Fear, Greed and Efficient Market- Evidence from News Sentiment Analytics

40 Pages Posted: 1 Mar 2016

Date Written: May 6, 2015

Abstract

I analyze the relationship between natural gas prices and the news sentiment data obtained from the Thomson Reuters News Analytics system. I conduct studies on two different horizons: the daily basis study and the intraday basis study. For the daily study, our results show that any correlation we observed between the daily news sentiments and the daily returns of natural gas can be largely attributed to the news relevant to historical natural gas price movements, or “price-related news”. This discovery also emphasizes the failure of price-related news sentiment in predicting future natural gas returns, which in turn supports the weak form of the Efficient Market Hypothesis. For the intraday study, I label news items with high positive and negative sentiment scores as extreme positive news and extreme negative news. I then conduct event study to analyze the price-related extreme new items’ impact on natural gas prices. I found natural gas has two different types of price movement in reaction to price-related extreme negative news. I also show that these two types of abnormal return can serve as a signal for the investors’ sentiment. I made a connection between these investors’ sentiment signals and the S&P 500 index to show that these signals can be used to predict long term S&P 500 returns. The capability of these signals to predict the future S&P 500 index and the fact that the signals are derived solely from past price reactions and past price-related news items suggests that the current natural gas prices and equity market prices do not fully reflect all the information related to historical prices.

Keywords: Natural Gas, News Sentiment, Event Study

JEL Classification: G14

Suggested Citation

Zhang, Tongli, Fear, Greed and Efficient Market- Evidence from News Sentiment Analytics (May 6, 2015). Available at SSRN: https://ssrn.com/abstract=2603381 or http://dx.doi.org/10.2139/ssrn.2603381

Tongli Zhang (Contact Author)

Johns Hopkins University ( email )

Baltimore, MD 20036-1984
United States

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