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Stock Closing Price Prediction using Machine Learning SVM Model

2020; International Journal for Research in Applied Science and Engineering Technology (IJRASET); Volume: 8; Issue: 11 Linguagem: Inglês

10.22214/ijraset.2020.32154

2321-9653

Desai Mitesh Madhusudan,

Stock Market Forecasting Methods

Stock market prediction is one of the most important things in financial world as it decides the flow of a company towards profit or loss in future. Prediction of a stock value of a particular entity or a company is a tough and difficult thing to do. Since early 1980 s investing money into the companies by taking some of its stock had become a big thing (trend). After 2000 s some Machine Learning, Artificial Intelligence concepts were introduced to make prediction a stock value. There are many Machine learning models such as Support Vector Machine (SVM) model, Regression model, LSTM model, Artificial Neural Network (ANN) etc. had used to make predictions by considering previous year data as sample. By far SVM model has shown most positive results by predicting stock value accurately. Since all this models were tested on little quantity of particular data set, if we take large amount of data (previous) as input then it will bring more accuracy in prediction. Also by considering Financial NEWS articles, Social Media trends, Company's work ethics and taking all this variables as input along with the previous year data will make it easy for models to predict the value more accurately. SVM-based stock market trend prediction system can find out a good subset and evaluate stock indicators which provide useful information for investors and produces better generalization performance over the conventional methods in terms of the hit ratio. In this paper, I proposed how accurately a SVM model can check the stock closing price of a Tesla Inc. which is a Tech company and Reliance Industries Limited which is a public company. A SVM Kernel models were trained and tested by using past 1 year (i.e. from Nov 2019 to Nov 2020) stock data of both the companies.