Big data–enabled sign prediction for Borsa Istanbul intraday equity prices

Name: Big data–enabled sign prediction for Borsa Istanbul intraday equity prices
Author: Kılıç, A., Güloğlu, B., Yalçın, Atakan, Üstündağ, A.

İsim	Big data–enabled sign prediction for Borsa Istanbul intraday equity prices
Yazar	Kılıç, A., Güloğlu, B., Yalçın, Atakan, Üstündağ, A.
Basım Tarihi:	2023-12
Basım Yeri	- Elsevier
Konu	Borsa Istanbul, Data analytics, Intraday, Machine learning, Market efficiency, Sign prediction
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2214-8450
Kayıt Numarası	9456a574-733c-4fdc-a7dc-bc254937de92
Lokasyon	International Finance
Tarih	2023-12
Örnek Metin	This paper employs a big data source, the Borsa Istanbul's “data analytics” information, to predict 5-min up, down, and steady signs drawn from closing price changes. Seven machine learning algorithms are compared with 2018 data for the entire year. Success levels for each method are reported for 26 liquid stocks in terms of macro-averaged F-measures. For the 5-min lagged data, nine equities are found to be statistically predictable. For lagged data over longer periods, equities remain predictable, decreasing gradually to zero as the markets absorb the data over time. Furthermore, economic gains for the nine equities are analyzed with algorithms where short selling is allowed or not allowed depending on these predictions. Four equities are found to yield more economic gains via machine learning–supported trading strategies than the equities' own price performances. Under the “efficient market hypothesis,” the results imply a lack of “semistrong-form efficiency.”
DOI	10.1016/j.bir.2023.08.005
Cilt	23

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Big data–enabled sign prediction for Borsa Istanbul intraday equity prices

Yazar Kılıç, A., Güloğlu, B., Yalçın, Atakan, Üstündağ, A.

Basım Tarihi 2023-12

Basım Yeri - Elsevier

Konu Borsa Istanbul, Data analytics, Intraday, Machine learning, Market efficiency, Sign prediction

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2214-8450

Kayıt Numarası 9456a574-733c-4fdc-a7dc-bc254937de92

Lokasyon International Finance

Tarih 2023-12

Örnek Metin This paper employs a big data source, the Borsa Istanbul's “data analytics” information, to predict 5-min up, down, and steady signs drawn from closing price changes. Seven machine learning algorithms are compared with 2018 data for the entire year. Success levels for each method are reported for 26 liquid stocks in terms of macro-averaged F-measures. For the 5-min lagged data, nine equities are found to be statistically predictable. For lagged data over longer periods, equities remain predictable, decreasing gradually to zero as the markets absorb the data over time. Furthermore, economic gains for the nine equities are analyzed with algorithms where short selling is allowed or not allowed depending on these predictions. Four equities are found to yield more economic gains via machine learning–supported trading strategies than the equities' own price performances. Under the “efficient market hypothesis,” the results imply a lack of “semistrong-form efficiency.”

DOI 10.1016/j.bir.2023.08.005

Cilt 23