Forecasting financial indicators by generalized behavioral learning method
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Forecasting financial indicators (indexes/prices) is a complex and a quite difficult issue because they depend on many factors such as political events, financial ratios, and economic variables. Also, the psychological facts or decision-making styles of investors or experts are other major reasons for this difficulty. In this study, a generalized behavioral learning method (GBLM) was employed to forecast financial indicators, which are the indexes/prices of 34 different financial indicators (24 stock indexes, 2 forexes, 3 financial futures, and 5 commodities). The achieved results were compared with the reported results in the literature and the obtained results by artificial neural network, which is widely used and suggested for forecasting financial indicators. These results showed that GBLM can be successfully employed in short-term forecasting financial indicators by detecting hidden market behavior (pattern) from their previous values. Also, the results showed that GBLM has the ability to track the fluctuation and the main trend.
Açıklama
Anahtar Kelimeler
Forecasting financial indicators, Generalized behavioral learning method, Extreme learning machine, Hidden market behavior
Kaynak
Soft Computing
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
22
Sayı
24
