Neuro-evolutionary approach to stock market prediction

Jacek Mańdziuk · Marcin Jaruszewicz

29 citations0/0 files storedInternational Joint Conference on Neural Networks (IJCNN)

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Stock Market Forecasting Methods Neural Networks and Applications Evolutionary Algorithms and Applications

Abstract

A neuro-evolutionary method for a short-term stock index prediction is presented. The data is gathered from the German stock exchange (the target market) and two other markets (Tokyo stock exchange and New York stock exchange) together with EUR/USD and USD/JPY exchange rates. Neural networks supported by genetic algorithm (GA) are used as the prediction engine. The GA is used to find suboptimal set of input variables for a one day prediction. Due to high volatility of mutual relations between input variables, a particular choice of input variables found by the GA is valid only for a short period of time and a new set of inputs is generated every 5 days. The method of selecting input variables works efficiently. Variables which are no longer useful are exchanged with the new ones. On the other hand some particularly useful variables are consequently utilized by the GA in subsequent independent steps. Simulation results of the proposed neuro-evolutionary system applied to prediction of the percentage change of closing value of DAX index are very promising and competitive to the ones obtained by the three other heuristical models implemented and tested for comparison.

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References

2003Support vector machine with adaptive parameters in financial time series forecasting 1998Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks 2000A case study on using neural networks to perform technical forecasting of forex 2003The adaptive selection of financial and economic variables for use with artificial neural networks 2001Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging 2001Computational learning techniques for intraday FX trading using popular technical indicators 2002Forecasting Financial Time Series using Neural Network and Fuzzy System-based Techniques 2001Empirical regularities from interacting long- and short-memory investors in an agent-based stock market

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