Study on Auto Sector Stock Price Trend Prediction by using Decision Tree

Abstract

Many national and international uncertain factors determine the auto sector stock price trend. It includes local as well as global factors. It is challenging to predict the impact of such a factor on the stock price trend as the impact of the same factor varies at different points of time and due to nonlinear nature of the financial stock market. Machine learning method is one of the techniques which capture patterns in the historical data. It is this feature which prompted us to use it for predicting auto sector stock price trend in this research work. It proposes detailed study on auto sector stock price trend prediction. This research specifically highlights decision tree classifier as a method to forecast trend in auto sector price prediction as it handle multidimensional data and doesn’t require any domain knowledge.