Forecasting financial distress in dynamic environments AI adoption signals and temporally pruned training windows

TL;DR

This paper demonstrates that incorporating AI adoption proxies and using temporally pruned training windows significantly enhances the accuracy of financial distress prediction models in dynamic environments.

Contribution

It introduces AI adoption proxies derived from textual disclosures and patents, and shows that adaptive, recent-data-focused training improves forecasting robustness.

Findings

AI proxies improve out-of-sample discrimination and reduce Type II errors.

Recent data training windows outperform full history models.

Explainability analyses highlight financial ratios and AI signals as key predictors.

Abstract

Forecasting corporate financial distress increasingly requires capturing firms' adoption of transformative technologies such as artificial intelligence, yet model performance remains vulnerable to temporal distribution shifts as these technologies diffuse. This study investigates whether firm-level artificial intelligence (AI) adoption proxies improve forecasting performance beyond standard accounting fundamentals. Using a panel of Chinese A-share non-financial firms from 2007 to 2023, we construct AI indicators from textual disclosures and patent data. We benchmark six machine learning classifiers under a strictly chronological design that fixes the final test year and progressively prunes the training history to capture temporal change. Results indicate that AI proxies consistently improve out-of-sample discrimination and reduce Type II errors, with the strongest gains in tree-based ensembles. Predictive performance is non-monotonic in training window length; models trained on recent data outperform those using full history, while single-year training proves unreliable. Explainability analyses reveal financial ratios as primary drivers, with AI adoption signals adding incremental forecasting content whose interpretation as a risk factor varies across training regimes. Our findings establish AI proxies as valuable predictors for distress screening and demonstrate that adaptive, temporally pruned forecasting windows are essential for robust early warning models in rapidly evolving technological and economic environments.