Stock Market Prediction with Deep Learning Using Financial News
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Dosyalar
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, the hourly movement directions of 9 banking stocks in Borsa Istanbul were predicted using Long-Short Term Memory(LSTM) networks with features obtained from financial news. In the feature creation phase, the word embedding referred as Fasttext, and the financial sentiment dictionary were utilized. Class labels indicating the movement direction were computed based on hourly close prices of the stocks and they were aligned with obtained feature vectors. Two different LSTM networks were trained to perform the prediction, and the performance of the classification process was evaluated by the Macro Averaged (M.A) F-Measure. In the experiments, the movement directions of the 9 stocks were predicted with an average M.A F-measure rate of 0.540. Although the results of both LSTM networks were higher than the Random and Naive benchmark methods, the use of Attention Mechanism in the second LSTM network did not positively affect the results.
Açıklama
26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEY
WOS: 000511448500469
WOS: 000511448500469
Anahtar Kelimeler
deep learning, stock market movement prediction, Borsa Istanbul(BIST), Long-Short Term Memory(LSTM), word embedding, Fasttext
Kaynak
2018 26Th Signal Processing And Communications Applications Conference (Siu)
WoS Q Değeri
N/A
