Blackbox systems - Caveat Emptor

Cross ref http://awanginvest.com/?p=690

August 26, 2008 – 4:57 pm After last week’s seminar at SMU, I received an enquiry about automated systems .

I thought it may be good to share my findings as follows:

Automated systems can be dangerous ; otherwise, we would all be successful without using our brains to an extent that indeed any blackbox system is the Holy Grail.

Quote:

The computerization of trading and investment is a logical and noble pursuit. However, attempts such as these are not without their pitfalls for a variety of reasons. Statistical arbitrage strategies have become progressively less and less attractive as more capital has flowed into the area. Where a super-smart quantitative manager could once design a high frequency, quasi-market making strategy that was both very profitable, required little capital and entailed a small degree of market risk, they now need to extend signal horizons and seek to generate returns by doing what everybody else does when they reach for return - take on more risk and accept greater variation in returns. Further, these high frequency strategies are often not very scalable, a real hinderance for a manager that wants to grow and leverage their brand into a multi-billion dollar operation. Returns in the various statistical arbitrage strategies display asymptotic profiles, where early alpha generation is eventually squeezed to zero as more brains and assets focus on the strategy in question. Managers innovate, enjoy attractive returns for a period after which they need to move on and develop the next set of algorithms. It may appear to a layperson that a black box trading strategy would be great - few PMs with huge egos, relatively modest investment in programmers and hardware to build a scalable platform and a nimble, easy-to-adjust model to adapt to changing market conditions. This, my friends, is simply not the case.

Consider the two black box managers with the most successful long-term records and asset growth - the aforementioned DE Shaw and Renaissance. They both have armies of PhD.s of all stripes - computer scientists, mathematicians, physicists, biologists, chemists, linguists, etc. This is not exactly the cheap and scalable infrastructure many have in mind. It takes a lot of money, relentless and effective recruiting and a culture to support the degree of innovation required to succeed. I think about it as the “cycle of the 4 M’s:”

  1. Man, who develops the
  2. Model, which is operated by the
  3. Machine, which executes the Model in the
  4. Market, which generates returns, results in feedback interpreted by Man, who modifies the Model, etc.

It is usually not the cute, campy story of a smart technician with his trusty computer building a successful and scalable hedge fund. Few have done it well, and it remains to be seen whether Ray Kurzweil and his lot will be able to make it into the pantheon of black box gurus like David and Jim. Do these new entrants have brains? Yes, and often in spades. But success ultimately requires A LOT more than brains, like:

  1. Managerial skill
  2. Risk management skill
  3. Recruiting skill, and
  4. Business-building skill, to name a few. Unquote

Ultimately, we must be realistic that there is no short-cut to success.

Using an automated system may be fine until there is a 9/11 event; also we do not know exactly what factors were being used by the system to give us confidence when changes occur.

ANA aka IDKIT

Ag Moderator

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