Stock Market Prediction with Stacked Autoencoder Based Feature Reduction

Basit Öğe Kaydı
dc.contributor.authorGunduz, Hakan
dc.date.accessioned2021-12-01T18:47:12Z
dc.date.available2021-12-01T18:47:12Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.description28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORKen_US
dc.description.abstractIn this study, the hourly movement direction of 9 banking stocks traded on Borsa Istanbul was predicted by Long-Short Term Memory (LSTM) network. In the prediction process raw stock prices, logarithmic scale stock prices and 11 different technical indicators were used. 1-hour samples of stocks were represented with 63 features with technical indicators computed for 5 different time periods. Class labels indicating the hourly movement direction were assigned based on the hourly closing prices of the stocks. Two different Long-Short Term Memory (LSTM) models were proposed in the prediction process. In the training of the first LSTM model, individual stock features were used, whereas in the second LSTM model, the features of all stocks were given as network inputs. The use of all stock features increased the size of the feature space to 567, and stacked autoencoders were used for dimensionality reduction. According to the experiments, the movement directions of 9 stocks were predicted with an average Macro-Averaged F-Measure rate of 0.573. The use of all stock features increased the prediction performance of the stocks by %0.9-1.9. The performance of both LSTM networks outperformed the Random and Naive benchmarking methods.en_US
dc.description.sponsorshipIstanbul Medipol Univen_US
dc.identifier.isbn978-1-7281-7206-4
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85100321788en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10185
dc.identifier.wosWOS:000653136100364en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorGunduz, Hakan
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2020 28Th Signal Processing And Communications Applications Conference (Siu)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectStock market predictionen_US
dc.subjectBorsa Istanbulen_US
dc.subjectlong-short term memoryen_US
dc.subjectstacked auto encodersen_US
dc.subjectNetworksen_US
dc.titleStock Market Prediction with Stacked Autoencoder Based Feature Reductionen_US
dc.typeConference Objecten_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
10185.pdf
Boyut:
135.17 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu