Document Type : Original Article
Authors
1
Professor of finance management, faculty of accounting and finance, college of management, university of Tehran. Iran.
2
Msc of finance management, faculty of accounting and finance, college of management, university of Tehran. Iran.
3
Assistant Professor, Industrial Management Deptment, Meybod University, Meybod, Iran
4
Msc Student of Algrithms and Computations, faculty of Electrical and Computer Engineering (ECE), college of Engineering, University of Tehran. Iran.
10.48308/jfmp.2025.238860.1475
Abstract
Purpose: In recent years, machine learning has gained significant attention as an effective tool for forecasting financial time series. However, many of these models function as black boxes, and their lack of transparency has led to reduced trust in their predictions. To address this limitation, the use of explainable artificial intelligence (XAI) models-capable of providing detailed insights into the prediction mechanisms-has become essential. Accordingly, the aim of this study is to develop and evaluate an artificial intelligence (AI)-based forecasting model that not only delivers high accuracy but also offers strong interpretability. In this context, the contribution and role of input variables in the model's predictions are explicitly identified, and the stability of the results in terms of both accuracy and explain ability is assessed using cross-validation techniques, particularly time series splitting.
Method: This applied research adopts a descriptive-analytical method with a quantitative forecasting approach. For the first time in Iran, it investigates the explain ability of optimized artificial intelligence models in forecasting the return of the price index for eight manufacturing industries listed on the Tehran Stock Exchange. The dataset, covering the period from 2018 to 2023, was collected from the Bourse View database. The Random Forest algorithm, as an ensemble learning method, was trained using a combination of technical, fundamental, and macroeconomic variables as input features. A Genetic Algorithm was utilized to optimize the model’s hyperparameters. To enhance transparency and model credibility, the SHAP (shapley additive explanations) technique was employed to analyze the influence and importance of each feature in the prediction process.
Findings: The results demonstrate that combining the Random Forest algorithm with Genetic Algorithm-based hyperparameter optimization and incorporating explain ability techniques such as SHAP values not only improves the prediction accuracy of the price index returns for Tehran’s manufacturing industries but also enhances model transparency and reliability. The findings highlight those technical indicators-particularly the Exponential Moving Average (EMA), MACD (Moving Average Convergence Divergence) index, trading volume, and free float shares-play the most significant role in enhancing predictive accuracy. In contrast, fundamental variables such as the price-to-earnings ratio and interest rates are influential but less impactful compared to technical indicators. Furthermore, time series cross-validation confirms the robustness and generalizability of the proposed model across different time periods.
Conclusion: In line with reputable international studies, the results suggest that explainable artificial intelligence (AI) models not only outperform traditional models in predictive tasks but also assist financial analysts in making informed and effective decisions. These models can play a pivotal role in risk management and portfolio optimization. Therefore, the proposed model-featuring operational transparency and high reliability- is introduced as an effective tool for financial analysts, opening new horizons for the application of explainable artificial intelligence (AI) in Iran’s financial sector.
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