This past week I had the opportunity to see MIT’s Professor Andrew Lo present his paper “What Happened to the Quants In August 2007?” as part of the seminar series on quantitative finance presented by NYU and Columbia and sponsored by BlackRock and other relevant institutions. If you’re in the NYC area and interested in such things, I recommend attending any lectures which might capture your fancy.
I had read his paper some time back and implemented, within the Puppetmaster environment, the mean-reversion trading strategy he used as a microscope into what transpired last August. I was interested to see him speak as he’s a seminal thinker on hedge funds and quantitative finance, but also because the strategy he described works pretty well and I thought he might hint at various improvements.
I’ve stolen a line from his paper to serve as the title of this post as it captures one of the central dilemmas faced by algorithmic traders.
The quote is:
In the face of the large losses of August 7-8, most of the affected funds – which includes market-neutral, long/short equity, 130/30, and certain long-only funds – would likely have cut their risk prior to Thursday’s open by reducing their exposures or “de-leveraging”, either voluntarily or because they exceeded borrowing and risk limits set by their prime brokers and other creditors. This was both prudent and, unfortunately, disastrous.
My business partner likes to speak of the art and science of algorithmic trading and, while it might make me cringe as a bit touchy-feely, this is a perfect example of where she’s precisely right. In the case Professor Lo describes, these funds may have been compelled to unwind their strategies as they were excessively levered. (Ironically, the funds that were able to hold fast were almost immediately rewarded with record gains which nearly offset the record losses they’d incurred.) But there’s a general problem here for quantitative strategies, namely, what to do when a profitable strategy incurs unusual losses?
While there’s considerable science which can be applied to this question, at the end of the day the decisions one makes or, better – the policies one establishes – rely as much on the “art” the practitioner applies as any quantitative measure which can be objectively administered. Since a significant advantage of programmatic execution is that it takes such discretionary decision-making out of the trading equation, this is an issue of some consequence. While there’s no magical formula that I know of, there are at least two reasonable approaches to the question.
The first is to have a coherent understanding as to why your strategy is profitable, so that when it seems to stop working you can develop and test explanatory hypotheses with an eye towards developing a well-reasoned approach to the issue. Of course, if your strategy is the result of some sort of data-mining effort, than this can be difficult or impossible – yet another argument against a purely data-mining approach to strategy development! In the example Dr. Lo provides, an astute practitioner might be able to deduce that similar strategies had over-committed and been forced to liquidate; patience might be maintained while the world reverted to a (hopefully!) profitable state of equilibrium. While this approach may be workable in some cases, it seems to ask a lot from the practitioner as a day-to-day policy and might cause more problems than it resolves in the heat of the moment.
The second and probably more workable approach is to structurally minimize the problem by treating strategies like financial instruments in their own right and then apply traditional portfolio management techniques to their allocation. With this approach, we might only allow some fraction of our (levered) portfolio to be managed by any particular algorithm and partner it with other strategies which exhibit negative or complementary correlation characteristics. While it’s well-understood that in periods of serious financial dislocation “correlations go to 1″ and thus this approach won’t cure all ills, it provides a sound decision-making foundation for addressing the issue. Dynamic portfolio re-balancing schemes can thus be used to address these cases perhaps in concert with an effort to understand the root causes of the under-performance.
It’s certainly possible that even a sophisticated and automated application of portfolio management to this problem would yield similarly prudent yet disastrous results, but this seems to me to provide the best framework for reasoning about such issues and implementing appropriate preventive policies.
