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

Mehmet Özçalıcı, Ayşe Tuğba Dosdoğru, Aslı Boru İpek and Mustafa Göçken

International Journal of Data Mining, Modelling and Management, 2022, vol. 14, issue 4, 335-357

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. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=126664 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:14:y:2022:i:4:p:335-357

Access Statistics for this article

More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().