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

arXiv: https://arxiv.org/abs/2208.08300 · HTML: https://mlsanity.com/paper/2208.08300/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility](https://mlsanity.com/paper/2109.12621/) (2021)
- [ANN Model to Predict Stock Prices at Stock Exchange Markets](https://mlsanity.com/paper/1502.06434/) (2014)

## Built on by (most cited first, 9 of 9)

- [Road Damage and Manhole Detection using Deep Learning for Smart Cities: A Polygonal Annotation Approach](https://mlsanity.com/paper/2510.03797/) (2025)
- [DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting](https://mlsanity.com/paper/2405.00522/) (2024)
- [DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection](https://mlsanity.com/paper/2407.13427/) (2024)
- [Gated Fusion Enhanced Multi-Scale Hierarchical Graph Convolutional Network for Stock Movement Prediction](https://mlsanity.com/paper/2511.01570/) (2025)
- [A Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data](https://mlsanity.com/paper/2311.10025/) (2023)
- [Comprehensive Modeling Approaches for Forecasting Bitcoin Transaction Fees: A Comparative Study](https://mlsanity.com/paper/2502.01029/) (2025)
- [Dynamic Factor Analysis of Price Movements in the Philippine Stock Exchange](https://mlsanity.com/paper/2510.15938/) (2025)
- [GAPNet: Plug-in Jointly Learning Task-Specific Graph for Dynamic Stock Relation](https://mlsanity.com/paper/2602.00888/) (2026)
- [A Benchmark of Classical and Deep Learning Models for Agricultural Commodity Price Forecasting on A Novel Bangladeshi Market Price Dataset](https://mlsanity.com/paper/2604.06227/) (2026)

## Abstract (arXiv)

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.