پیش‌‌بینی بازده صندوق‌‌های سرمایه‌‏گذاری مشترک کشورهای عضو فدراسیون بورس‌‌های اروپایی و آسیایی ـ رویکرد فضایی و هوش مصنوعی

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.