Predicting Mutual Fund Returns in Member Countries of the Federation of Euro-Asian Stock Exchanges: A Spatial and Artificial Intelligence Approach

Document Type : Research Paper

Authors

1 PhD Candidate, Department of Accounting, Faculty of Economics and Management, Urmia University, Urmia, Iran.

2 Associate Prof., Department of Accounting, Faculty of Economics and Management, Urmia University, Urmia, Iran.

3 Assistant Prof., Department of Accounting, Faculty of Economics and Management, Urmia University, Urmia, Iran.

10.22059/frj.2025.385026.1007665

Abstract

Objective
The primary objective of this study is to forecast the returns of investment funds in developed and developing countries that are members of the Federation of Euro-Asian Stock Exchanges (FEAS).
 
Methods
This applied research study analyzes financial data from investment funds in FEAS countries over the period 2015–2023. The modeling framework employs artificial intelligence techniques, spatial econometrics, and a hybrid approach combining both within a spatial hybrid panel structure. The study examines the effects of key variables—including the Sharpe ratio, Jensen’s alpha, asset growth rate, and the proportion of retail investors—on fund returns. The central aim is to assess the predictive efficiency of these models under different economic conditions.
 
Results
The findings demonstrate that artificial intelligence models outperform alternative approaches in forecasting returns for both developed and developing country groups. The neoclassical neural network showed the strongest performance across both groups, while the multilayer perceptron also proved effective in developed markets; in contrast, decision tree models exhibited weaker predictive capability. Moreover, integrating artificial intelligence methods with spatial techniques led to significant improvements in forecasting accuracy. The results indicate that market returns had a positive effect in both groups, with a more pronounced impact in developed countries. The analysis identified the Sharpe ratio, Jensen’s alpha, asset growth rate, and the proportion of retail investors as significant determinants of fund returns. Notably, the Sharpe ratio had a significant positive effect in both groups, reflecting the greater sensitivity of investors in developed markets to risk-adjusted returns. Finally, forecasting mutual fund returns using the combined artificial intelligence and spatial hybrid panel approach revealed substantial differences in model performance between developed and developing countries. In developed countries, models such as the multilayer perceptron and decision tree achieved superior performance, whereas in developing countries, deep learning and support vector machine models demonstrated greater effectiveness.
 
Conclusion
This study demonstrates that developed markets substantially outperform developing markets in terms of predictability and performance stability. Advanced artificial intelligence models proved effective in forecasting fund returns in both developed and developing country groups. Ultimately, the design of hybrid models that integrate artificial intelligence with spatial analysis and spatial hybrid panels can enhance the accuracy of fund return forecasts, thereby enabling investors and fund managers to make more informed financial decisions. Accordingly, the implementation of hybrid artificial intelligence and spatial hybrid panel models in both groups improved efficiency and increased forecasting precision, exerting a considerable influence on the quality of financial decision-making. This research also revealed that variables affecting fund returns—including the Sharpe ratio, Jensen’s alpha, asset growth rate, and the proportion of retail investors—together with countries’ economic conditions, influence model performance, underscoring the importance of incorporating macroeconomic factors into financial and accounting analyses. For market practitioners and financial analysts, these findings can contribute to enhanced financial reporting processes, more rigorous risk assessment, and improved investment decision-making. Specifically, the results can facilitate greater transparency of financial information within investment funds.

