Neutrosophic α-Discounted Cognitive Mapping for Financial Distress Prediction: Evidence from Saudi Emerging Markets

This study proposes a new framework to predict corporate financial distress in emerging markets, with a focus on Saudi Arabia. Traditional models such as the Altman Z-score and Ohlson O-score assume that financial data are complete, precise, and reliable. However, in emerging markets, information is often missing, noisy, or conflicting, and many important factors are qualitative. To address this problem, the paper develops a Neutrosophic α-Discounted Cognitive Mapping (Nα-FDM) model that can represent both risk and uncertainty. Financial indicators are expressed as neutrosophic triples that measure the degrees of distress, indeterminacy, and financial health. Expert judgments about the relative importance of key criteria, such as liquidity, solvency, and profitability, are combined using the α-discounting method to obtain consistent weights even when initial preferences are inconsistent. These elements are integrated into a neutrosophic cognitive map, which produces a Neutrosophic Financial Distress Index (NFDI) and a scalar score for each firm. A case study on four Saudi non-financial firms, using the current ratio, debt-to-equity ratio, and return on assets, shows that the model can distinguish clearly distressed, clearly healthy, and grey-zone firms characterized by high uncertainty. The results suggest that the Nα-FDM framework is a useful decision-support tool for investors, managers, and regulators in uncertainty-rich environments.