Predicting Stock Movements using Social Network
According to “Wisdom of Crowds” hypothesis, a large crowd can perform better than smaller groups or few individuals. Based on this hypothesis, we investigate the impact of online social media, a group of interacting individual, on financial market in Indi
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Abstract. According to “Wisdom of Crowds” hypothesis, a large crowd can perform better than smaller groups or few individuals. Based on this hypothesis, we investigate the impact of online social media, a group of interacting individual, on financial market in Indian context. The interaction of different users of www.moneycontrol.com, a popular online Indian stock forum, is put to a social graph model and several key parameters are derived from that social graph along with the user’s suggestion such as (Buy, Sell or Hold ) related to a stock. The user’s impact in that forum is then calculated using the social graph of the users. Stock price movement is then predicted using user’s suggestions and their impact in that forum. As per our knowledge, this is the first paper which considers the impact of www.moneycontrol.com user’s suggestions and social relation to predict the stock prices. Keywords: Sentiment analysis price movement
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· Wisdom of crowd · Page rank · Stock
Introduction
Stock market investment decisions are mainly driven by the market information available to investor. In earlier days, the main source of new information was news articles containing information related to a company, such as the company fundamentals, future plans and so on. The stock price of a company was driven by these publications. The rise of internet and finance related websites and applications has changed the scenario as the new information about a company is readily available. Some of the websites are developed as a forum where number of interested users interact with each other and give their opinion on different stocks. A system which can utilize this data and peoples opinion to predict future changes in prices is highly required to support the decision making of investors and traders. There are two popular hypothesis regarding stock price prediction (i) Efficient Market Hypothesis (EMH) [1] and (ii) Random Walk [2]. The EMH [1] states that it is impossible to “beat the market” because stock market efficiency causes existing share prices to always incorporate and reflect all relevant information. The random walk hypothesis states that stock market prices evolve c IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved Y.K. Dwivedi et al. (Eds.): I3E 2016, LNCS 9844, pp. 567–572, 2016. DOI: 10.1007/978-3-319-45234-0 50
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according to a random walk and hence cannot be predicted which is consistent with the EMH. Inspite of both the hypothesis stating stock price can not be predicted, some automated systems, which use the financial news to help in decision making of investors and traders have been proposed [3]. Another research [4] has shown that there is a strong relationship between stock price fluctuations and publications of relevant news. Schumaker and Chen [5] proposed a system to prove the effect of news items on the stock prices using stock’s history data. The other notable works using historical data to predict the stoc
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