International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 03 | Mar 2023
p-ISSN: 2395-0072
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Data-Driven Approach to Stock Market Prediction and Sentiment Analysis Shreya Bamane1, Mitali Belge2, Shweta Sharad Nikam3, Ankita Umale4, Monica Charate5 1, 2, 3 Computer Science and Technology, Usha Mittal Institute of Technology,
SNDT Women’s University Computer Science and Technology, Usha Mittal Institute of Technology, SNDT Women’s University ---------------------------------------------------------------------***--------------------------------------------------------------------The stock market appears in every newspaper, every day. Abstract - Predicting stock market prices has been an 4 5Professor, Dept.
Since it is quite often intensively discussed people are always keen on knowing what will happen. People could have good returns on their investments if proper methods and algorithms are used to predict. Previous methods of stock predictions involve only the use of a single method that is using historical data. But stock markets are so volatile that they are mainly affected by events happening in the world. Therefore sentiment analysis of a stock is also required. This paper combines two things, one is historical data to predict the possible values of stocks and two, sentiment analysis to understand the sentiment for a company, giving an investor a better understanding of the stocks future. In this project the problem is solved, with a system constructed to predict news polarity which may affect changes in stock trends.
interest- ing topic because of high gains against investment over a short period of time, therefore it interests analysts and researchers for a long time. Stock prices are hard to predict because of their highly volatile nature which depends on diverse political and economic factors, change of political factors, investor sentiment, and many other factors. The method of predicting stock prices that are based on historical data or textual information alone has been proven to be insufficient. Existing studies studied by analysts in sentiment analysis have found that there is a very strong correlation between the movement of stock prices and the publication of news articles. Several sentiment analysis studies have been attempted at various levels using different machine learning algorithms such as Random Forest, etc. The prediction system in this paper shows both the results of predicting past data using Recurrent Neural Network(RNN) with Long Short- Tem Memory(LSTM) i.e RNN-LSTM algorithm and sentiment analysis using Support Vector Machine (SVM) algorithm to improve the accuracy of stock price prediction.
Time-series prediction which uses historical data is a common technique widely used in many real-world applications such as weather forecasting and financial market prediction. It uses continuous data that is the historical data over a period of time to predict the outcome of the next unit of specified time period. Numerous time series forecasting algorithms have been effective in practice. The most common algorithms are currently based on recurrent neural networks (RNNs) and a special form, Long-Short Term Memory (LSTM), it is a type of RNN. The stock market is a representative area that represents time series data, and many researchers have studied it and presented various models.
Key Words: RNN-LSTM, SVM, stock market, sentiment analysis
1. INTRODUCTION The financial market is dynamic because it keeps changing and is a composite system. This is a place where people can buy and sell currencies, stocks, equities and derivatives over virtual platforms or apps supported by brokers. The stock market has allowed investors to own shares of public companies through trading, exchange or over the counter markets. This market has given investors a great chance of gaining money and having an opportunity to live a prosperous life through investing small initial amounts of money which is low risk compared to the risk of opening a new business or the need for a high salary career that demands a great amount of effort. Stock markets are influenced by factors which are in large amounts causing the uncertainty and change in the market.
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For sentiment analysis Support Vector Machine (SVM) algorithm is used, it is a machine learning algorithm that analyzes data and recognizes patterns or decision boundaries in a data set, and is primarily used for classification and regression analysis. SVMs can handle multiple different types of variables, this nature of SVM makes it the preferred algorithm for sentiment analysis. In Module 2, of this paper SVM algorithm is used to predict the sentiment of a particular company in the market on the basis of news headlines.
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