Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market

Tashreef Muhammad, Anika Bintee Aftab, Md. Mainul Ahsan, Maishameem Meherin Muhu, Muhammad Ibrahim, Shahidul Islam Khan and Mohammad Shafiul Alam

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Abstract: In modern capital market the price of a stock is often considered to be highly volatile and unpredictable because of various social, financial, political and other dynamic factors. With calculated and thoughtful investment, stock market can ensure a handsome profit with minimal capital investment, while incorrect prediction can easily bring catastrophic financial loss to the investors. This paper introduces the application of a recently introduced machine learning model - the Transformer model, to predict the future price of stocks of Dhaka Stock Exchange (DSE), the leading stock exchange in Bangladesh. The transformer model has been widely leveraged for natural language processing and computer vision tasks, but, to the best of our knowledge, has never been used for stock price prediction task at DSE. Recently the introduction of time2vec encoding to represent the time series features has made it possible to employ the transformer model for the stock price prediction. This paper concentrates on the application of transformer-based model to predict the price movement of eight specific stocks listed in DSE based on their historical daily and weekly data. Our experiments demonstrate promising results and acceptable root mean squared error on most of the stocks.

Date: 2022-08
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-for
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Citations: View citations in EconPapers (1)

Published in Preprint of an article published in [International Journal of Computational Intelligence and Applications, Volume 22, No. 01, 2023] \c{opyright} [copyright World Scientific Publishing Company] [https://www.worldscientific.com/worldscinet/ijcia]

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