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.
Highlights
The Share market happens to be an erratic system in which copious data is produced at frequent intervals and fluctuates rapidly due to several disparate factors
The Deep Learning (DL) algorithms like Recurrent Neural Network (RNN) are applied to the stock market data in an attempt to predict the stock prices
Systems and algorithms have been utilized in an attempt to predict the stock market trends
Summary
The Share market happens to be an erratic system in which copious data is produced at frequent intervals and fluctuates rapidly due to several disparate factors. In the domain of Machine Learning, the algorithms such as the RF Classifier, KNN or Decision Trees all fail to function as efficient regressors and deliver poor accuracy in predicting the stock prices. The most popular SVM Regressor is a more accurate and consistent ML algorithm but has its limitations The results it delivers are dependent on how its parameters are tuned and optimized and the selection of an appropriate kernel poses a big problem in time-series problems. In the domain of Deep Learning, RNN and ANN networks are a popular choice when it comes to predicting the stock prices. These two have their limitations and drawbacks such as the Vanishing Gradient problem of RNN. A Sentiment Analyzer is a part of both the models, that factors the news sentiment affecting the stock market for more accurate results
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