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Predicting mutual fund performance using artificial neural networks

Author

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  • Indro, D. C.
  • Jiang, C. X.
  • Patuwo, B. E.
  • Zhang, G. P.

Abstract

This study utilizes an artificial neural network (ANN) approach to predict the performance of equity mutual funds that follow value, blend and growth investment styles. Using a multi-layer perceptron model and GRG2 nonlinear optimizer, fund-specific historical operating characteristics were used to forecast mutual funds' risk-adjusted return. Results show that ANN generates better forecasting results than linear models for funds of all styles. In addition, our model outperforms that of Chiang et al. [Chiang WC, Urban TL, Baldridge GW. A neural network approach to mutual fund net asset value forecasting. Omega Int J Manage Sci 1996:24;205-215.] in predicting the performance of growth funds. We also employed a heuristic approach of variable selection via neural networks and compared it with the stepwise selection method of linear regression. Results are encouraging in that the reduced ANN models still outperform the linear models for growth and blend funds and yield similar results for value funds.

Suggested Citation

  • Indro, D. C. & Jiang, C. X. & Patuwo, B. E. & Zhang, G. P., 1999. "Predicting mutual fund performance using artificial neural networks," Omega, Elsevier, vol. 27(3), pages 373-380, June.
    • Handle: RePEc:eee:jomega:v:27:y:1999:i:3:p:373-380
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    References listed on IDEAS

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    1. Michael C. Jensen, 1968. "The Performance Of Mutual Funds In The Period 1945–1964," Journal of Finance, American Finance Association, vol. 23(2), pages 389-416, May.
    2. Brown, Stephen J & Goetzmann, William N, 1995. "Performance Persistence," Journal of Finance, American Finance Association, vol. 50(2), pages 679-698, June.
    3. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
    4. Brown, Stephen J, et al, 1992. "Survivorship Bias in Performance Studies," The Review of Financial Studies, Society for Financial Studies, vol. 5(4), pages 553-580.
    5. Chiang, W. -C. & Urban, T. L. & Baldridge, G. W., 1996. "A neural network approach to mutual fund net asset value forecasting," Omega, Elsevier, vol. 24(2), pages 205-215, April.
    6. Hung, Ming S. & Denton, James W., 1993. "Training neural networks with the GRG2 nonlinear optimizer," European Journal of Operational Research, Elsevier, vol. 69(1), pages 83-91, August.
    7. Elton, Edwin J, et al, 1993. "Efficiency with Costly Information: A Reinterpretation of Evidence from Managed Portfolios," The Review of Financial Studies, Society for Financial Studies, vol. 6(1), pages 1-22.
    8. Lehmann, Bruce N & Modest, David M, 1987. "Mutual Fund Performance Evaluation: A Comparison of Benchmarks and Benchmark Comparisons," Journal of Finance, American Finance Association, vol. 42(2), pages 233-265, June.
    9. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    10. Venkat Subramanian & Ming S. Hung, 1993. "A GRG2-Based System for Training Neural Networks: Design and Computational Experience," INFORMS Journal on Computing, INFORMS, vol. 5(4), pages 386-394, November.
    11. Hendricks, Darryll & Patel, Jayendu & Zeckhauser, Richard, 1993. "Hot Hands in Mutual Funds: Short-Run Persistence of Relative Performance, 1974-1988," Journal of Finance, American Finance Association, vol. 48(1), pages 93-130, March.
    12. Elton, Edwin J & Gruber, Martin J & Blake, Christopher R, 1996. "The Persistence of Risk-Adjusted Mutual Fund Performance," The Journal of Business, University of Chicago Press, vol. 69(2), pages 133-157, April.
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    1. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can Machine Learning Help to Select Portfolios of Mutual Funds?," Working Papers 1245, Barcelona School of Economics.
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    3. Ching-Hsue Cheng & Ssu-Hsiang Wang, 2015. "A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress," Quantitative Finance, Taylor & Francis Journals, vol. 15(12), pages 1979-1994, December.
    4. Hwarng, H. Brian, 2001. "Insights into neural-network forecasting of time series corresponding to ARMA(p,q) structures," Omega, Elsevier, vol. 29(3), pages 273-289, June.
    5. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
    6. Laura Fabregat-Aibar & Maria-Teresa Sorrosal-Forradellas & Glòria Barberà-Mariné & Antonio Terceño, 2021. "Can Artificial Neural Networks Predict the Survival Capacity of Mutual Funds? Evidence from Spain," Mathematics, MDPI, vol. 9(6), pages 1-10, March.
    7. Huarng, Kunhuang & Yu, Tiffany Hui-Kuang, 2006. "The application of neural networks to forecast fuzzy time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(2), pages 481-491.
    8. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
    9. Luiz Moutinho & K.-H. Huarng & Tiffany Yu & C.-Y. Chen, 2008. "Modeling and forecasting tourism demand: the case of flows from Mainland China to Taiwan," Service Business, Springer;Pan-Pacific Business Association, vol. 2(3), pages 219-232, September.
    10. Maldonado, Sebastián & Domínguez, Gonzalo & Olaya, Diego & Verbeke, Wouter, 2021. "Profit-driven churn prediction for the mutual fund industry: A multisegment approach," Omega, Elsevier, vol. 100(C).
    11. Neupane, Suman & Thapa, Chandra & Vithanage, Kulunu, 2023. "Context‐specific experience and institutional investors’ performance," Journal of Banking & Finance, Elsevier, vol. 149(C).
    12. Konstantina Pendaraki & Michael Doumpos & Constantin Zopounidis, 2003. "Assessing Equity Mutual Funds' Performance Using a Multicriteria Methodology: A Comparative Analysis," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 1(1), pages 85-104.
    13. Malhotra, Rashmi & Malhotra, D. K., 2003. "Evaluating consumer loans using neural networks," Omega, Elsevier, vol. 31(2), pages 83-96, April.
    14. Perez Katarzyna & Szczyt Małgorzata, 2021. "Classification of Open-End Investment Funds Using Artificial Neural Networks. The Case of Polish Equity Funds," Central European Economic Journal, Sciendo, vol. 8(55), pages 269-284, January.
    15. Robert G. Biscontri, 2012. "A Radial Basis Function Approach To Earnings Forecast," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(1), pages 1-18, January.
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    Keywords

    Forecasting GRG2 Mutual fund performance Neural networks;

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