Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Stock price movements classification using machine and deep learning techniques-the case study of indian stock market(Springer Verlag service@springer.de, 2019) Naik, N.; Mohan, B.R.Stock price movements forecasting is an important topic for traders and stock analyst. Timely prediction in stock yields can get more profits and returns. The predicting stock price movement on a daily basis is a difficult task due to more ups and down in the financial market. Therefore, there is a need for a more powerful predictive model to predict the stock prices. Most of the existing work is based on machine learning techniques and considered very few technical indicators to predict the stock prices. In this paper, we have extracted 33 technical indicators based on daily stock price such as open, high, low and close price. This paper addresses the two problems, first is the technical indicator feature selection and identification of the relevant technical indicators by using Boruta feature selection technique. The second is an accurate prediction model for stock price movements. To predict stock price movements we have proposed machine learning techniques and deep learning based model. The performance of the deep learning model is better than the machine learning techniques. The experimental results are significant improves the classification accuracy rate by 5% to 6%. National Stock Exchange, India (NSE) stocks are considered for the experiment. © Springer Nature Switzerland AG 2019.Item Optimal Feature Selection of Technical Indicator and Stock Prediction Using Machine Learning Technique(Springer Verlag service@springer.de, 2019) Naik, N.; Mohan, B.R.Short-term trading is a difficult task due to fluctuating demand and supply in the stock market. These demands and supply are reflected in stock prices. The stock prices may be predicted using technical indicators. Most of the existing literature considered the limited technical indicators to measure short-term prices. We have considered 33 different combinations of technical indicators to predict the stock prices. The paper has two objectives, first is the technical indicator feature selection and identification of the relevant technical indicators by using Boruta feature selection technique. The second objective is an accurate prediction model for stocks. To predict stock prices we have proposed ANN (Artificial Neural Network) Regression prediction model and model performance is evaluated using metrics is Mean absolute error (MAE) and Root mean square error (RMSE). The experimental results are better than the existing method by decreasing the error rate in the prediction to 12%. We have used the National Stock Exchange, India (NSE) data for the experiment. © 2019, Springer Nature Singapore Pte Ltd.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.