Abstract
In this chapter, a hybrid approach for stock market forecasting is presented. It allows to develop a mixture of hybrid experts, each consisting of a genetic classifier and an associated artificial neural network. The resulting experts have been applied to stock market forecasting using technical trading rules as genetic inputs and other inputs—in particular past quotations—for the neural networks. In particular, the former are used to find quasi-stationary regimes within the financial data series, whereas the latter are assigned the task of making “context-dependent” predictions on the next day trend of the market. To this end, a novel kind of feedforward artificial neural network has been defined, allowing to implement suitable predictors without being compelled to exploit more complex neural architectures. Test runs have been performed on some well-known stock market indexes, also taking into account trading commissions. The tests pointed to the good forecasting capability of the proposed approach, which repeatedly outperformed the buy-and-hold strategy.
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Armano, G., Marchesi, M., Murru, A. (2002). NXCS: Hybrid Approach to Stock Indexes Forecasting. In: Chen, SH. (eds) Genetic Algorithms and Genetic Programming in Computational Finance. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0835-9_6
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DOI: https://doi.org/10.1007/978-1-4615-0835-9_6
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