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Utilizing Technical Data to Discover Similar Companies in Dhaka Stock Exchange

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  • Tashreef Muhammad
  • Tahsin Aziz
  • Mohammad Shafiul Alam

Abstract

Stock market investment have been an ideal form of investment for many years. Investing capitals smartly in stock market yields high profit returns. But there are many companies available in a market. Currently there are more than $345$ active companies who have stocks in Dhaka Stock Exchange (DSE). Analyzing all these companies is quite impossible. However, many companies tend to move together. This study aims at finding which companies in DSE have a close connection and move alongside each other. By analyzing this relation, the investors and traders will be able to analyze a lot of companies' statistics from a calculating just a handful number of companies. The conducted experiment yielded promising results. It was found that though the system was not given anything other than technical data, it was able to identify companies that show domain specific outcomes. In other words, a relation between technical data and fundamental data was discovered from the conducted experiment.

Suggested Citation

  • Tashreef Muhammad & Tahsin Aziz & Mohammad Shafiul Alam, 2023. "Utilizing Technical Data to Discover Similar Companies in Dhaka Stock Exchange," Papers 2301.04455, arXiv.org.
    • Handle: RePEc:arx:papers:2301.04455
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    References listed on IDEAS

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    1. Tashreef Muhammad & Anika Bintee Aftab & Md. Mainul Ahsan & Maishameem Meherin Muhu & Muhammad Ibrahim & Shahidul Islam Khan & Mohammad Shafiul Alam, 2022. "Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market," Papers 2208.08300, arXiv.org.
    2. Szczepocki Piotr, 2019. "Clustering Companies Listed on the Warsaw Stock Exchange According to Time-Varying Beta," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(2), pages 63-79, June.
    3. Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2021. "Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility," Papers 2109.12621, arXiv.org.
    4. Taewook Kim & Ha Young Kim, 2019. "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-23, February.
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