Keywords

Main Subjects

References

 
Abe, M. & Nakayama, H. (2018). Deep learning for forecasting stock returns in the cross-section. D. Phung, V.S. Tseng, G.I. Webb, B. Ho, M. Ganji & L. Rashidi (Eds.), Advances in knowledge discovery and data mining, 1(22), 273–284.
Aggarwal, R. K. & Jorion, P. (2010). The performance of emerging hedge funds and managers. Journal of Financial Economics, 96(2), 238-256.
Akhbari, H., Mohammadzadeh Salteh, H., Baradaran Hassanzadeh, R. & Zeynali, M. (2024). Optimizing Risk-based Stock Return Prediction in Tehran Stock Exchange industries: A Data Envelopment Analysis. Financial Research Journal, 26(2), 331-354. (in Persian)
Al Shawawreh, F. K. (2024). Risk Modeling, Return Forecasting, and Optimal Portfolio Selection: An Empirical Study on Amman Stock Exchange, 39(2), 49-97.
Alabdullah, A.A., Iqbal, M., Zahid, M., Khan, K., Amin, M.N., Jalal, F.E. )2022(. Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis. Constr. Build. Mater. 345 (128296), 2-13.
Al-Sultan, S. Y. & Al-Baltah, I. A. (2024). An improved random forest algorithm (ERFA) utilizing an unbalanced and balanced dataset to predict customer churn in the banking sector. IEEE Access, 1, 99-101.
Bali, T. G., Brown, S. J. & Caglayan, M. O. (2014). Macroeconomic risk and hedge fund returns. Journal of Financial Economics, 114(1), 1-19. https://doi.org/10.1016/j.jfineco.2014.06.008
Ballings, M., Poel, D. V. D., Hespeels, N. & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications, 42, 7046–7056.
Bauer, M. D. & Hamilton, J. D. (2018). Robust bond risk premia. The Review of Financial Studies, 31(2), 399-448.
Behera, J., Pasayat, A. K., Behera, H. & Kumar, P. (2023). Prediction based mean-value-at-risk portfolio optimization using machine learning regression algorithms for multi-national stock markets. Engineering Applications of Artificial Intelligence, 120 (2), 2-14.
Ben-David, I., Franzoni, F. & Moussawi, R. (2017). Exchange-traded funds. Annual Review of Financial Economics, 9(1), 169-189.
Bollen, N. P. & Busse, J. A. (2005). Short-term persistence in mutual fund performance. The Review of Financial Studies, 18(2), 569-597.
Boonprasope, A. & Tippayawong, K. Y. (2024). Predicting Healthcare Mutual Fund Performance Using Deep Learning and Linear Regression. International Journal of Financial Studies, 12(1), 23.
Box, G. E. & Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American statistical Association, 65(332), 1509-1526.‏
Campbell, J. Y. & Ammer, J. (1993). What moves the stock and bond markets? A variance decomposition for long‐term asset returns. The Journal of Finance, 48(1), 3-37.
Chavoshi, K., Saber, A. (2013). Prediction of return of mutual investment funds with the approach of artificial neural networks. Financial Engineering and Portfolio Management, 17(4), 117 – 131. (in Persian)
Chen, J., Hong, H., Huang, M. & Kubik, J. D. (2004). Does fund size erode mutual fund performance? The role of liquidity and organization. American Economic Review, 94(5), 1276-1302.
Chen, J., Ma, F., Qiu, X. & Li, T. (2023). The role of categorical EPU indices in predicting stock-market returns. International Review of Economics & Finance, 87, 365-378.‏
Chen, W., Zhang, H., Mehlawat, M. K. & Jia, L. (2021). Mean-variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing, 100(1), 1-18.
Cheng, C. S. A. & Hollio, D. (2008). Do core and noncore cash flows from operation persist differentially in predicting future cash flows? Review of Quantitative Finance and Accounting, 31, 29-53.
Chia, M. Y., Huang, Y. F. & Koo, C. H. (2022). Resolving data-hungry nature of machine learning reference evapotranspiration estimating models using inter-model ensembles with various data management schemes. Agricultural Water Management, 261, 107343.‏
Choi, I. & Kim, W. C. (2024). Enhancing Exchange-Traded Fund Price Predictions: Insights from Information-Theoretic Networks and Node Embeddings. Entropy, 26(1), 70.
Danil, B., Christensen, B. J., Mühlbach, N. N. & Nielsen, M. S. (2024). Targeting predictors in random forest regression. Journal of Financial Econometrics, 22(1), 104-128.
Danquah, R. & Yu, B. (2023). Selection ability and market timing skills of mutual fund and unit trust managers in a developing economy: evidence from Ghana. Business Analyst Journal, 44(1), 1-14.
Davis, J. & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning (ICML), 2006, 233-240.
Deng, Y., Wang, Y. & Zhou, T. (2024). Macroeconomic Expectations and Expected Returns. Journal of Financial and Quantitative Analysis, 1–37.
Eling, M. & Schuhmacher, F. (2007). Does the choice of performance measure influence the evaluation of hedge funds? Journal of Banking & Finance, 31(9), 2632-2647.
Fama, E. (1970) Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25: 383-417.
