Forecasting Dhaka stock exchange prices using machine learning models: a Performance analysis

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

التفاصيل البيبلوغرافية
المؤلفون الرئيسيون:	Alam, Md. Iftekharul, Rahman, MD Jubaier, Shakil, Nurul Islam, Ibnat, Maisha, Tasnia, Rifah
مؤلفون آخرون:	Noor, Jannatun
التنسيق:	أطروحة
اللغة:	English
منشور في:	Brac University 2024
الموضوعات:	Financial markets Investors Predicting stock prices Historical data Predict future values Forecasting Market movement Machine learning Artificial intelligence
الوصول للمادة أونلاين:	http://hdl.handle.net/10361/23075

id	10361-23075
record_format	dspace
spelling	10361-230752024-06-03T21:01:57Z Forecasting Dhaka stock exchange prices using machine learning models: a Performance analysis Alam, Md. Iftekharul Rahman, MD Jubaier Shakil, Nurul Islam Ibnat, Maisha Tasnia, Rifah Noor, Jannatun Department of Computer Science and Engineering, Brac University Financial markets Investors Predicting stock prices Historical data Predict future values Forecasting Market movement Machine learning Artificial intelligence This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 64-69). The financial markets have always been a focal point of interest for investors, analysts, and researchers. Predicting stock prices accurately has remained a challenging task. For many years, academics are analyzing the historical data to predict the prices; the most challenging and profitable use has been stock valuation forecasting. However, only a tiny portion of the elements which impact market movement can be measured. Examples of these factors include transaction volume, previous prices, and current prices. These variety of factors makes machine learning-based stock price prediction challenging and, to certain levels, questionable. Statistical and machine learning algorithms are used to predict short-term fluctuations in markets on an average market day, assuming there is ample historical data and factors available. This research uses a variety of machine learning techniques to present several comparison models for stock price prediction like LSTM, GRU and Nbeats and ARIMA. Historical data gathered from the official website of the Dhaka Stock Exchange (DSE) was used to train the models. Factors such as Date, Volume, Open, High, Low Close prices are included in the financial data. Conventional strategic metrics such as Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE) R-Squared and Mean Absolute Error (MAE) were used for assessing the models. Furthermore, since stock prices are impacted by various other real - world factors other than numerical data, this research attempts to incorporate external factors like political situations, daily grocery prices and corruption to the existing numerical variables in stock prediction. Our research contributes to the developing repository of knowledge in machine learning of machine learning for financial forecasting with significant implications for investors, financial institutions, and policymakers who depend on detailed stock price predictions to make informed decisions. The opportunity for more study in this area is outlined in the thesis conclusion, along with the practical ramifications of the results for the larger financial sector. Md. Iftekharul Alam MD Jubaier Rahman Nurul Islam Shakil Maisha Ibnat Rifah Tasnia B.Sc in Computer Science 2024-06-03T04:48:00Z 2024-06-03T04:48:00Z 2023 2024-01 Thesis ID 19301265 ID 19301179 ID 19301093 ID 19101680 ID 23341140 http://hdl.handle.net/10361/23075 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 69 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Financial markets Investors Predicting stock prices Historical data Predict future values Forecasting Market movement Machine learning Artificial intelligence
spellingShingle	Financial markets Investors Predicting stock prices Historical data Predict future values Forecasting Market movement Machine learning Artificial intelligence Alam, Md. Iftekharul Rahman, MD Jubaier Shakil, Nurul Islam Ibnat, Maisha Tasnia, Rifah Forecasting Dhaka stock exchange prices using machine learning models: a Performance analysis
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
author2	Noor, Jannatun
author_facet	Noor, Jannatun Alam, Md. Iftekharul Rahman, MD Jubaier Shakil, Nurul Islam Ibnat, Maisha Tasnia, Rifah
format	Thesis
author	Alam, Md. Iftekharul Rahman, MD Jubaier Shakil, Nurul Islam Ibnat, Maisha Tasnia, Rifah
author_sort	Alam, Md. Iftekharul
title	Forecasting Dhaka stock exchange prices using machine learning models: a Performance analysis
title_short	Forecasting Dhaka stock exchange prices using machine learning models: a Performance analysis
title_full	Forecasting Dhaka stock exchange prices using machine learning models: a Performance analysis
title_fullStr	Forecasting Dhaka stock exchange prices using machine learning models: a Performance analysis
title_full_unstemmed	Forecasting Dhaka stock exchange prices using machine learning models: a Performance analysis
title_sort	forecasting dhaka stock exchange prices using machine learning models: a performance analysis
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/23075
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Forecasting Dhaka stock exchange prices using machine learning models: a Performance analysis

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