Forecasting Financial Investment Firms’ Insolvencies Empowered with Enhanced Predictive Modeling

Ahmed Amer Abdul-Kareem (), Zaki T. Fayed, Sherine Rady, Salsabil Amin El-Regaily and Bashar M. Nema
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Ahmed Amer Abdul-Kareem: Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
Zaki T. Fayed: Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
Sherine Rady: Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
Salsabil Amin El-Regaily: Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
Bashar M. Nema: Department of Computer Science, College of Science, Mustansiriyah University, Baghdad 10001, Iraq

JRFM, 2024, vol. 17, issue 9, 1-21

Abstract: In the realm of financial decision-making, it is crucial to consider multiple factors, among which lies the pivotal concern of a firm’s potential insolvency. Numerous insolvency prediction models utilize machine learning techniques try to solve this critical aspect. This paper aims to assess the financial performance of financial investment firms listed on the Iraq Stock Exchange (ISX) from 2012 to 2022. A Multi-Layer Perceptron predicting model with a parameter optimizer is proposed integrating an additional feature selection process. For this latter process, three methods are proposed and compared: Principal Component Analysis, correlation coefficient, and Particle Swarm Optimization. Through the fusion of financial ratios with machine learning, our model exhibits improved forecast accuracy and timeliness in predicting firms’ insolvency. The highest accuracy model is the integrated MLP + PCA model, at 98.7%. The other models, MLP + PSO and MLP + CC, also exhibit strong performance, with 0.3% and 1.1% less accuracy, respectively, compared to the first model, indicating that the first model serves as a powerful predictive approach.

Keywords: insolvency; MLP; feature selection; financial ratio (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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