Borasky Research Journal

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In all the technology news last week, you might have missed this story. I only saw it mentioned on Reuters, not on any of the major technology blogs that I read. As is my usual practice when I see a technology story that matches my interests, I try to locate the original sources and post links on Twitter. So in case you missed those, here they are:

LinkedIn shares were a bubble: academic model | Reuters http://meb.tw/iNiM8R

2011/06/01 20:55 via bitlyReplyRetweetFavorite

@znmeb

M. Edward Borasky

Is There a Bubble in LinkedIn's Stock Price?http://meb.tw/loYBD3 [pdf]

2011/06/02 10:27 via bitlyReplyRetweetFavorite

@znmeb

M. Edward Borasky

There’s a fair amount of technical detail about the model in the paper cited in my second tweet. If you want even more, the model itself is documented here:

How to Detect an Asset Bubble by Robert Jarrow, Younes Kchia, Philip Protter :: SSRN http://meb.tw/iqvwUQ

So what’s the story here? From “Is There a Bubble in LinkedIn’s Stock Price?”:

It has been well documented in the financial press that a methodology is needed that can identify an asset price bubble in real time. William Dudley, the President of the New York Federal Reserve, in an interview with Planet Money [3] stated “…what I am proposing is that we try to identify bubbles in real time, try to develop tools to address those bubbles, try to use those tools when appropriate to limit the size of those bubbles and, therefore, try to limit the damage when those bubbles burst.”

It is also widely recognized that this is not an easy task. Indeed, in 2009 the Federal Reserve Chairman Ben Bernanke said in Congressional Testimony [1] “It is extraordinarily difficult in real time to know if an asset price is appropriate or not”.

Here’s a link to the William Dudley interview, and one to Bernanke’s testimony.

Professor Jarrow and his colleagues took up the challenge laid down by the Federal Reserve Board. The model they have devised is quite complex, involving stochastic differential equations and reproducing kernel Hilbert spaces. They tested this model on stock price data from “the alleged internet dotcom bubble (and beyond), from 1999 to 2005.” While there will no doubt be much more peer review of the data, model and conclusions, the test shows promise. Moreover, it can be applied to the price of any publicly-traded stock. The test has three possible results:

1. There’s definitely a bubble.
2. There’s definitely not a bubble.
3. No conclusion about a bubble can be drawn from the data.

So now we come to LinkedIn. LinkedIn was publicly traded for the first time on May 19, 2011, using the symbol LNKD. Professor Jarrow and his colleagues obtained real-time price data from Bloomberg for the first four days of trading and applied their model. And their claim is quite definitive:

We have found, definitively, that there is a price bubble!

While the technology is certainly interesting in its own right, at least to data journalists like myself, what are the wider implications of this? First of all, the context of the Dudley interview was the Finance / Insurance / Real Estate (FIRE) sector and the holdings of the Federal Reserve Board in that industry. As we all know, the Great Recession we discuss on a daily basis originated in the FIRE sector.

The context of the model Jarrow, et. al., have created, on the other hand, is publicly-traded stocks. In particular, the model was initially tested on Internet stocks during a well-documented bubble, and applied to a social media stock within days of its initial public offering. Moreover, the model should work in real time. Given a live data feed and enough computing capacity, it should be possible to monitor data and make investment decisions in real time.

Even though the model is designed for real-time publicly-traded stocks, it should be applicable to any financial time series that satisfies the underlying mathematical assumptions. This includes, for example, prices of shares in the “secondary markets” for companies like Facebook and Twitter. I haven’t attempted to implement the model yet – I’ve been away from computational finance for several years and I’m in the process of coming back up to speed on the methodologies. The core technologies are available in the Data Journalism Developer Studio, however, and if anyone is interested in working on this, send me a tweet @znmeb.