Readers might be interested in an article that A. Duran and I have coming out in Quantitative Finance this year entitled A Profitable Trading and Risk Management Strategy Despite Transaction Cost. In the article, we develop a strategy which outperforms the “market” in rigorous out-of-sample testing. We’ve made sure to check the robustness of the results by performing Monte Carlo simulations on both the S&P 500 and Russell 2000 while varying the subsets of stocks and time periods used in the simulation.
The strategy is interesting in that it is based on behavioral patterns. Unlike many other algorithmic trading models, our strategy is modeled after a human trader with quarterly memory who categorizes the market return distribution and market risk into low, medium, and high categories. Technically, it accomplishes this by non-parametrically categorizing windowed estimates of the first four moments of the return distribution and the normalized leading eigenvalue of the windowed correlation matrix. Based on the assessment of these low/medium/high categories and past experience in similar states, the strategy then decides whether to invest in the market index, invest in the risk-free asset, or short the market. The strategy soundly outperforms the market index in multiple markets over random windows and on random subsets of stocks.
While you’re waiting for its publication in Quantitative Finance, you might check out a copy over at SSRN. Here’s the abstract and a figure below comparing the log-return of our strategy with the market over one realization:
We present a new profitable trading and risk management strategy with transaction cost for an adaptive equally weighted portfolio. Moreover, we implement a rule-based expert system for the daily financial decision making process by using the power of spectral analysis. We use several key components such as principal component analysis, partitioning, memory in stock markets, percentile for relative standing, the first four normalized central moments, learning algorithm, switching among several investments positions consisting of short stock market, long stock market and money market with real risk-free rates. We find that it is possible to beat the proxy for equity market without short selling for S&P 500-listed 168 stocks during the 1998-2008 period and Russell 2000-listed 213 stocks during the 1995-2007 period. Our Monte Carlo simulation over both the various set of stocks and the interval of time confirms our findings.
Another in the series of talks at TED 2010 …. From the description … “Gary Flake demos Pivot, a new way to browse and arrange massive amounts of images and data online. Built on breakthrough Seadragon technology, it enables spectacular zooms in and out of web databases, and the discovery of patterns and links invisible in standard web browsing.”
“Blaise Aguera y Arcas is an architect at Microsoft Live Labs, architect of Seadragon, and the co-creator of Photosynth, a monumental piece of software capable of assembling static photos into an interactive three-dimensional space. This seamless patchwork of images can be viewed via multiple angles and magnifications, allowing us to look around corners or “fly” in for a (much) closer look ….”
This is a very important paper by Jonathan Chang and David Blei. Suffice to say, it has potential use in a wide class of social science applications. Click here to access related material on Professor Blei’s Princeton homepage. Click here for some slides (note 7.0 mb). Check it out!
Download the paper: Collins, Christopher; Viégas, Fernanda B.; Wattenberg, Martin. Parallel Tag Clouds to Explore Faceted Text CorporaTo appear in Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST), October, 2009. [Note: The Paper is 24.5 MB]
Here is the abstract: Do court cases differ from place to place? What kind of picture do we get by looking at a country’s collection of law cases? We introduce Parallel Tag Clouds: a new way to visualize differences amongst facets of very large metadata-rich text corpora. We have pointed Parallel Tag Clouds at a collection of over 600,000 US Circuit Court decisions spanning a period of 50 years and have discovered regional as well as linguistic differences between courts. The visualization technique combines graphical elements from parallel coordinates and traditional tag clouds to provide rich overviews of a document collection while acting as an entry point for exploration of individual texts. We augment basic parallel tag clouds with a details-in-context display and an option to visualize changes over a second facet of the data, such as time. We also address text mining challenges such as selecting the best words to visualize, and how to do so in reasonable time periods to maintain interactivity.
From the Abstract … “The race is on to build a computer that exploits quantum mechanics. Such a machine could solve problems in physics, mathematics and cryptography that were once thought intractable, revolutionizing information technology and illuminating the foundations of physics. But when?” (Subscription may be required for Access)
From the Abstract … ”Scientific publications have at least two goals: (i) to announce a result and (ii) to convince readers that the result is correct. Mathematics papers are expected to contain a proof complete enough to allow knowledgeable readers to fill in any details. Papers in experimental science should describe the results and provide a clear enough protocol to allow successful repetition and extension.” (Institutional or Individual Subscription Required).
During our break from blogging, Ian Ayers offered a very interesting post over a Freakonomics entitled “Prediction Markets vs. Super Crunching: Which Can Better Predict How Justice Kennedy Will Vote?” In general terms, the post compares the well known statistical model offered by Martin-Quinn to the new Supreme Court Fantasy Leaguecreated by Josh Blackman. We were particularly interested in a sentence located at end of the post … “[T]he fantasy league predictions would probably be more accurate if market participants had to actually put their money behind their predictions (as with intrade.com).” This point is well taken. Extending the idea of having some “skin in the game,” we wondered what sort of intellectual returns could be generated for the field of quantitative Supreme Court prediction by some sort of Netflix style SCOTUS challenge.
The Martin-Quinn model has significantly advanced the field of quantitative analysis of the United States Supreme Court. However, despite all of the benefits the model has offered, it is unlikely to be the last word on the question. While only time will tell, an improved prediction algorithm might very well be generated through the application of ideas in machine learning and via incorporation of additional components such as text, citations, etc.
With significant financial sum at stake … even far less than the real Netflix challenge … it is certainly possible that a non-trivial mprovement could be generated. In a discussion among a few of us here at the Michigan CSCS lab,we generated the following non-exhaustive set of possible ground rules for a Netflix Style SCOTUS challenge:
To be unseated, the winning team should be required to make a non-trivial improvement upon the out-of-sample historical success of the Martin-Quinn Model.
To prevent overfitting, the authors of this non-trivial improvement should be required to best the existing model for some prospective period.
All of those who submit agree to publish their code in a standard programming language (C, Java, Python, etc.) with reasonable commenting / documentation.
