A Hybrid Neurogenetic Approach for Stock Forecasting

Yung‐Keun Kwon · Byung-Ro Moon

175 citations0/0 files storedIEEE Transactions on Neural Networks

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Stock Market Forecasting Methods Neural Networks and Applications Forecasting Techniques and Applications

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 construction.

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References

1970EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK*1970Efficient Capital Markets: A Review of Theory and Empirical Work 1996Designing a neural network for forecasting financial and economic time series 2003Support vector machine with adaptive parameters in financial time series forecasting 1968The Random Character of Stock Market Prices 1999Using genetic algorithms to find technical trading rules 1966Filter Rules and Stock-Market Trading 1961Price Movements in Speculative Markets : Trends or Random Walks

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2010A method for automatic stock trading combining technical analysis and nearest neighbor classification 2016Financial Time-Series Data Analysis Using Deep Convolutional Neural Networks 2008Trend Time–Series Modeling and Forecasting With Neural Networks 2018Automated trading systems statistical and machine learning methods and hardware implementation: a survey 2008Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction

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