Fama, E. F. & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of financial economics, 33(1), 3-56.‏
Fama, E. F. & Schwert, G. W. (1977). Asset returns and inflation. Journal of financial economics, 5(2), 115-146.
Farshadfar, Z. & Prokopczuk, M. (2019). Nonlinear model improves stock return out of sample forecasting (case study: united state stock market). International Journal of Finance & Managerial Accounting, 3(12), 1-13.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Ferson, W. E. & Harvey, C. R. (1991). The variation of economic risk premiums. Journal of Political Economy, 99(2), 385-415.
Fieberg, C., Metko, D., Poddig, T. & Loy, T. (2023). Machine learning techniques for cross-sectional equity returns’ prediction. OR Spectrum, 45(1), 289-323.
Gargano, A., Pettenuzzo, D. & Timmermann, A. (2019). Bond return predictability: Economic value and links to the macroeconomy. Management Sci., 65(2), 508–540.
Goodfellow, I., Bengio, Y. & Courville, A. (2016). Deep Learning. MIT Press.
Grinblatt, M. & Keloharju, M. (2000). The investment behavior and performance of various investor types: a study of Finland's unique data set. Journal of financial economics, 55(1), 43-67.
Gurkaynak, R.S., Sack, B. & Wright, J.H. (2007) The U.S. treasury yield curve: 1961 to the present. J. Monetary Econom. 54(8), 2291–2304.
Harrison, B. & Moore, W. (2012). Forecasting stock market volatility in central and eastern European countries. Journal of forecasting, 31(6), 490-503.‏
Harvey, C. R. & Liu, Y. (2022). Luck versus skill in the cross section of mutual fund returns: Reexamining the evidence. The Journal of Finance, 77(3), 1921-1966.‏
Ho, W.K., Tang, B.-S., Wong, S.W. (2021). Predicting property prices with machine learning algorithms. J. Prop. Res., 38 (1), 48–70.
Hyndman, R. J. & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.‏
Jensen, M. C. (1968). The performance of mutual funds in the period 1945–1964. The Journal of finance, 23(2), 389-416.
Jorion, P. (1991). The pricing of exchange rate risk in the stock market. Journal of financial and quantitative analysis, 26(3), 363-376.
Joseph, A., Larrain, M. & Turner, C. (2017). Daily stock returns characteristics and forecastability. Procedia computer science, 114, 481-490.‏
 Kacperczyk, M., Sialm, C. & Zheng, L. (2005). On the industry concentration of actively managed equity mutual funds. The Journal of Finance, 60(4), 1983-2011.
Kanade, A., Sherdiwala, M. & Sherekar, S. (2022). Performance of machine learning algorithms to predict right mutual funds of Indian companies. In Modern Approaches in Machine Learning & Cognitive Science: A Walkthrough (pp. 21-34). Cham: Springer International Publishing.
Keshavarz Haddad, Gh., Ebrahimnejad, A. & Grossi, M. (2022). The performance of real and legal investors in predicting the return of mutual investment funds. Biquarterly Scientific Journal of Economic Studies and Policies, 9(1), 94-115. (in Persian)
Khosroyani, Mahdi & Heydarpoor, Farzaneh (2022). Modeling to Predict the Liquidity Risk of Iran's Government Banks Using Artificial Neural Networks and Accounting Indicators. Financial Accounting and Auditing Researches, 14(55), 163-180. (in Persian)
LeSage, J. P. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press.
Levy, R. A. (1967). Random walks, Reality or myth. Financial Analysts Journal, 23(6), 69-77.‏
Li, X., Qin, Zh. & Kar, S. (2010). Mean-variance skewness model for portfolio selection with fuzzy returns, European Journal of Operational Research, 202 (1), 239-247.
Lin, G., Lin, A., Gu, D. (2022). Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient. Inform. Sci. 608, 517–531.
Lu, C. J. & Wu, J. Y. (2011). An efficient CMAC neural network for stock index forecasting. Expert Systems with Applications, 38(12), 15194-15201.‏
Lu, C. J., Lee, T. S. & Chiu, C. C. (2009). Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 47, 115–125.
Ma, Y., Han, R. & Wang, W. (2021). Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications, 165(1), 1-15.‏
Makridakis, S., Spiliotis, E. & Makridakis, M. (2020). M3-Competition: Results, conclusions, and way forward. International Journal of Forecasting, 36(1), 16-32.
Mallikarjuna, M. & Rao, R. P. (2019). Evaluation of forecasting methods from selected stock market returns. Financial Innovation, 5(1), 40.
Matías, J. M. & Reboredo, J. C. (2012). Forecasting performance of nonlinear models for Intraday Stock Returns. Journal of Forecasting, 31, 172–188.
Miffre, J. (2007). Country-specific ETFs: an efficient approach to global asset allocation. J. Asset Manag. 8 (2), 112–122.
Mishra, V. K., Dasgupta, U., Patra, S., Pal, R. & Anand, K. (2022). A dynamic two‐level artificial neural network for estimation of parameters in combined mode conduction‐radiation heat transfer in porous medium: An application to handle huge dataset with noise, 51(2), 1306-1335.‏
Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
Namazi, M. & Kiamhar, M. (2007). Predicting Daily Stock Returns of Companies listed in Tehran Stock Exchange Using Artificial Neural Networks. Financial Research Journal, 9(24), 116-134. (in Persian)
Nyukorong, R. (2020). Exchange-Traded Funds: What You Need to Know? European Scientific Journal, 1857-7881.
Pant, M. & Kumar, S. (2022). Fuzzy time series forecasting based on hesitant fuzzy sets, particle swarm optimization and support vector machine-based hybrid method. Granul. Comput., 7 (4), 861–879.
Pasayat, A. K., Mitra, A. & Bhowmick, B. (2024). Determination of essential features for predicting start-up success: an empirical approach using machine learning. Technology Analysis & Strategic Management, 36(8), 1810-1828.‏
Patel, M., Gupta, S. D. & Madhavan, V. (2023). Investment style consistency and performance of Indian fixed income mutual funds. IIMB Management Review, 35(3), 229-239.
Phan, D. H. B., Sharma, S. S. & Narayan, P. K. (2015). Stock return forecasting: Some new evidence. International Review of Financial Analysis, 40, 38-51.‏
Piovezan, R. P. B., de Andrade Junior, P. P. & Ávila, S. L. (2023). Machine Learning Method for Return Direction Forecast of Exchange Traded Funds (ETFs) Using Classification and Regression Models. Computational Economics, 1-26.
Piovezan, R. P. & Junior, P. P. D. A. (2022). Machine learning method for return direction forecasting of Exchange Traded Funds using classification and regression models. arXiv preprint arXiv, 2205, 12746.
‏Qureshi, F., Kutan, A. M., Ghafoor, A., Khan, H. H. & Qureshi, Z. (2019). Dynamics of mutual funds and stock markets in Asian developing economies. Journal of Asian Economics, 65, 2-16.
Rajabi, R., Asgari, M., Dehghan, A. (2018). Prediction of stock returns of companies listed in Tehran Stock Exchange using data mining techniques (neural network (MLP) and general additive model (GAM). New research approaches in management sciences, 2 (10), 1-5. (in Persian)
Rasel, R. I., Sultana, N. & Meesad, P. (2015). An efficient modelling approach for forecasting financial time series data using support vector regression and windowing operators. International Journal of Computational Intelligence Studies, 4(2), 134–150.
Sahu, S., Yadav, M. K., Gupta, A. K., Uddameri, V., Toppo, A. N., Maheedhar, B. & Ghosal, P. S. (2022). Modeling defluoridation of real-life groundwater by a green adsorbent aluminum/olivine composite: Isotherm, kinetics, thermodynamics and novel framework based on artificial neural network and support vector machine. Journal of Environmental Management, 302, 2-14.‏
Sharif far, A., Khaliliaraghi, M., Raeesi Vanani, I. & Fallahshams, M. (2022). Application of Deep Learning Architectures in Stock Price Forecasting: A Convolutional Neural Network ‎Approach. Journal of Asset Management and Financing, 10(3), 1-20.
(in Persian)
Sharpe, W. F. (1994). The Sharpe ratio. Journal of portfolio management, 21(1), 49-58.
Shu, T., Sulaeman, J. & Yeung, P. E. (2012). Local religious beliefs and mutual fund risk-taking behaviors. Management Science, 58(10), 1779–1796.
Simutin, M. (2010). Excess cash and stock returns. Financial Management, 39(3), 1197-1222.
Singhania, M. & Saini, N. (2023). Institutional framework of ESG disclosures: comparative analysis of developed and developing countries. Journal of Sustainable Finance & Investment, 13(1), 516-559.
Sirri, E. R. & Tufano, P. (1998). Costly search and mutual fund flows. The journal of finance, 53(5), 1589-1622.
Tang, J., Henderson, A., Gardner, P., 2021. Exploring AdaBoost and random forests machine learning approaches for infrared pathology on unbalanced data sets. Analyst, 146 (19), 5880–5891.
Tavakoli, S. & Ashtab, A. (2023). Comparison of the Effectiveness of Machine Learning Models and Statistical Models in Predicting Financial Risk. Financial Management Strategy, 11(1), 53-76. (in Persian)
Torabi, T., Tarighi, S. & Tataei, P. (2016). A Review of Mutual Investment Funds Performance with a View of Market Timing. International Journal of Finance & Managerial Accounting, 1(3), 47-53.‏
 Treynor, J. L. (1965). How to rate management of investment funds. Harvard business review, 43(1), 63-75.
Yu, J. R., Chiou, W. J. P., Lee, W. Y. & Lin, S. J. (2020). Portfolio models with return forecasting and transaction costs. International Review of Economics & Finance, 66, 118–130.
Zhang, X., Zheng, Y., Lien, D. & Yu, X. (2024). Can mutual fund investors benefit from volatility managing? Evidence from China. Pacific-Basin Finance Journal, 83 (102228), 2-19
Zhong, X., Enke, D. (2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1), 2-20.
Financial Research Journal
Volume 28, Issue 1
2026
Pages 187-235

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