Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

Entropy

卷:22

Sina Ardabili¹  Fatih Ecer²  Amir Mosavi³  ShahabS. Band⁴ 

[1] Biosystem Engineering Department, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran;

[2] Department of Business Administration, Afyon Kocatepe University, 03030 Afyonkarahisar, Turkey;

[3] Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany;

[4] Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan;

关键词: stock market;    machine learning;    multilayer perceptron;    financial data;    artificial intelligence;    artificial neural networks;   

DOI  :  10.3390/e22111239

来源: DOAJ

【 摘 要 】

Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs’ direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron–genetic algorithms (MLP–GA) and multilayer perceptron–particle swarm optimization (MLP–PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP–PSO with population size 125, followed by MLP–GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.

【 授权许可】

Unknown