An improved predictor of daily stock index based on a genetic filter

Abstract

In this study, a genetic algorithm-based filter feature selection was applied to the data on the rates of change in various economic indices used worldwide to predict the rates of change of the Korea Composite Stock Price Index (KOSPI). The fitness function is composed of a combination of the results of information gains, F-test, and correlation coefficients. Data for the past 12 years (from 2007 to 2018) were divided into sections according to the yearly time intervals, and feature selection was applied to the data in each section. It was found that the amount of calculation time required for a genetic filter was approximately 75% lower compared to that required for a previous genetic wrapper. It was also found that the mean absolute error of the genetic filter was approximately 26% lower compared to that of the genetic wrapper. The analytic results verified that the genetic filter exhibited better calculation performance and required less time to calculate the optimal solution. Furthermore, a new combination of features related to the KOSPI was obtained.