Predicting the Financial Risk of Public Companies with a Hybrid Algorithm FA-PSO-LSTM

Document Type : Original Article

Authors

1 MS in Financial Management. Faculty of Economics and Social Sciences, Shahid Chamran University of Ahvaz. Ahvaz. Iran

2 Department of management, Faculty of Economics and Social Sciences, Ahvaz, Iran

3 Professor, Faculty of Economics and Social Sciences, Department of Management, Shahid Chamran university of Ahvaz, Ahvaz, Iran

Abstract

This research, considering the need for continuous monitoring of financial data and focusing on artificial intelligence algorithms, has carefully evaluated the data of companies active in the three industries of basic metals, automotive, and petroleum products over a ten-year period using an experimental and field study and a new case model. After determining the effective financial factors from factor analysis, the mean square error and predicted values of the LSTM neural network were used to optimize the particle swarm algorithm function and optimize the learning rate and number of hidden layers of the neural networks. The FA-PSO-LSTM deep learning model used is an innovative and relatively new model that can fully benefit from the advantages of the LSTM network in time series processing and lead to the evolution of the theory in this regard. The results indicate that the proposed model predicts financial risk in the petroleum products industry with high accuracy and highlights the importance of variables such as liquidity, cash flow, and profitability. In the automotive industry, indicators such as liquidity, operational capacity, and sustainable development were more effective, while in the basic metals industry, the most data stability was observed and the best model performance was recorded. Overall, the variables of liquidity, profitability, cash flow, operational capacity, and development capability were identified as key common risk factors. It was also found that the proposed model has higher accuracy and efficiency compared to traditional methods and other algorithms..

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References

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Budget and Finance Strategic Research
Volume 6, Issue 4 - Serial Number 24
Winter Quarterly
February 2026
Pages 11-30

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History

  • Receive Date: 12 July 2025
  • Revise Date: 28 October 2025
  • Accept Date: 09 February 2026

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