AI adoption proxies from text and patents improve out-of-sample distress prediction in Chinese firms when machine learning models use temporally pruned recent training windows.
Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
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Forecasting financial distress in dynamic environments AI adoption signals and temporally pruned training windows
AI adoption proxies from text and patents improve out-of-sample distress prediction in Chinese firms when machine learning models use temporally pruned recent training windows.