International Journal of Advanced Innovative Technology in Engineering (IJAITE)

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Stock Market Prediction Using Machine Learning Approach: A Review

Abhishek Rajput, Prof. Mahip Bartere

Abstract :

Now a day’s, the prediction of stock market prices and conditions has become a major researched topic amongst data scientists, investment bankers, and stockbrokers. As the stock market behavior is very non-linear and volatile in nature, it makes a very high-risk investment. Consequently, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement. Researchers have used various computer science and economics methods in their quests to gain a piece of this volatile information and make a great fortune out of the stock market investment. Data mining and machine learning approaches can incorporate into Business Intelligence (BI) systems to help users for decision support in many real-life applications. This paper presents a brief survey of the application of machine learning in stock market prediction and investigates various techniques for the stock market prediction using Artificial Neural Network (ANN) and Support Vector Machine (SVM). ANN is a non-linear and non-parametric classifier that is viable for forecasting stock prices. SVM focuses on marginal values rather than average values for the classification prediction model. This paper aims to provide a review of the application of machine learning in stock market prediction to determine what can be done in the future

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