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If you're looking for a contrarian, unique data set that has been an accurate prediction of corrections in the market, a recent study called "Quantifying Trading Behavior in Financial Markets Using Google Trends" by Tobias Preis, Helen Susannah Moat and H. Eugene Stanley seeks to use Google Trends data to see how we investors interact with the internet. I encourage you to read this study, as I believe that it presents a very intriguing new data point for investors. If you want to get the gist of the study, here is the most important part for investors or traders:

It is widely recognized that investors prefer to trade on their domestic market, suggesting that search data for U.S. users only, as used in analyses so far, should better capture the information gathering behavior of U.S. stock market participants than data for Google users worldwide. Indeed, we find that strategies based on global search volume data are less successful than strategies based on U.S. search volume data in anticipating movements of the U.S. market (<R>US = 0.60, <R>Global = 0.43; t = 2.69, df = 97, p < 0.01, two-sided paired t-test).

Our empirical results so far are consistent with a two part hypothesis: namely that key increases in the price of the DJIA were preceded by a decrease in search volume for certain financially related terms, and conversely, that key decreases in the price of the DJIA were preceded by an increase in search volume for certain financially related terms. However, our trading strategy can be decomposed into two strategy components: one in which a decrease in search volume prompts us to buy (or take a long position) and one in which an increase in search volume prompts us to sell (or take a short position).

While they looked at the Dow Jones (DJIA) index, I believe that we could apply this to individual stocks. Perhaps the most recent example where a contrarian mindset could have saved one money was last October when Apple (AAPL) was trading near $700. At that time everyone was bullish on Apple, but as we all know, it soon crashed and is now trading at $425.

(click to enlarge)

This is Apple's chart from the beginning of 2012 to the present.

AAPL Chart

AAPL data by YCharts

Here is the Google Trends data from 2012 for the Google search "AAPL."

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100 is the peak. The peak number of searches for "AAPL" was the week of October 21-27, and while Apple had already sold off around 100 points, if you sold then you would have a 200 point sell-off. Then, in early January when the term "AAPL" was searched for the most, the stock slid from $500 down to $450 and continued to go lower.

To see is this works with a different stock, I will look at Amazon (AMZN) data. Here is Amazon's chart from the start of 2012 to right now.

AMZN Chart

AMZN data by YCharts

Now here is the Google Trends data for the search "AMZN."

(click to enlarge)As you can see, the bumps in Google search interest were around the same time as sell-offs in the stock, and just when the Google search interest hit a yearly low, Amazon rallied into the end of the year. Then, when the sentiment reached a high in early January, the stock began to sell off. More importantly, the search interest is still low, suggesting that Amazon could continue to march higher even after the recent rally.

What It's Saying Now

The question that many investors have been asking themselves is,, "Can this market go any higher?" Using the Dow Jones ETF (DIA) as a proxy for the broader market, here is the Google trends data for the search "Dow Jones."

(click to enlarge)

We all know that the market sold off earlier this summer, and the fact the Google searches are low would seem to indicate that the market could continue the small rebound we have had. There are many other indicators to look at, but Google Trends is simply another tool we can use to judge sentiment. Not only as indicator of sentiment, but a contrary one at that, which makes it an even more valuable tool.

Source: Using Google Trends To Gauge Sentiment: A Case Study In Apple And Amazon