Deep Learning Based Price Prediction and Algorithmic Trading on BIST100

Ahmet Akusta and Mehmet Nuri Salur

Fiscaoeconomia, 2024, issue 3

Abstract: This research addresses using deep learning-based methodologies for trading stocks in the BIST100 index. In particular, the focus is on recent market fluctuations. A deep learning-based trading algorithm called Predictive Trading Algorithm (PTA) is developed, and its success in predicting stock movements in various sectors represented in the BIST100 is evaluated. The study is based on data from August 2022 to December 2023, covering 270 trading days. Algorithmic trading is essential in the modern financial world thanks to its efficiency, speed, and precision in trade execution. Especially in dynamic markets such as the BIST100, the importance of algorithmic trading becomes even more evident due to the difficulties of traditional strategies in adapting to rapid changes and complexities. The methodology adopted in this study involves developing and applying a deep learning model to predict future stock movements using historical price, volume, stock index, and exchange rate data. This model forms the basis of a Predictive Trading Algorithm based on a defined set of rules to execute buy or sell orders. The main findings of the research show that the PTRA achieves remarkable success with an average profit of 15.87% on the selected stocks. These results emphasize the potential of algorithmic trading and the effectiveness of using deep learning methodologies in financial markets.

Keywords: Algorithmic Trading; Price Prediction; Deep Learning; Predictive Trading Algorithm (search for similar items in EconPapers)
JEL-codes: F31 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:fis:journl:240312

DOI: 10.25295/fsecon.1447129

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