Title: Comparison of harmony search derivatives for artificial neural network parameter optimisation: stock price forecasting

Authors: Mehmet Özçalıcı; Ayşe Tuğba Dosdoğru; Aslı Boru İpek; Mustafa Göçken

Addresses: Faculty of Economics and Administrative Sciences, International Trade and Logistics Department, Kilis 7 Aralık University, 79000, Kilis, Turkey ' Engineering Faculty, Industrial Engineering Department, Adana Alparslan Türkeş Science and Technology University, 01250, Adana, Turkey ' Engineering Faculty, Industrial Engineering Department, Adana Alparslan Türkeş Science and Technology University, 01250, Adana, Turkey ' Engineering Faculty, Industrial Engineering Department, Adana Alparslan Türkeş Science and Technology University, 01250, Adana, Turkey

Abstract: This study has been conducted on forecasting, as accurately as possible, the next day's stock price using harmony search (HS) and its variants [improved harmony search (IHS), global-best harmony search (GHS), self-adaptive harmony search (SAHS), and intelligent tuned harmony Search (ITHS) together with artificial neural network (ANN)]. The advantage of the proposed models are that the useful information in the original stock data is found by input variable selection and simultaneously the most proper number of hidden neurons in hidden layer is discovered to mitigate overfitting/underfitting problem in ANN. The results have shown that forecasts made by HS-ANN, IHS-ANN, GHS-ANN, SAHS-ANN, and ITHS-ANN demonstrate a tendency to achieve hit rates above 89%, which is considerably better than previously proposed forecasting models in literature. Hence, ANN models provide more valuable forecasting results for investors to hedge against potential risk in stock markets.

Keywords: stock price forecasting; artificial neural network; harmony search and its variants.

DOI: 10.1504/IJDMMM.2022.126664

International Journal of Data Mining, Modelling and Management, 2022 Vol.14 No.4, pp.335 - 357

Received: 15 Oct 2020
Accepted: 22 Mar 2021
Published online: 01 Nov 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article