DebtRank, Google, and the future of investment research

Subscribers to my weekly newsletter were alerted to an interesting article about a new, novel way to conduct company research.

Many times, as investors, we accept the media’s view or Accepted Wisdom to guide our thinking about investing. But, sometimes, when this view is challenged, we realize that our assumptions were simplistic. Not accurate. Off the mark. Astray.

You get the point.

‘Too Big to Fail’ Analysis = Fail

Well, researchers are concerned about the fragility of our financial services sector (shouldn’t we all be??). We hear incessantly about banks that are TBTF (Too Big To Fail). The measure of a bank’s ‘size’ has become the single, most-salient issue in determining its risk.

Well, did you ask yourself whether ‘size’ is really the best way to describe a bank’s risk? Is a bigger bank MORE likely to bring down others in its wake if it stumbles?

Using an approach right out of Google’s playbook, researchers challenged our own research by looking NOT at how big a bank is, but how interwoven it is into the overall banking sector.

This process mimics the way Google ranks webpages (PageRank) , where quality scoring is done by how many other sites link to a specific page. A page with more inbound links is considered higher quality — that’s how it elicited more links.

In a post last week, Mark Buchanan explained how this new analysis, called DebtRank, works:

Now, what about finance? The analogy for DebtRank is quite direct — those institutions that present the greatest risks to the financial system are those that, if they fail, would cause the widest spread of economic distress. Naturally, you would tend to have a high DebtRank if you are linked by loans and other financial ties to other firms with high DebtRank — the same circularity again.

Co-lateral research: cross-pollinating the investing process

I wrote an entire chapter in Tradestreaming about what I call, co-lateral research. This is a process of using inherently non-financial data/information as inputs to the research process. Frequently this research captures social sentiment, but it doesn’t have to.

Co-lateral research information can come from a variety of sources, like:

As technology continues to develop, large web platforms are petri dishes for this type of research. I’m looking forward to the Amazon.com Strategy — where you go long stocks with products on the ascendance in popularity on the ecommerce site and fade those losing interest with consumers.

How to use Google search data to invest (transcript)

This transcript is of a conversation I had with Dr Joey Engelberg, Professor of Finance at the University of North Carolina’s Kenan-Flagler Business School  (listen to the podcast). You can always subscribe to Tradestreaming Radio on iTunes.

In my book, Tradestreaming and on my website, I talk a lot about what I call collateral research. This is information that’s inherently non-financial in nature, but that investors are using to aid in their investment decisions.

Using Google Search Data to Invest by tradestreaming

One example I talk about in the book specifically is Amazon sales data. You can go onto Amazon.com, look up best selling computers, and you can get a list at that moment in time, updated hourly, of what’s selling well. So, if you were an investor in Apple, and Apple was introducing a new product to the market, that information, although it doesn’t say specific sales numbers, of what Apple itself is seeing through selling on Amazon, that information is at least important in the sense of how well a product may be received into the market.

Another area of concern for investors, of interest, is Google search data. Google recognizes that itself, and launched about two years ago on Google Finance something called Google domestic search trends, GDST. That’s a mouthful. What that is basically is Google itself is looking at a vertical search, something about the auto industry, unemployment, something where there are a series of search terms around a particular category, and then mapping them against the volume of other search queries.

So, you can get a feel for, qualitatively, how a certain search term or industry is trending vis a vis the rest of the search market. You can then overlay that information on top of an ETF or a mutual fund that may track that industry, and you can get a view for how well some of that data may, or may not influence future price movements.

Today’s guest on the podcast is Joey Engelberg, who studied this actually quite intensely. He’s a Professor of Finance at the University of North Carolina, the Kenan-Flagler Business School. He previously worked at the SEC, as a research specialist.

He recently produced a paper that caught my eye, called In Search of Attention. That basically looks at Google search data and tries to map it to future price movements. He actually did find a correlation that certain abnormal trends in search data can lead to abnormal returns in the stock market.

Continue reading “How to use Google search data to invest (transcript)”

How to use Google search data to invest (podcast)

tradestream radio, discussing investing and technology

In my book, Tradestream, I talk a lot about what I call “Co-lateral Research”.  This is information inherently non-financial in its nature that investors can use to make better investment decisions.

Take Amazon Sales Ranking, for example.  Amazon provides almost real-time ranking of its best selling items.  While Amazon won’t reveal exactly how many units of Apple’s ($AAPL) iPad it’s selling, investors can get a qualitative feel for how well products are moving.

Summary

UNC Professor Joey Engelberg has been studying another form of co-lateral research, Google search data.  He’s been studying search trends for stocks (ie $PCLN or $NFLX) as a way to measure investor attention.  Prof Engelberg has found a linkage between changes in search volume and subsequent moves in stock prices.  He joins us for this installment of Tradestreaming Radio.

We discuss

  • which particular stocks investors pay attention do during the trading day
  • the demand side of news and information for stocks
  • how Google search volume is correlated to stock pricing
  • a trading strategy that uses search volume to beat the market

Listen below

Resources:

 

When searching for stock gains, use Google (search data)

A wealth of information creates a poverty of attention

Smart investors avail themselves of all valuable resources as inputs into the investment research process.  I write about this faculty in my book Tradestream in the chapter “Co-lateral Research“.  What co-lateral research means is all the non-financial/non-traditional sources of information that can be used by investors to connect-the-dots.

I’ve written about Google Domestic Trends, search volume data Google has made public and overlayed on top of stock index charts.  GDT continues to be a good resource for investors.

And now, there’s more research to support using Google search data to auger where markets are headed.

In In Search of Attention, researchers found that Google’s Search Volume Index captures retail investors’ attention in stocks.

Among our sample of Russell 3000 stocks, stocks that experienced an increase in ASVI [me: abnormal search volume index reading] this week are associated with an outperformance of more than30 basis points (bps) on a characteristic-adjusted basis during the subsequent two weeks. This initial positive price pressure is almost completely reversed by the end of the year.

The paper also finds that increased search volume leading up to hot IPOs may be responsible for that big first-day pop! that such issues experience.

As the first paper that has really looked at search data from an investing standpoint, this should be piped and smoked.  In fact, the authors conclude the paper with a somewhat foretelling statement:

Search volume is an objective way to reveal and quantify the interests of investors and therefore should have many other potential applications in fi…nance. We leave those for future research.

Bring it on.

Source

In search for attention (Da, Engelberg, Gao), November, 2010

HT: Net//Worth