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Permabear Sentiment Index

by Tom Huggens, 2881 days ago
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Yesterday, I have created a download item that creates an index called the Roubini Sentiment Indicator. The same guys who created this indicator also created another one called the Permabear sentiment index. Permabear refers to people who are always pessimistic and negative about the future direction of the economy and markets. The Permabear sentiment index is built using aggregate keyword volume data from Google Trends. The index is the result of the sum of four Google Trends popularity indices; the keywords used to create these indices are: Roubini, Peter Schiff, Marc Faber and Nassim Taleb.

The item will login in your Google account, load Google trends and create a Google Trends popularity index for each permabear. It will then download the data, parse it, calculate the sum of each permabear volume data and then save the result in ticker symbol: ^GOOGLE_TRENDS_PERMABEAR. As with the Roubini Sentiment Indicator, you must set the email and password fields of this download item to your Google email and password (Select the item, click on Update, click on the "2 Field(s)" button).

The author of the Permabear Sentiment Index claims that the Permabear Sentiment Index can make 3.5% over a short period of 3 weeks compared to the 1.6% yield of the Roubini Sentiment Indicator over the same period. The author gives one reason behind the good performance of this index. He says that each time these permabears are pessimists, they affect medias' view of the market which in turn affect investors' view and this leads to a decline in the stock market.
To read more about the Permabear Sentiment Index, go to: http://www.insidermonkey.com/blog/2010/09/27/make-3-5-percent-in-3-weeks-by-shorting-spy-with-the-permabear-sentiment-index/

Using keyword volume data, we can say that the Nouriel Roubini and Peter Schiff have the same popularity, followed by Marc Faber then Nassim Taleb.




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Type: Download Script

Object ID: 726


Country:
United States

Market: Stock Market

Style:
Fundamental Analysis

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