A Hybrid Neurogenetic Approach for Stock Forecasting
- 7 May 2007
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 18 (3), 851-864
- https://doi.org/10.1109/tnn.2007.891629
Abstract
In this paper, we propose a hybrid neurogenetic system for stock trading. A recurrent neural network (NN) having one hidden layer is used for the prediction model. The input features are generated from a number of technical indicators being used by financial experts. The genetic algorithm (GA) optimizes the NN's weights under a 2-D encoding and crossover. We devised a context-based ensemble method of NNs which dynamically changes on the basis of the test day's context. To reduce the time in processing mass data, we parallelized the GA on a Linux cluster system using message passing interface. We tested the proposed method with 36 companies in NYSE and NASDAQ for 13 years from 1992 to 2004. The neurogenetic hybrid showed notable improvement on the average over the buy-and-hold strategy and the context-based ensemble further improved the results. We also observed that some companies were more predictable than others, which implies that the proposed neurogenetic hybrid can be used for financial portfolio constructionKeywords
This publication has 27 references indexed in Scilit:
- Training neural network with genetic algorithms for forecasting the stock price indexPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Evolving artificial neural networksProceedings of the IEEE, 1999
- Making use of population information in evolutionary artificial neural networksIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1998
- A new evolutionary system for evolving artificial neural networksIEEE Transactions on Neural Networks, 1997
- Evolving artificial neural networks to combine financial forecastsIEEE Transactions on Evolutionary Computation, 1997
- Designing a neural network for forecasting financial and economic time seriesNeurocomputing, 1996
- Boosting and Other Ensemble MethodsNeural Computation, 1994
- Adaptive Mixtures of Local ExpertsNeural Computation, 1991
- Neural network ensemblesIEEE Transactions on Pattern Analysis and Machine Intelligence, 1990
- Filter Rules and Stock-Market TradingThe Journal of Business, 1966