Stock Market Prediction with Deep Learning Using Financial News
| dc.contributor.author | Gündüz, Hakan | |
| dc.contributor.author | Yaslan, Yusuf | |
| dc.contributor.author | Çataltepe, Zehra | |
| dc.date.accessioned | 2020-04-30T23:32:14Z | |
| dc.date.available | 2020-04-30T23:32:14Z | |
| dc.date.issued | 2018 | |
| dc.department | DÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
| dc.description | 26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEY | en_US |
| dc.description | WOS: 000511448500469 | en_US |
| dc.description.abstract | 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. | en_US |
| dc.description.sponsorship | IEEE, Huawei, Aselsan, NETAS, IEEE Turkey Sect, IEEE Signal Proc Soc, IEEE Commun Soc, ViSRATEK, Adresgezgini, Rohde & Schwarz, Integrated Syst & Syst Design, Atilim Univ, Havelsan, Izmir Katip Celebi Univ | en_US |
| dc.identifier.isbn | 978-1-5386-1501-0 | |
| dc.identifier.issn | 2165-0608 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12684/4654 | |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.language.iso | tr | en_US |
| dc.publisher | Ieee | en_US |
| dc.relation.ispartof | 2018 26Th Signal Processing And Communications Applications Conference (Siu) | en_US |
| dc.relation.ispartofseries | Signal Processing and Communications Applications Conference | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | stock market movement prediction | en_US |
| dc.subject | Borsa Istanbul(BIST) | en_US |
| dc.subject | Long-Short Term Memory(LSTM) | en_US |
| dc.subject | word embedding | en_US |
| dc.subject | Fasttext | en_US |
| dc.title | Stock Market Prediction with Deep Learning Using Financial News | en_US |
| dc.type | Conference Object | en_US |
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