A Twitter sentiment based Indian stock price forecasting using deep learning
Sanghani, Sureshkumar
A Twitter sentiment based Indian stock price forecasting using deep learning by Sureshkumar Sanghani - IIT Jodhpur Department of Computer Science and Technology 2023 - vii,29p. HB
A share market investor is always seeking advice from different investment experts to decide their next investment plan or strategy. Basically, they always look for news about a company’s (in which they are interested) future plans, current financial position, current and future order book, and the impact of local or global scenarios and local or international government policies, etc. They must familiarize themselves with all this information as well as the different opinions of various experts. Twitter is a very good source of such news or expert advice on this topic. Almost all major financial news agencies and stock market experts have their Twitter handles, which publish news and their opinions on the share market based on recent events.
We will scrape these tweets and aggregate news about Nifty Bank, Nifty IT, Nifty Energy, and Nifty Automobile stock indexes and do sentiment analysis on this data to calculate the sentiment of each day. We will use stock data of all 4 indexes and use this sentiment value of a given day as one additional variable to train a deep learning model that can predict day-level stock prices at the end of the day. The stock markets are often volatile and change abruptly due to economic conditions, political situations, and major events. Therefore, including the effect of some major events for different top stock indexes is worthwhile for the model to learn the impact and predict the price more accurately.
Department of Computer Science and Technology
Stock Index Prediction
Sentiment Analysis
MTech Theses
006.312 / S225T
A Twitter sentiment based Indian stock price forecasting using deep learning by Sureshkumar Sanghani - IIT Jodhpur Department of Computer Science and Technology 2023 - vii,29p. HB
A share market investor is always seeking advice from different investment experts to decide their next investment plan or strategy. Basically, they always look for news about a company’s (in which they are interested) future plans, current financial position, current and future order book, and the impact of local or global scenarios and local or international government policies, etc. They must familiarize themselves with all this information as well as the different opinions of various experts. Twitter is a very good source of such news or expert advice on this topic. Almost all major financial news agencies and stock market experts have their Twitter handles, which publish news and their opinions on the share market based on recent events.
We will scrape these tweets and aggregate news about Nifty Bank, Nifty IT, Nifty Energy, and Nifty Automobile stock indexes and do sentiment analysis on this data to calculate the sentiment of each day. We will use stock data of all 4 indexes and use this sentiment value of a given day as one additional variable to train a deep learning model that can predict day-level stock prices at the end of the day. The stock markets are often volatile and change abruptly due to economic conditions, political situations, and major events. Therefore, including the effect of some major events for different top stock indexes is worthwhile for the model to learn the impact and predict the price more accurately.
Department of Computer Science and Technology
Stock Index Prediction
Sentiment Analysis
MTech Theses
006.312 / S225T