IntelliFin: Advanced Stock Prediction using Hybrid ML and LSTM Model with Financial Indicators powered by Sentiment Determination using NLP
Shashank Singh1, Maaz Ahmad2, Aditya Bhattacharya3, D. Prabhu4
1Shashank Singh*, Department of Electrical Computer Science and Engineering from SRM Institute of Science and Technology, Chennai.
2Maaz Ahmad, Department of Electrical Computer Science and Engineering from SRM Institute of Science and Technology, Chennai.
3Aditya Bhattacharya, Department of Electrical Computer Science and Engineering from SRM Institute of Science and Technology, Chennai.
4D. Prabhu, Assistant Professor in Computer Science and Engineering at SRM Institute of Science and Technology, Chennai.
Manuscript received on April 11, 2020. | Revised Manuscript received on May 15, 2020. | Manuscript published on June 30, 2020. | PP: 399-403 | Volume-9 Issue-5, June 2020. | Retrieval Number: D8437049420/2020©BEIESP | DOI: 10.35940/ijeat.D8437.069520
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Stock Trading has been one of the most important parts of the financial world for decades. People investing in the share market analyze the financial history of a corporation, the news related to it and study huge amounts of data so as to predict its stock price trend. The right investment i.e. buying and selling a company stock at the right time leads to monetary benefits and can make one a millionaire overnight. The stock market is an extremely fluctuating platform wherein data is produced in humongous quantities and is influenced by numerous disparate factors such as socio-political issues, financial activities like splits and dividends, news as well as rumors. This work proposes a novel system “IntelliFin” to predict the share market trend. The system uses the various stock market technical indicators along with the company’s historical market data trends to predict the share prices. The system employs the sentiment determination of a company’s financial and socio-political news for a more accurate prediction. This system is implemented using two models. The first is a hybrid LSTM model optimized by an ADAM optimizer. The other is a hybrid ML model which integrates a Support Vector Regressor, K-Nearest Neighbor classifier, an RF classifier and a Linear Regressor using a Majority Voting algorithm. Both models employ a sentiment analyzer to account for the news impacting the stock prices which is powered by NLP. The models are trained continuously using Reinforcement Learning implemented by the Q-Learning Algorithm to increase the consistency and accuracy. The project aims to support the inexperienced investors, who don’t have enough experience in investing in the stock market and help them maximize their profit and minimize or eliminate the losses. The developed system will also serve as a tool for professional investors to help and aid their decision making.
Keywords: LSTM, Support Vector Regressor, K-Nearest Neighbor Classifier, RF Classifier, Sentiment Determination, NLP, Linear Regression, Reinforcement Learning