Nayak, Aparna and Pai, Manohara M.M. and Pai, Radhika M (2016) Prediction Models for Indian Stock Market. Procedia Computer Science, 89. pp. 441-449. ISSN 1877-0509

PDF 1306.pdf - Published Version Restricted to Registered users only Download (695kB) | Request a copy

Abstract

Stock market price data is generated in huge volume and it changes every second. Stock market is a complex and challenging system where people will either gain money or lose their entire life savings. In this work, an attempt is made for prediction of stock market trend. Two models are built one for daily prediction and the other one is for monthly prediction. Supervised machine learning algorithms are used to build the models. As part of the daily prediction model, historical prices are combined with sentiments. Up to 70% of accuracy is observed using supervised machine learning algorithms on daily prediction model. Monthly prediction model tries to evaluate whether there is any similarity between any two months trend. Evaluation proves that trend of one month is least correlated with the trend of another month.

Item Type:	Article
Uncontrolled Keywords:	Boosted Decision Tree; Logistic Regression; Sentiment Analysis; Stock market; Support Vector Machine
Subjects:	Engineering > MIT Manipal > Information and Communication Technology
Depositing User:	MIT Library
Date Deposited:	19 Oct 2016 14:33
Last Modified:	19 Oct 2016 14:33
URI:	http://eprints.manipal.edu/id/eprint/147246

Actions (login required)

View Item