The use of data mining and neural networks for forecasting stock market returns

  • Authors:

  • David Enke;Suraphan Thawornwong

  • Affiliations:

  • Laboratory for Investment and Financial Engineering, Smart Engineering Systems Lab, Intelligent Systems Center, University of Missouri, Rolla, MO 65409-0370, USA;Laboratory for Investment and Financial Engineering, Smart Engineering Systems Lab, Intelligent Systems Center, University of Missouri, Rolla, MO 65409-0370, USA

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2005

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Abstract

It has been widely accepted by many studies that non-linearity exists in the financial markets and that neural networks can be effectively used to uncover this relationship. Unfortunately, many of these studies fail to consider alternative forecasting techniques, the relevance of input variables, or the performance of the models when using different trading strategies. This paper introduces an information gain technique used in machine learning for data mining to evaluate the predictive relationships of numerous financial and economic variables. Neural network models for level estimation and classification are then examined for their ability to provide an effective forecast of future values. A cross-validation technique is also employed to improve the generalization ability of several models. The results show that the trading strategies guided by the classification models generate higher risk-adjusted profits than the buy-and-hold strategy, as well as those guided by the level-estimation based forecasts of the neural network and linear regression models.