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荷兰推出最新研究成果,利用推特预测股价变动

伊拉斯姆斯大学鹿特丹管理学院的Ting Li和Jan van Dalen分析了超过一百万条涉及标准普尔100指数上市股票的推特,并就此开发了一种算法,用来扫描股票推特,预测股价变动。

具体来说,这种算法观察的时推特的情绪,并提取出嵌入其中的不同的“买入”、“持有”和“卖出”信号,然后将其与未来几天股票的实际价格波动进行比较。

看涨如“买”的股票经历了平均更高的异常收益。同时,有影响力的Twitter用户的相关性更强,这些用户经常被转推,而且经常被提及 - 而某个特定股票的推特数量可以预测交易量,波动性和股票的后续回报。

为了研究该算法是否可以作为盈利交易策略的基础,研究人员利用研究中的信息进行了一个为期21周的模拟,发现即使考虑了交易成本,模拟收益率也超过了市场平均水平。

研究组成员表示,这种算法可以被机构投资者或家庭日间交易者使用,因为他们的研究证明Twitter不仅仅是一种社交噪音 ,人们同样可以从中提取有用信息,帮助自己做出更好的投资决定。

Ting Li和Jan van Dalen远不是第一个将Twitter和股价挂钩的研究人员。早在2010年,研究人员就发现,通过使用两种情绪跟踪工具分析每日Twitter Feed的内容,道琼斯工业平均指数收盘价日内涨跌的预测准确率为87.6%。该算法后来被用于一个2500万英镑对冲基金,虽然实验表现稳定,但实验随后却被迅速关闭。

Researchers from a Dutch university have developed an algorithm that scans tweets about stocks, successfully predicting price fluctuations.

Ting Li and Jan van Dalen from RSM Rotterdam School of Management, Erasmus University analysed over a million tweets that mentioned stocks listed on the S&P 100 share index.

The researchers developed an algorithm that looked at the sentiment of the tweets and extracted distinct ‘buy’, ‘hold’ and ‘sell’ signals embedded in them - before comparing them to actual price fluctuations on the stocks over the following days.

Stocks that saw bullish tweets such as ‘buy!’ experienced, on average, higher abnormal returns. Meanwhile, the correlation is even stronger for influential Twitter users who are frequently retweeted and often mentioned - while the number of tweets about a particular stock could predict trading volumes, volatility and follow-up return on a stock.

To see if the algorithm could be used as a the basis of a profitable trading strategy, the researchers ran a 21 week simulation using the information from the study and found that, even taking transaction costs into account, the simulated returns beat the market.

Says Li: "This could be used by institutional investors or home-based day traders and proves that twitter isn’t just noise - useful information can be extracted and could help investors make better decisions."

Li and van Dalen are far from the first researchers to link Twitter and stock prices. As far back as 2010, researchers found that analysing the content of daily Twitter feeds using two mood tracking tools could predict with an 87.6% accuracy the daily ups and downs in the closing value of the Dow Jones Industrial Average.

The algorithm used became the basis of a £25 million hedge fund, although the experiment was quickly shuttered despite a steady performance.


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