Stock Market Price movement prediction using RNN and Point-weight Sentiment

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022

Détails bibliographiques
Auteurs principaux:	Uddin, S. M. Rageeb Noor, Naim, Jannatul Arafat, Pranjol, Mashuk Arefin, Ashrafi, Almas, Emon, Ibthasham Amin
Autres auteurs:	Rashid, Warida
Format:	Thèse
Langue:	en_US
Publié:	Brac University 2022
Sujets:	Social media Twitter Newspaper Stock Market Prediction Point-weight Sentiment RNN LSTM NLP VADER Machine learning
Accès en ligne:	http://hdl.handle.net/10361/17200

id	10361-17200
record_format	dspace
spelling	10361-172002022-09-12T21:01:36Z Stock Market Price movement prediction using RNN and Point-weight Sentiment Uddin, S. M. Rageeb Noor Naim, Jannatul Arafat Pranjol, Mashuk Arefin Ashrafi, Almas Emon, Ibthasham Amin Rashid, Warida Department of Computer Science and Engineering, Brac University Social media Twitter Newspaper Stock Market Prediction Point-weight Sentiment RNN LSTM NLP VADER Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022 Cataloged from PDF version of thesis. Includes bibliographical references (pages 24-26). Predicting the price of stocks has always been an exciting and challenging field for academics and investors for a long time as it helps to gain high-profit margins for investment companies, investors, and emerging advanced automated trading bots. Existing forecasting algorithms and studies on statistical models using sentiment analysis have shown promising results. However, due to the highly volatile nature of the stock market and many private and public variables that directly affect the market, it is very challenging to predict prices for extreme situations with reasonable accuracy. This study introduces a point-weight algorithm for tweets and news to gain a similar pattern as stock prices, combined with stock data and feed into the RNN network for time-series prediction. We will experiment with different mechanisms for point-weight algorithms to compare results, correlate with stock price patterns and changes while focusing on accuracy. Furthermore, we will experiment with other multivariate stocks and different architecture of RNN to find how it affects the accuracy of model training. S. M. Rageeb Noor Uddin Jannatul Arafat Naim Mashuk Arefin Pranjol Almas Ashraf Ibthasham Amin Emon B. Computer Science and Engineering 2022-09-12T07:21:02Z 2022-09-12T07:21:02Z 2022 2022-01 Thesis ID: 17201110 ID: 17201119 ID: 17201094 ID: 17201111 ID: 17201135 http://hdl.handle.net/10361/17200 en_US 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. 26 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	en_US
topic	Social media Twitter Newspaper Stock Market Prediction Point-weight Sentiment RNN LSTM NLP VADER Machine learning
spellingShingle	Social media Twitter Newspaper Stock Market Prediction Point-weight Sentiment RNN LSTM NLP VADER Machine learning Uddin, S. M. Rageeb Noor Naim, Jannatul Arafat Pranjol, Mashuk Arefin Ashrafi, Almas Emon, Ibthasham Amin Stock Market Price movement prediction using RNN and Point-weight Sentiment
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022
author2	Rashid, Warida
author_facet	Rashid, Warida Uddin, S. M. Rageeb Noor Naim, Jannatul Arafat Pranjol, Mashuk Arefin Ashrafi, Almas Emon, Ibthasham Amin
format	Thesis
author	Uddin, S. M. Rageeb Noor Naim, Jannatul Arafat Pranjol, Mashuk Arefin Ashrafi, Almas Emon, Ibthasham Amin
author_sort	Uddin, S. M. Rageeb Noor
title	Stock Market Price movement prediction using RNN and Point-weight Sentiment
title_short	Stock Market Price movement prediction using RNN and Point-weight Sentiment
title_full	Stock Market Price movement prediction using RNN and Point-weight Sentiment
title_fullStr	Stock Market Price movement prediction using RNN and Point-weight Sentiment
title_full_unstemmed	Stock Market Price movement prediction using RNN and Point-weight Sentiment
title_sort	stock market price movement prediction using rnn and point-weight sentiment
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/17200
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_version_	1814308147575128064

Stock Market Price movement prediction using RNN and Point-weight Sentiment

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