Forecasting Financial Distress for Shaping Public Policy

Objective: This paper seeks to develop a robust predictive model for identifying firms in the Indian context other than the financial service sector that may face financial distress and also to check the impact of one essential predictor, i.e., future cash flow, on financial distress prediction. Besides, the study also aims at making research that can inform public policy and provide recommendations.

Methods: The study employs financial information from the Prowess Database but is confined to non-financial service sector firms in India. Logistic regression, linear discriminant analysis (LDA), and artificial neural networks (ANNs) are applied to predict financial distress and their ability to foretell future cash flows. Other methods adopted in evaluating the models include accuracy, sensitivity, and specificity.

Results: ANNs outperform the other models based on accuracy and predictability, which are higher than the rates given by the other two models, namely logistic regression and LDA. The ANN model performs well in identifying financially distressed firms; thus, it is informative in evaluating their financial position. Also, results suggest that future cash flow substantially affects financial distress prediction, an essential new variable that needs to be considered in future research.

Conclusion: This predictive model of financial distress further gives a sound platform for the corresponding sector in India. In general, ANNs offer profound opportunities for managers, investors, policymakers, regulators and shareholders as an effective tool for preventive decision-making to reinforce the corporate world. This research demonstrates that high-level machine-learning approaches are still crucial in financial analysis and policymaking.