While doing some research on the quality and volume of executions at the open and close of US equity markets, I came across two topical research reports by Celent, a finance consultancy. The first report is a detailed look at execution quality on nasdaq issues while the second addresses the same topic for the nyse. An abstract of the first report can be found here and of the second here. Both are interesting enough on their own, though I’ve yet to acquire the full reports.
Read more…
I mentioned several weeks ago that I’ve been developing and trading a strategy that’s proven to be quite interesting and profitable. In that post, I described how I’d tried to improve the strategy through the use of a dynamic hedge. The results of that crude hedge were quite good, but just as no worthwhile software project is ever really complete, trading strategies demand constant iterative development.
Below I describe some of the steps I’ve taken to incrementally improve this strategy, discarding the relatively expensive hedge I’d developed earlier in favor of a complementary strategy. We see that when you combine two positive and uncorrelated results, you end up with a product that is literally better than the sum of its parts.
Read more…
People have long imagined ways to make money while they slept. Happily, it’s not a pursuit I’m particularly bothered by, but as I develop trading strategies, I do make note of different market behaviors that correlate to the time of day. Or night.
In particular, I’ve been looking at various market-breadth ETFs recently as possible fodder for the little dynamic hedger I’ve described before, and I’ve noticed an interesting behavior among several of them…
Like the majority of traders, they do better when they’re not trading!
That is, they actually display better performance at night than they do during the regular trading day; there’s more profit to be had in their gaps between sessions than there is during trading sessions. Below I quantify this observation more thoroughly…
Read more…
The always excellent Wilmott Magazine has recently posted a series of articles by Ed Thorp (pictured) in which he describes his experiences developing and evolving a statistical arbitrage product. Part I provides some insights into his current operation, revealing that he maintains a dollar-neutral portfolio as I’d discussed in another post, they trade some 1.5 billion shares / year, and that they limit position sizes to 2.5% on the long side of the portfolio and 1.5% on the short side. In Part II, he explains why a stat arb system is considered an “arbitrage” and how, with the help of a talented team and led by the insights of Gerry Bamberger, they developed the first iteration of a stat arb product. Part III details the evolution of the system from a set of dollar-neutral sector-oriented portfolios to the more general sets of portfolios generated through statistical factor analysis. He concludes with some anecdotes including the emergence of David E Shaw. Very recommended.
I came across and had to share this excellent vignette by Freeman Dyson on the perils of excess model parameterization…
In desperation I asked Fermi whether he was not impressed by the agreement between our calculated numbers and his measured numbers. He replied, “How many arbitrary parameters did you use for your calculations?†I thought for a moment about our cut-off procedures and said, “Four.†He said, “I remember my friend Johnny von Neumann used to say, with four parameters I can fit an elephant, and with five I can make him wiggle his trunk.â€
