Stock market prediction: A big data approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

The Stock market process is full of uncertainty and is affected by many factors. Hence the Stock market prediction is one of the important exertions in finance and business. There are two types of analysis possible for prediction, technical and fundamental. In this paper both technical and fundamental analysis are considered. Technical analysis is done using historical data of stock prices by applying machine learning and fundamental analysis is done using social media data by applying sentiment analysis. Social media data has high impact today than ever, it can aide in predicting the trend of the stock market. The method involves collecting news and social media data and extracting sentiments expressed by individual. Then the correlation between the sentiments and the stock values is analyzed. The learned model can then be used to make future predictions about stock values. It can be shown that this method is able to predict the sentiment and the stock performance and its recent news and social data are also closely correlated.

Original languageEnglish
Title of host publicationTENCON 2015 - 2015 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2016-January
ISBN (Electronic)9781479986415
DOIs
Publication statusPublished - 05-01-2016
Event35th IEEE Region 10 Conference, TENCON 2015 - Macau, Macao
Duration: 01-11-201504-11-2015

Conference

Conference35th IEEE Region 10 Conference, TENCON 2015
CountryMacao
CityMacau
Period01-11-1504-11-15

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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    Attigeri, G. V., Manohara Pai, M. M., Pai, R. M., & Nayak, A. (2016). Stock market prediction: A big data approach. In TENCON 2015 - 2015 IEEE Region 10 Conference (Vol. 2016-January). [7373006] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2015.7373006