Comparison of harmony search derivatives for artificial neural network parameter optimisation: stock price forecasting
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Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Inderscience Enterprises Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
stock price forecasting, artificial neural network, harmony search and its variants
Kaynak
International Journal of Data Mining Modelling and Management
WoS Q Değeri
N/A
Scopus Q Değeri
Q4
Cilt
14
Sayı
4
