In this paper, a hybrid approach to stock market forecasting is presented. It entails utilizing a mixture of hybrid experts, each expert embedding a genetic classifier coupled with an artificial neural network. Information retrieved from technical analysis is supplied as input to genetic classifiers, while past stock market prices - together with other relevant data - are used as input to neural networks. In this way it is possible to implement a strategy that resembles the one used by human experts. In particular, genetic classifiers based on technical-analysis domain knowledge are used to identify quasi-stationary regimes within the financial data series, whereas neural networks are designed to perform context-dependent predictions. For this purpose, a novel kind of feedforward artificial neural network has been defined whereby effective stock market predictors can be implemented without the need for complex recurrent neural architectures. Experiments were performed on. a major Italian stock market index, also taking into account trading commissions. The results point to the good forecasting capability of the proposed approach, which allowed outperforming the well known buy-and-hold strategy, as well as predictions obtained using recurrent neural networks.

Stock market prediction by a mixture of genetic-neural experts

ROLI, FABIO
2002

Abstract

In this paper, a hybrid approach to stock market forecasting is presented. It entails utilizing a mixture of hybrid experts, each expert embedding a genetic classifier coupled with an artificial neural network. Information retrieved from technical analysis is supplied as input to genetic classifiers, while past stock market prices - together with other relevant data - are used as input to neural networks. In this way it is possible to implement a strategy that resembles the one used by human experts. In particular, genetic classifiers based on technical-analysis domain knowledge are used to identify quasi-stationary regimes within the financial data series, whereas neural networks are designed to perform context-dependent predictions. For this purpose, a novel kind of feedforward artificial neural network has been defined whereby effective stock market predictors can be implemented without the need for complex recurrent neural architectures. Experiments were performed on. a major Italian stock market index, also taking into account trading commissions. The results point to the good forecasting capability of the proposed approach, which allowed outperforming the well known buy-and-hold strategy, as well as predictions obtained using recurrent neural networks.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/1088324
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