Using genetic programming to predict financial data

Abstract

This paper presents the application of genetic programming (GP) to the prediction of price data in the Japanese stock market. The goal of this task is to choose the best stocks when making an investment and to decide when and how many stocks to sell or buy. There have been several applications of genetic algorithms (GAs) to financial problems, such as portfolio optimization, bankruptcy prediction, financial forecasting, fraud detection and scheduling. GP has also been applied to many problems in time-series prediction. However, relatively few studies have been made for the purpose of predicting stock market data by means of GP. This paper describes how successfully GP is applied to predicting stock data so as to gain a high profit. Comparative experiments are conducted with neural networks to show the effectiveness of the GP-based approach.

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