The profitability of daily stock market indices trades based on neural network predictions: case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965–1999

A variety of new and powerful time series tools are available to test for predictive components in data which previously have been regarded as weak form efficient. The key issue is whether these new tools support profitable trading. A method is introduced based on univariate neural networks using untransformed data inputs to provide short-term predictions of the stock market indices returns. The profitability of trading signals generated from the out-of-sample short-term predictions for daily returns of S&P 500, DAX, TOPIX and FTSE stock market indices is evaluated over the period 1965–1999. The results provide strong evidence of high and consistent predictability contrasting the previous finding of weak form efficiency for index series and is notable because two of the series (S&P 500 and DAX) are confirmed as random using conventional tests. The out-of-sample prediction performance of neural networks is evaluated using RMSE, NMSE, MAE and sign and direction change statistics against a benchmark linear autoregressive model. Significant information advantage is confirmed by the Pesaran–Timmermann test. Finally, it is shown that buy and sell signals derived from neural network predictions are significantly different from unconditional one-day mean return and are likely to provide significant net profits for plausible decision rules and transaction cost assumptions.

[1]  H. Leland.,et al.  Beyond Mean–Variance: Performance Measurement in a Nonsymmetrical World (corrected) , 1999 .

[2]  Yves Chauvin,et al.  Backpropagation: the basic theory , 1995 .

[3]  J. Faraway,et al.  Time series forecasting with neural networks: a comparative study using the air line data , 2008 .

[4]  Ramazan Gençay,et al.  The predictability of security returns with simple technical trading rules , 1998 .

[5]  R. Thaler,et al.  Does the Stock Market Overreact , 1985 .

[6]  J. Poterba,et al.  Mean Reversion in Stock Prices: Evidence and Implications , 1987 .

[7]  R. Donaldson,et al.  An artificial neural network-GARCH model for international stock return volatility , 1997 .

[8]  A. Lo,et al.  The Size and Power of the Variance Ratio Test in Finite Samples: a Monte Carlo Investigation , 1988 .

[9]  B. LeBaron,et al.  Simple Technical Trading Rules and the Stochastic Properties of Stock Returns , 1992 .

[10]  Johann deVilliers,et al.  Beyond Mean–Variance: Performance Measurement in a Nonsymmetrical World , 1999 .

[11]  Min Qi,et al.  Nonlinear Predictability of Stock Returns Using Financial and Economic Variables , 1999 .

[12]  A. Lo,et al.  Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test , 1987 .

[13]  Robert M. Farber,et al.  How Neural Nets Work , 1987, NIPS.

[14]  Thanasis Stengos,et al.  Moving average rules, volume and the predictability of security returns with feedforward networks , 1998 .

[15]  M. Hashem Pesaran,et al.  A Simple Nonparametric Test of Predictive Performance , 1992 .

[16]  B. LeBaron,et al.  A test for independence based on the correlation dimension , 1996 .

[17]  David E. Rumelhart,et al.  Predicting the Future: a Connectionist Approach , 1990, Int. J. Neural Syst..

[18]  D. Logue,et al.  The Total Cost of Transactions on the NYSE , 1988 .

[19]  E. Fama.  EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[20]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[21]  A. Craig MacKinlay,et al.  Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test , 1988 .

[22]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[23]  J. A. Stewart,et al.  Nonlinear Time Series Analysis , 2015 .

[24]  H. White,et al.  Economic prediction using neural networks: the case of IBM daily stock returns , 1988, IEEE 1988 International Conference on Neural Networks.

[25]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[26]  E. Fama,et al.  Permanent and Temporary Components of Stock Prices , 1988, Journal of Political Economy.

[27]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[28]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..