Web Media and Stock Markets: A Survey and Future Directions From A Big Data Perspective

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Aim:

Analyze the financial data for making prediction in stock markets by using big data analytics and recommend the user investment in various category stock markets based on web media.

Existing System:

IN traditional finance, the efficient market hypothesis states that a stock price is always driven by “unemotional” investors to equal the firm’s rational present value of expected future cash flows. Specifically, stock investors are constantly adjusting their beliefs on the potential market performances of stocks, although they typically disagree on the matter. This disagreement among competing market participants leads to discrepancies between the actual price and the intrinsic value, causing a stock price to fluctuate around a stock’s intrinsic value, i.e., new information has intricate influences on asset prices. Although traditional finance and modern behavioral finance have different views on how information shapes stock movements, both believe that the volatility of the stock market comes from the release, dissemination and absorption of information.

Proposed System:

The research on media-aware stock movements began with financial reports and news articles. With the popularity of Web 2.0, new media sources, such as blogs, tweets/micro blogs, discussion boards, and social news, have emerged and played important roles in affecting stock markets. As a pilot study, found that the emotions of tweets affected stock trends for a brief period after the release of the tweets. In contrast to traditional news, social media allows users to express their opinions and feelings via comments, votes and so forth. Such user engagement efficiently enhances information dissemination and increases the value of the information. We are using Google API to connect to the internet and get the news articles to analyze the stock trends and recommended the correct stocks to the users.