Asset price and direction prediction via deep 2D transformer and convolutional neural networks

Name: Asset price and direction prediction via deep 2D transformer and convolutional neural networks
Author: Tuncer, Tuna, Kaya, Uygar, Sefer, Emre, Alacam, Onur, Hoşer, Tuğcan

İsim	Asset price and direction prediction via deep 2D transformer and convolutional neural networks
Yazar	Tuncer, Tuna, Kaya, Uygar, Sefer, Emre, Alacam, Onur, Hoşer, Tuğcan
Basım Tarihi:	2022-11-02
Basım Yeri	- ACM
Konu	Attention, Convolutional neural networks, Deep learning, Stock price prediction, Transformers
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-4503-9376-8
Kayıt Numarası	e1120373-85f5-4398-a694-e8f270ed89dc
Lokasyon	Computer Science
Tarih	2022-11-02
Örnek Metin	Artificial intelligence-based algorithmic trading has recently started to attract more attention. Among the techniques, deep learning-based methods such as transformers, convolutional neural networks, and patch embedding approaches have become quite popular inside the computer vision researchers. In this research, inspired by the state-of-the-art computer vision methods, we have come up with 2 approaches: DAPP (Deep Attention-based Price Prediction) and DPPP (Deep Patch-based Price Prediction) that are based on vision transformers and patch embedding-based convolutional neural networks respectively to predict asset price and direction from historical price data by capturing the image properties of the historical time-series dataset. Before applying attention-based architecture, we have transformed historical time series price dataset into two-dimensional images by using various number of different technical indicators. Each indicator creates data for a fixed number of days. Thus, we construct two-dimensional images of various dimensions. Then, we use original images valleys and hills to label each image as Hold, Buy, or Sell. We find our trained attention-based models to frequently provide better results for ETFs in comparison to the baseline convolutional architectures in terms of both accuracy and financial analysis metrics during longer testing periods.
DOI	10.1145/3533271.3561738

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Asset price and direction prediction via deep 2D transformer and convolutional neural networks

Yazar Tuncer, Tuna, Kaya, Uygar, Sefer, Emre, Alacam, Onur, Hoşer, Tuğcan

Basım Tarihi 2022-11-02

Basım Yeri - ACM

Konu Attention, Convolutional neural networks, Deep learning, Stock price prediction, Transformers

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-4503-9376-8

Kayıt Numarası e1120373-85f5-4398-a694-e8f270ed89dc

Lokasyon Computer Science

Tarih 2022-11-02

Örnek Metin Artificial intelligence-based algorithmic trading has recently started to attract more attention. Among the techniques, deep learning-based methods such as transformers, convolutional neural networks, and patch embedding approaches have become quite popular inside the computer vision researchers. In this research, inspired by the state-of-the-art computer vision methods, we have come up with 2 approaches: DAPP (Deep Attention-based Price Prediction) and DPPP (Deep Patch-based Price Prediction) that are based on vision transformers and patch embedding-based convolutional neural networks respectively to predict asset price and direction from historical price data by capturing the image properties of the historical time-series dataset. Before applying attention-based architecture, we have transformed historical time series price dataset into two-dimensional images by using various number of different technical indicators. Each indicator creates data for a fixed number of days. Thus, we construct two-dimensional images of various dimensions. Then, we use original images valleys and hills to label each image as Hold, Buy, or Sell. We find our trained attention-based models to frequently provide better results for ETFs in comparison to the baseline convolutional architectures in terms of both accuracy and financial analysis metrics during longer testing periods.

DOI 10.1145/3533271.3561738