  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.
