Author
Tuncer, Tuna, Kaya, Uygar, Sefer, Emre, Alacam, Onur, Hoşer, Tuğcan
Publication Date
2022-11-02
Publication Place
-
ACM
Subject
Attention, Convolutional neural networks, Deep learning, Stock price prediction, Transformers
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-1-4503-9376-8
Record ID
e1120373-85f5-4398-a694-e8f270ed89dc
Library Location
Computer Science
Date
2022-11-02
Sample Text
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
