Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Automated stock price prediction and trading framework for Nifty intraday trading(2013) Bhat, A.A.; Kamath S․, S.S.Research on automated systems for Stock price prediction has gained much momentum in recent years owing to its potential to yield profits. In this paper, we present an automatic trading system for Nifty for deciding the buying and selling calls for intra-day trading that combines various methods to improve the quality and precision of the prediction. Historical data has been used to implement the various technical indicators and also to train the Neural Network that predicts movement for intra-day Nifty. Further, Sentiment Analysis techniques are applied to popular blog articles written by domain experts and to user comments to find sentiment orientation, so that analysis can be further improved and better prediction accuracy can be achieved. The system makes a prediction for every trading day with these methods to forecast if next day will be a positive day or negative. Further, buy and sell calls for intra-day trading are also decided by the system thus achieving full automation in stock trading. © 2013 IEEE.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 A TFD Approach to Stock Price Prediction(Springer, 2020) Chanduka, B.; Bhat, S.S.; Rajput, N.; Mohan, B.R.Accurate stock price predictions can help investors take correct decisions about the selling/purchase of stocks. With improvements in data analysis and deep learning algorithms, a variety of approaches has been tried for predicting stock prices. In this paper, we deal with the prediction of stock prices for automobile companies using a novel TFD—Time Series, Financial Ratios, and Deep Learning approach. We then study the results over multiple activation functions for multiple companies and reinforce the viability of the proposed algorithm. © 2020, Springer Nature Singapore Pte Ltd.Item Stock Price Prediction Using Corporation Network and LSTM(Institute of Electrical and Electronics Engineers Inc., 2022) Bisarya, U.; Parekh, V.; Bhattacharjee, S.The problem of stock price prediction is addressed in this work by incorporating additional stock-related factors and using them to model relations between stocks. We have built a corporation network that displays the relation between stocks based on common shareholders and their shareholding ratio. The network is constructed by including all involved corporations based on investment facts from the real market. In this work, we have used a deep learning-based model, long short-term memory (LSTM) for the prediction of stock prices. We have considered node embedding methods that can store the properties of the nodes in the network, and use this information to make the model more accurate. The results produced by an initial and a revised LSTM model are compared, which have achieved a minimum mean average percentage error (MAPE) value of 4.121% for the initial LSTM model, and 1.788% for the revised LSTM model. © 2022 IEEE.