Prediction of National Stock Exchange Market Prices using Supervised Machine Learning Models

  • Sanjay Gandhi Gundabattini , Suresh Babu Kolluru


Share market or Stock market is one of the most sophisticated and complicated businesses. Brokerage firms, smaller ownerships, banking sectors, etc. cling to this cultured body to make profitable revenues and to mitigate the market risks. Analysis and Prediction of stock market prices plays a key role in real life economy. Making predictions on the Stock market is lucrative in finance and business applications due to its unforeseeable raise and fall. This uncertainty attracted the researchers as lots of factors influencing the stock prices on a day to day basis. Our research highlights the application of Machine learning algorithms in building the highly accurate prediction techniques that actually predicts the stock prices in the current market trend. Support vector machine, random forest, decision trees, ensemble method and also a few hybridization methods are used to build prediction model that predicts the current stock market prices for different individual stock exchanges. Challenges confronted in building prediction models are also addressed in the paper.