Performing stock price prediction use of hybrid model

Abstract

PurposeThe purpose of this paper is to propose an analysis method based on a hybrid model, which combines principal component regression (PCR) model and general regression neural network (GRNN) to solve both multicollinearity problems and non‐linear problems at the same time.Design/methodology/approachFirst, the financial ratio data of companies with stocks listed in regular stock market and over‐the‐counter stock market in Taiwan and Mainland China are collected and used as sample data. Grey relational analysis is used to rank the enterprises' operation performance, and the enterprises in Taiwan and Mainland China with business operation performance in the first place are selected and their stock information collected to perform the prediction of stock closing price.FindingsFive indices such as the root mean square error, revision Theil inequality coefficient, mean absolute error, mean absolute percentage error and coefficient of efficiency of the test result are calculated; the empirical results show that the prediction power of the hybrid model of PCR+genetic algorithm general regression neural network is obviously better than the model of PCR, GRNN and PCR+GRNN.Originality/valueThe paper adopts a hybrid model and parameter adjustment to increase prediction capability.