 1500 firms from 1993 to 2018 building quantitative, longitudinal measures for different facets of digital transformation. We find that firms with greater industry similarity achieve higher performance. Yet, firms that diverge from the industry and have the right institutional intuition can break out of this logic and achieve superior performance

Insurance Fraud and Isolation Forests

Fraud is a significant issue for insurers. Previous literature has mainly used supervised learning to detect insurance fraud. However, supervised learning must deal with significant difficulties in fraud detection, such as very few cases being labeled as fraud and overfitting to the outcomes of pre-existing fraud detection systems, which can lead to overlooking new fraud patterns. Unsupervised learning methods producing anomaly scores could be a remedy to improve insurance fraud detection systems. However, unsupervised learning must identify anomalies that are conceptionally meaningful for fraud. In this paper, we suggest a theoretical framework for choosing features to include in fraud detection models. We evaluate this framework using isolation forests for anomaly detection based on more than 32,000 automobile insurance claims. We further evaluate textual information based on concepts from deception detection in computational linguistics using straightforward cluster methods and state-of-the-art transformers

Peter Benson Tobacco, Capitalism. Growers, Migrant Workers, and the Changing Face of a Global Industry

Peter Benson, Tobacco Capitalism. Growers, Migrant Workers, and the Changing Face of a Global Industry. Princeton, Princeton University Press, 2012, 323 p. Assistant professeur à l’Université Washington de Saint-Louis (Missouri), Peter Benson envisage les mutations de la culture du tabac aux États-Unis à partir de l’exemple de la Caroline du Nord. L’impact des politiques anti-tabac est au cœur du propos, sous l’angle de la redéfinition des identités rurales en particulier. En filigrane de l’..