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Data Journalism Developer Studio 2012LX

Five Best Business Books Of 2011

 Books, Business Intelligence, Data Journalism, Empirical Finance, Social Media Analytics, Trading  Comments Off
Dec 272011
 

Data Journalism Developer Studio 2012LX Blog

I was on the webinar that introduced this book, along with thousands of others. Over 30,000 registered, and the count of people who attended was 10,899. If you had a Kindle, you could download this book for free. I did. In fact, if you’re an Amazon Prime member, you can still get the Kindle edition for free. Dan Zarrella has been collecting Twitter and Facebook data for some time now, and this book is the result of a careful study of what works, what doesn’t work, and what sometimes works. It’s an easy read and very useful.

Pulse is along the lines of the previous book, but is much more detailed, and talks about more data sources. Moreover, it’s research-oriented – how to do the sort of thing Dan Zarrella did to define his hierarchy of contagiousness.

Pulse covers Google search trends, Facebook connections, blogs and Twitter in some detail. THe themes are “what we surf, who we friend and what we say.” Chapter 8 describes three “potential pulses” – “where we go, what we buy and how we play”. You should think of this book as an overview – you’ll need to dig deeper if you want to implement any of the research described.

One of the recurring themes in the past two years has been the so-called “lean startup”. I have some skepticism about the concept, particularly the way it’s been described in blogs. So it’s refreshing to see a book like Venture Deals come out that’s full of actual meat, not just admonitions to “fail faster.” The authors were here in Portland a few weeks ago for a well-attended lecture on the contents, and I have a signed copy. If you’re starting a business, you need to read this book first.

I’ve covered this book at some length here, so I’ll defer you to the previous review. While it’s very advanced technically, it’s the best book published this year on trading technology. Most of the year’s other trading books are rehashes of decades-old technical analysis methods that may or may not work any more. If you want to be a trader, I highly recommend getting this book.

We heard a lot about economic inequality this year, and we’ll hear a lot more in 2012. Whether it’s Occupy Wall Street, calls for higher taxes on the wealthy by Warren Buffett, or President Obama’s speech in Osawatomie, Kansas, economic inequality and how to reverse it have become a topic of interest.

Changing Inequality is an easy-to-read book on the subject. It traces the causes of economic inequality over the past three decades, and suggests a few possible ways to reverse the trend. It should be noted, however, that reversing a 30-year trend of rising inequality isn’t easy. For example, as noted in the book,

In the late 1980s, when it first became clear that rapid increases in inequality were more than a short-term or cyclical phenomenon, researchers began to look for causes. It was almost a decade before widespread consensus was reached among economists that these changes were largely driven by skill-biased increases in demand, many of them probably the result of technological changes linked to a growing use of computer technologies.

The challenge for policy-makers is to devise policies that promote both growth and equality. Changing Inequality is a good place to start.

 Posted by Ed Borasky at 15:42

Three Must-Have Trading Books

 Books, Finance, Time Series Analysis, Trading  Comments Off
Dec 112011
 

Data Journalism Developer Studio 2012LX Blog

The world of computational finance has changed dramatically since I first got interested in the underlying mathematics in 1982. We’ve seen events like the stock market crashes in 1987 and 1989, the failure of Long Term Capital Management in 1998, and more recently, the collapse of Lehman Brothers in September 2008 and the “Flash Crash” in May of 2010.

I’ve spent a fair amount of time over the past year catching up on the theory and practice of algorithmic trading. The following three books are the best I’ve found on the subject. Having made my way through them, I consider traditional technical analysis at best useless and at worst downright suicidal. They are expensive; if you can only afford one of them, I’d recommend the second, Asset Price Dynamics, Volatility, and Prediction by Stephen J. Taylor.

 

Financial Markets and Trading is the newest of these books, and is also the most expensive. It’s designed as a textbook at the undergraduate / graduate level and is fairly self-contained. Schmidt does cover a lot of ground, however, and for implementation details you’ll probably need to search out the original papers on the Internet.

What makes this book unique is

  • An extended section on high-frequency trading, including an overview of the May 2010 “Flash Crash”, and
  • A comprehensive chapter on testing technical trading rules.

These testing techniques go well beyond the traditional backtesting / optimization techniques that are well-known among traders. As this book and its references show, technical analysis sometimes works and sometimes it doesn’t. You’ll need these algorithms to know the difference.

 

 

As I noted above, if you can only afford one of these books, this is the one to get. Unique features include

  • Spreadsheet formulas for many of the algorithms,
  • Algorithms for extracting information from high-frequency data
  • Implied return density calculations from options prices

There are also some algorithms for testing technical trading rules, but I think Schmidt’s treatment of the subject is far more comprehensive.

 

This is the oldest book of the three, and probably the most theoretical. However, it provides much more detail on market microstructure models than the other two, and it includes a chapter on order execution timing strategies.

 Posted by Ed Borasky at 16:11

Yes, There’s A Bubble In LinkedIn Stock!

 Computational Econometics, Computational Journalism, Computer-Assisted Reporting, Data Journalism, Data-Driven Journalism, Empirical Finance, Facebook, Finance, FIRE, Insurance, Journalism, LinkedIn, Project Kipling, Real Estate, Real-Time Journalism, Time Series Analysis, Trading, Twitter  Comments Off
Jun 092011
 

About Data Journalism Developer Studio

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.

 Posted by Ed Borasky at 12:41
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