Analyzing the Application of Artificial Intelligence and Machine Learning Algorithms for Evaluating the Financial Performance of Publicly Listed Investment Companies with an Emphasis on the Case of Ghadir Investment Group

Document Type : Original Article

Authors

1 Ph.D., Department of Chemistry, Molecular Simulation Research Laboratory, University of Science and Technology, Tehran, Iran.

2 M.A,, Department of Financial Management, Faculty of Economics and Management, Urmia University, Urmia, Iran.

Abstract

The objective of the present study was to identify the optimal combination of machine learning algorithms, feature selection methods, and clustering techniques for predicting company performance on the Tehran Stock Exchange. The statistical population includes all companies listed on the Tehran Stock Exchange between the years 1389 and 1402. A systematic elimination method was employed to select the sample, resulting in a final dataset of 171 companies. The collected data were analyzed using RapidMiner for machine learning processes and EViews for econometric analysis.
The results obtained from conducting comparative analysis revealed that advanced models such as support vector machines, random forests, and neural networks generally outperform traditional methods in financial forecasting tasks. However, the competitive performance of simpler models, such as logistic regression in specific scenarios, highlights the ongoing relevance of model interpretability and suggests caution against unnecessary algorithmic complexity. Our findings indicate that dimensionality reduction techniques—namely principal component analysis (PCA) and self-organizing maps (SOMs)—often result in reduced predictive accuracy in this context, suggesting that their use should be guided by a clear understanding of the data characteristics and the predictive objectives. Moreover, ensemble approaches, particularly bagging, significantly enhanced model robustness, demonstrating the value of combining multiple learners to navigate the inherent volatility of financial markets. Notably, our top-performing models generalized well when applied to the portfolio of Ghadir Investment Holding, underscoring their practical applicability. The findings of the study demonstrate that models trained on broader market data can provide valuable insights for specialized investment portfolios. Therefore, the findings offer meaningful implications for investment firms and financial analysts, highlighting the potential of machine learning for improving decision-making processes.

Keywords

References

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اقتصاد دفاع و توسعه پایدار
Volume 10, Issue 37
September 2025
Pages 121-147

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