We recently wrote about a study from Pace University which showed that the social media popularity of a company can predict its daily stock price. Now economists at the Technical University of Munich (TUM) have launched a website that predicts individual stock trends based on Twitter chatter.
TUM previously conducted a study in which text and sentiment analysis methods were used to analyse tweets related to particular stocks and whether they contained "buy", "sell" or "hold" messages. The study showed that the sentiment from Twitter messages tracks stock market returns for individual stocks and can even predict them a day ahead. If an investor had oriented his share purchases according to the Twitter sentiment in the first half of 2010, he would have achieved an average rate of return of up to 15 percent.
While the Pace University study only tracked the stock prices of 3 companies (Starbucks, Coca Cola and Nike), TUM's study looked at tweets related to any S&P 500 company over 6 months in 2010 as indicated by the presence of a relevant hash tag. and their relationship to stock returns, trading volume and stock price volatility. 250,000 tweets were included.
The information in tweets captured market movements fairly quickly and strong buy signals on Twitter are followed by abnormal returns (higher than expected returns) from that stock the following day. Higher volumes of tweets about a particular stock was also related to higher next day trading volume of that stock.
High volatility, where the stock price varied a lot, led to higher Twitter message volume as people consulted their peers for information. There were also more disagreements between Twitter users on a particular stock (whether to buy, sell or hold) when the trading volume was higher.
The researchers also looked at how investment advice circulated on Twitter and whether users who gave higher quality investment advice gained more followers or retweets. Surprisingly, retweeted information was not found to be higher quality, in terms of predicting returns, than other tweets.
The 1.5 percent of users who were responsible for 50 percent of the tweets analysed also didn't necessarily give better investment advice than average. But a certain subset of users in the high-frequency group made accurate predictions in more than three-quarters of messages and these users were rewarded with higher RT rates and follower increases.
Tags: stock market, Twitter
Companies: Technical University of Munich, Twitter
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Source: http://feeds.nytimes.com/click.phdo?i=7ddf190cdc6bf237a460d680e01e2d12
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