How to Utilize Big Data for Business Intelligence in the Stock Market

@article{Bajunaid2017HowTU,
  title={How to Utilize Big Data for Business Intelligence in the Stock Market},
  author={Walaa A. Bajunaid and Maram Meccawy},
  journal={International Journal of Computer Applications},
  year={2017},
  volume={166},
  pages={13-16}
}
Big Data plays a vital role in the stock market, especially for traders who need real-time information. Due to the bulk and nature of such data, several data mining technologies have been developed and employed in their collection, classification, storage and analysis, putting them in a form that is useful to traders. In the stock market, big data is useful in fundamental and technical analysis as it captures both historical trends as well as market sentiment. This paper discusses the possible… 

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References

SHOWING 1-10 OF 16 REFERENCES

Financial Volatility Forecasting by Least Square Support Vector Machine Based on GARCH, EGARCH and GJR Models: Evidence from ASEAN Stock Markets

Using hybrid models, LSSVM provides improved performances in forecasting the leverage effect volatilities, especially during the recently global financial market crashes in 2008.

Big Data Analytics the Next Big Learning Opportunity

How undergraduate business schools may help students in higher education gain the big data and data analytics skills and experience necessary to fill the current employment gap of trained professionals in the field is focused on.

A Survey on Web based Traffic Sentiment Analysis

This survey will try to focus on sentiment analysis approaches, related work for automated web data crawling, different levels of SA, subjectivity classification, some machine learning techniques on the basis of their usage and importance for the analysis, evaluation of Sentiment classifications and its recent advancements and the future research directions in the field of traffic Sentiment Analysis.

Mining and summarizing customer reviews

This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.

Automatic Sentiment Analysis in On-line Text

An overview of various techniques used to tackle the problems in the domain of sentiment analysis are given, and some of their own results are added.

Sentiment analysis: A combined approach

Thumbs up? Sentiment Classification using Machine Learning Techniques

This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.

From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series

This work connects measures of public opinion measured from polls with sentiment measured from text, and finds that temporal smoothing is a critically important issue to support a suc- cessful model.

Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis

A new approach to phrase-level sentiment analysis is presented that first determines whether an expression is neutral or polar and then disambiguates the polarity of the polar expressions.

Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews

A simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (Thumbs down) if the average semantic orientation of its phrases is positive.