The Chair asked how can one trade the news.

"Big Data Gets Bigger: Now Google Trends Can Predict The Market":

Yesterday three economists, (Tobias Preis of Warwick Business School in the U.K., Helen Susannah Moat of University College London, and H. Eugene Stanley of Boston University) published an eye-opening paper that said Google Trends data was useful in predicting daily price moves in the Dow Jones industrial average, which consists of 30 stocks. Their research result:

An uptick in Google searches on finance terms reliably predicted a fall in stock prices.

"Debt" was the most reliable term for predicting market ups and downs, the researchers found. By going long when "debt" searches dropped and shorting the market when "debt" searches rose, the researchers were able to increase their hypothetical portfolio by 326 percent. (In comparison, a constant buy-and-hold strategy yielded just a 16 percent return.)

This was a 180-degree turnaround from earlier research, by Prof. Preiss published back in 2010.

Back in 2010, he used Google Trends data and found the opposite conclusion:

"The Google data *could not predict the weekly fluctuations in stock prices*. However, the team found a strong correlation between Internet searches for a company's name and its trade volume, the total number of times the stock changed hands over a given week. So, for example, if lots of people were searching for computer manufacturer IBM one week, there would be a lot of trading of IBM stock the following week. But the Google data couldn't predict its price, which is determined by the ratio of shares that are bought and sold."

What happened? Are people revealing more of their investment intentions in their searches?

The clue may be in looking at changes in the nature of what's reported on Google Trends. In a nutshell, the data is getting bigger, by getting finer, and faster.

Also, this is an interesting graph with current downtick in search and uptick in stocks.

Pitt T. Maner III writes: 

More research via Nature which references possibly useful search term analysis:

1) "Quantifying Trading Behavior in Financial Markets Using Google Trends":

'Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as "early warning signs" of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior.'

And a graph using a "Google Trends" Strategy claims to show this:

'Profit and loss for an investment strategy based on the volume of the search term debt, the best performing keyword in our analysis, with Δt = 3 weeks, plotted as a function of time (blue line). This is compared to the "buy and hold" strategy (red line) and the standard deviation of 10,000 simulations using a purely random investment strategy (dashed lines). The Google Trends strategy using the search volume of the term debt would have yielded a profit of 326%.'

2) A reference to Goodhart's Law:

'Furthermore, economists acknowledge that any transparently profitable strategy for playing the markets will quickly lead to a change in trader behaviour that cancels it out — a principle called Goodhart's law, after the British economist Charles Goodhart. "Social systems have the complication that the system may directly react to predictions being made about its behaviour," agrees Susannah Moat, a computational social scientist at University College London and a co-author on the study.'





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