Decoding Real Estate Stock Prices in Emerging Markets: An Explainable AI-Enhanced Fuzzy Logic and Machine Learning Framework

Year 2025, Volume: 7 Issue: 3, 221 - 231, 30.11.2025

Pham Thuy Tu

https://doi.org/10.51537/chaos.1680500

https://izlik.org/JA94LN42SY

Abstract

Stock price prediction in emerging markets is challenging due to volatility, inefficiencies, and complex macroeconomic interactions. Traditional econometric models struggle to capture nonlinear dynamics, limiting their predictive power. This study proposes a hybrid AI-driven forecasting framework integrating Fuzzy Logic, Particle Swarm Optimization (PSO), and Explainable AI (XAI) with Machine Learning (ML) to enhance both accuracy and interpretability. Using data from 22 publicly traded Vietnamese real estate firms (2013–2024), the model combines financial indicators (e.g., book value, earnings per share) with macroeconomic variables (e.g., CPI, oil prices, Federal Reserve rates). The hybrid approach outperforms traditional models, achieving an R² of 96% compared to prior benchmarks (R² ≤ 82%). XAI techniques, particularly SHAP, reveal key stock price drivers such as firm size and interest rate fluctuations, offering deeper insights for investors and policymakers. This study extends the Fama-French and Arbitrage Pricing Theory (APT) frameworks, integrating AI-based feature interpretation for improved decision-making. The findings have significant implications for portfolio management, risk assessment, and regulatory oversight, equipping stakeholders with a robust tool to navigate market uncertainties.

Keywords

Explainable AI (XAI) , Fuzzy logic , Machine learning , Real estate companies , Stock price prediction

References

• Aline, F., J. Fernando, M. Mauro, F. Rodrigo, and A. Claudia, 2021 The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management 57: 102225.
• Barboza, F., H. Kimura, and E. Altman, 2017 Machine learning models and bankruptcy prediction. Expert Systems with Applications 83: 405–417.
• Bosman, R., R. Kräussl, and E. Mirgorodskaya, 2017 Modifier words in the financial press and investor expectations. Journal of Economic Behavior & Organization 138: 85–98, Handle: RePEc:eee:jeborg:v:138:y:2017:i:c:p:85-98.
• Chakravarty, S. and P. Dash, 2012 A pso based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices. Applied Soft Computing 12: 931–941.
• Chen, Y. and Y. Hao, 2017 A feature weighted support vector machine and k-nearest neighbor algorithm for stock market indices prediction. Expert Systems with Applications 80: 340– 355.
• Doshi-Velez, F. and B. Kim, 2017 Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
• Eberhart, R. and Y. Shi, 2001 Particle swarm optimization: Developments, applications, and resources. In Proceedings of the 2001 Congress on Evolutionary Computation (CEC2001), volume 1, pp. 81–86.
• Fama, E. F., 1970 Efficient capital markets: A review of theory and empirical work. The Journal of Finance 25: 383–417.
• Fama, E. F. and K. R. French, 1993 Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33: 3–56.
• Ferdaus, M., R. Chakrabortty, and M. Ryan, 2021 Multiobjective automated type-2 parsimonious learning machine to forecast time-varying stock indices online. IEEE Transactions on Systems, Man, and Cybernetics: Systems 52: 2874–2887.
• Francisco, R., A. de Almeida, and J. Gama, 2019 Xgboost: Enhancing performance on large-scale bankruptcy prediction. Journal of Risk and Financial Management 12: 67.
• Friedman, J. H., 2000 Greedy function approximation: A gradient boosting machine. Annals of Statistics 29: 1189–1232.
• Hiep, T. T. and N. V. Cuong, 2024 Factors affecting stock prices of listed real estate enterprises in the vietnamese stock market. Journal of Economy and Forecasting .
• Huda, S., S. Alyahya, and A. A. Bakar, 2024 Machine learning techniques for stock market prediction: A review. IEEE Access 12: 123456–123470.
• Jiang, Y. and Z. Zhou, 2018 Does the time horizon of the return predictive effect of investor sentiment vary with stock characteristics? a granger causality analysis in the frequency domain. arXiv preprint arXiv:1803.02962 .
• Jin, S., 2024 A comparative analysis of traditional and machine learning methods in forecasting the stock markets of china and the us. International Journal of Advanced Computer Science and Applications 15: 1–8.
• Kahneman, D. and A. Tversky, 1979 Prospect theory: An analysis of decision under risk. Econometrica 47: 263–291.
• Kennedy, J. and R. Eberhart, 1995 Particle swarm optimization. In Proceedings of ICNN’95 – International Conference on Neural Networks, volume 4, pp. 1942–1948.
• Larojan, C., 2021 Impact of accounting ratios on stock market price of listed companies in colombo stock exchange. Journal of Economics and Business .
• Lo, A. W., 2004 The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. Journal of Portfolio Management 30: 15–29.
• Mahinda, M., L. Christoph, L. Pasi, and P. Jari, 2022 Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications 197: 1–41.
• Molnar, C., 2022 Interpretable machine learning: A guide for making black box models explainable.
• Nadia, B., F. Roland, and W. Alois, 2021 Covid-19’s impact on real estate markets: Review and outlook. Financial Markets and Portfolio Management 35.
• Parichat, S. and C. Surachai, 2024 Impact of oil and gold prices on southeast asian stock markets: Empirical evidence from quantile regression analysis. ABAC Journal 44: 123–137.
• Phuong, L., H. Trong, and V. Bao, 2022 Factors affecting stock prices of listed real estate enterprises in vietnam. Journal of Integration and Development 63: 21–28.
• Prakash, K., U. Acharya, M. Geetha, S. Rajat, and R. Abraham, 2022 A comparative study of deep neural network and statistical models for stock price prediction. In Proceedings of the 2022 3rd International Conference for Emerging Technology (INCET), Belgaum, India, IEEE.
• Quy, V. T. and D. T. N. Loi, 2016 Macroeconomic factors and stock price – a case of real estate stocks on ho chi minh stock exchange. Journal of Science, Ho Chi Minh City Open University 2: 63–75.
• Rimal, R., B. Rimal, H. Bhandari, N. Pokhrel, and K. Dahal, 2024 Real estate market prediction using deep learning models. Annals of Data Science .
• Ross, S. A., 1976 The arbitrage theory of capital asset pricing. Journal of Economic Theory 13: 341–360.
• Rudin, C., 2019 Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1: 206–215.
• Thuan, T., T. Tam, V. Ha, C. Van, L. Hiep, et al., 2023 Factors affecting stock prices of listed real estate enterprises in the vietnamese stock market. Asian Journal of Economics and Banking p. 211.
• Van, P., 2021 Factors affecting stock prices of listed real estate enterprises in the vietnamese stock market. Journal of Science and Technology pp. 158–167.
• Vikas, D. and D. Kumar, 2023 Stock price prediction of aapl stock by using machine learning techniques: A comparative study. In Proceedings of the 2023 12th International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, India, IEEE.
• Yan, C., X. Zhilong, Z.Wenjie, X. Rong, and Q. Li, 2020 Quantifying the effect of real estate news on chinese stock movements. Emerging Markets Finance and Trade 57: 4185–4210.
• Zadeh, L., 1965 Fuzzy sets. Information and Control 8: 338–353.
• Zhang,W. and S. Hamori, 2021 Crude oil market and stock markets during the covid-19 pandemic: Evidence from the us, japan, and germany. International Review of Financial Analysis 74: 101702.