Predicting mutual fund performance using artificial neural networks

Predicting mutual fund performance using artificial neural networks

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Article ID: iaor2001283
Country: United Kingdom
Volume: 27
Issue: 3
Start Page Number: 373
End Page Number: 380
Publication Date: Jun 1999
Journal: OMEGA
Authors: , , ,
Keywords: neural networks, time series & forecasting methods
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. 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.

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