Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Time series with sentiment analysis for stock price prediction(Institute of Electrical and Electronics Engineers Inc., 2019) Sharma, V.; Khemnar, R.; Kumari, R.; Mohan, B.R.Stock price prediction has been a major area of research for many years. Accurate predictions can help investors take correct decisions about the selling/purchase of stocks. This paper aims to predict and gauge stock costs and patterns, utilizing the power of machine learning, content examination and fundamental analysis, to give traders a hands-on tool for keen speculations particularly for the volatile Indian Stock Market. We propose a technique to analyze and predict the stock price with the help of sentiment analysis and decomposable time series model along with multivariate-linear regression. © 2019 IEEE.Item Stock Market Prediction Using Historical Stock Prices And Dependence On Other Companies In Automotive Sector(Institute of Electrical and Electronics Engineers Inc., 2022) Sharma, N.; Mohan, B.R.Stock market investment, due to its volatile nature and dependence on many factors like own company policies, dependence on other companies' stock value, people's outlook on the company, etc., is a big gamble. However, if understood, it can heap in big rewards to investors. This is one of the reasons why stock market analysis has been such a hot topic and a highly researched field. Fundamental and Technical analysis are two ways to study and predict future company stocks. A lot of work has been done previously to predict stock prices using either sentiment analysis or historical stock data, but a very little emphasis has been put on combining multiple factors to predict stock prices. In this study, we will work on companies registered in the automotive sector in NSE. We have focused on historical companies' stock details and the dependence of stock price of one company on other companies in the same sector to predict future stocks. Both of these factors were studied and analyzed, and then a comparative analysis was done to see which model better predicts the closing stock price of Tata Motors, our target company. We have used Autoregressive integrated moving average, Artificial Neural Network, Long Short-Term Memory (LSTM), a type of Recurrent Neural Network models in our research and a comparative analysis among them will be done. © 2022 IEEE.