Year: 2016
Type: conference-paper
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.
Source: Procedia Computer Science
Institution: Manipal Academy of Higher Education
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W2518345121