Predicting Stock Market Trends using Hybrid SVM Model and LSTM with Sentiment Determination using Natural Language Processing
shashank Singh1, maaz Ahmad2, aditya Bhattacharya3, m. Azhagiri4
1Shashank Singh, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu.
2Maaz Ahmad, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu.
3Aditya Bhattacharya, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu.
4M. Azhagiri, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2870-2875 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1106109119/2019©BEIESP | DOI: 10.35940/ijeat.A1106.109119
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© 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: In the financial world, stock trading is one of the most crucial activities. Investors make educated guesses to predict stock market trends by analyzing news, studying the company history, industrial history and a lot of other data. successfully predicting the stock market trends and investing in the right shares at the right time can maximize the investor’s profit or at least minimize the losses. Stock market price data is generated in huge volumes and is affected by various diverse factors. This work proposes two models to predict the stock market prices. The first model is an LSTM model that employs a backpropagation optimized LSTM network to forecast future stock prices. The second model is a hybrid model that combines an SVM model, KNN model and a Random Forest classifier using the Majority Voting algorithm to predict stock market trends. Both models have a sentiment analyzer to factor the news influencing the stock market using Natural Language Processing (NLP). The project aims to help investors who are new to the stock market and don’t possess sufficient knowledge to make share investments as well the experienced investors by predicting stock market trends.
Keywords: Stock Prediction, LSTM, SVM, KNN, Random Forest, Majority Voting, Sentiment Analysis, Natural Language Processing (NLP).