The Application of Genetic Programming on the Stock Movement Forecasting System

Abstract

The financial tsunami is a crisis that happened in 2007. It broke out in the United States, and then spread to the whole world. Taiwanese economy exhibited a negative growth of 7.53%, and the fluctuation is manifest in Taiwan stock index. It has been even dramatically losing 60%. Now, TAIEX has exceeded the level before the financial crisis. TAIEX closed at 10,383.94 on September 30, 2017. The establishment of the Stock Movement Forecasting System has become an important issue. This paper intends to demonstrate the application of an artificial intelligence system named GPLAB on the prediction of stock price movement in TWSE. GPLAB was built on biological evolutionary concept to realize fittest surviving rules in the natural selection process. This concept has been applied on the field of finance to build up forecasting models predicting future price movement within one day, one month and one season. The empirical results of this inter-discipline study has revealed this bio-financial hybrid system successfully predicted the stock price movement in a one-month forecasting range by 23% and 22% lower than the appointed benchmark during a random chosen period and a bear market period respectively. This empirical evidence suggests the market efficiency in TWSE is a semi-strong form market that stock price movement could be predicted with the analysis of historical data. This paper also further indicates the credibility of different technical and fundamental factors regarding to the prediction of future price movement in four different market situations including non-specific, static, bull and bear market period. At the end of this paper also revealed the strength and weakness of GPLAB as a financial forecasting tool. A short discussion concerning the system improvements regarding to the application of GPLAB is also included. Keywords: Stock Movement Forecasting, GP , Genetic Programming JEL Classifications: G1, C9, C6