We have discussed the composition and aggregate performance of the Compact Model Portfolio in other articles in this blog. Briefly, it is composed using a ranking that results from the application of time series analysis methodologies to the total value traded rather than the stock price.

In the above chart we exhibit a simple conditional analysis of the average return over the prior decade of portfolio members versus their ranking number. In these series, when the stock rank changes we follow the rank and not the stock. Thus the performance of the stock ranked “1” is the performance of the first ranked stock through time and not the performance of the stock currently ranked “1” (which is currently AAPL). In the chart we do see an out-performance for the higher ranked stocks, but its statistical significance is only notable when the general trend is estimated. If each rank were considered separately, we would not accept the hypothesis that the stock has significantly outperformed the benchmark.

The following chart updates the actual daily performance of the Compact Model Portfolio. This is a nominal scale portfolio, but it is an actually traded portfolio. The gap in early April occurred as I made some modifications to the system. Since then we're taking more risk, and so far the performance has picked up noticably. I'll explain these changes in a later post.

In the prior post we discussed the Compact Model Portfolio, in terms of its anomalous covariance with the benchmark index that served it well for years. To provide a little more context, here we exhibit a chart of the cumulative daily performance of the portfolio index (as distinct from the value of a traded portfolio) and it's benchmark, the NASDAQ-100.

Professional managers are fully awhere of the transient and random nature of the returns they create, whether actively or passively, and are real human beings with the behavioural biases and oddities that characterize us as a group. Thus, when we are presented with a month in which we do very well, we are aware that the future will likely hold periods of underperformance. Furthermore, it is likely that the month following a good month, the month during which we are preparing a formal summary of the prior returns that we know were good, we are more likely to underperform that recent history than outperform it. Nobody wants to write the letter:

Dear Investor, last month we did very well. However, as I write this I know that we're doing less well, so don't get too carried away with your newfound wealth that I've already lost.

Furthermore, a manager who is confessing to a particularly dire prior period of returns would greatly like to write:

Dear Investor, last month we did badly. However, as I write this I know that we're doing very well, so please do not distress too much over your losses, which have already been erased.

For an example of this latter tendency, I can simply refer to my prior post on the September, 2009, performance of our NASDAQ-100 futures trading system. Both these forces together, provide the incentive for outperforming managers to report their returns promply and for underperforming managers to linger a while before sending the letters out of the door. Thus, we can explain the tendency observed in our analysis of the incremental updates of the BarclayHedge data.

In my prior post, describing the August, 2009, performance of the Dynamic Trading Risk Factor, I alluded to the fact that the source data, the Barclay Hedge hedge fund indices, does not accrue atomically — in fact it is gradually updated during the course of each month.

In prior posts, I pointed out that the final month's factor return is mostly little changed from that computed from earlier, incomplete, data. As it should be, if the Law of Large Numbers is working it's usual magic. However, nature is often a little more subtle than that, and mankind often deliberately so. It therefore falls to us to ask whether there is any bias between the latency of a given fund's publication of its performance numbers and that same fund's performance relative to the mean of all reporting funds? Our strong prior is that human frailty would lead the bearers of good news to rush it out as soon as possible; and, in contrast, the bearers of bad news to wait a little longer than they should before revealing their shame.

The above chart illustrates my attempt to quantify this effect on the September, 2009, data, which represents the aggregated reported August, 2009, returns of around 2,500 funds as reported during the month. Not possessing an inside track at the data vendor, I had to resort to archiving and timestamping sampled copies of the cumulatively reported data. Thus, it was tedious to collect. Exhibited above is the mean relationship between the relative error in the Hedge Fund Index's reported average return and the relative proportion of the total number of funds that had reported at that time. (In both cases, relative means relative to the final sample value I have for that month's datum.) From this sample there does appear to be a very strong negative correlation, confirming our cynical prior from the earlier paragraph. Unfortunately, the serial correlation of the errors complicates a statistical analysis of the goodness of fit — but by eye it appears excellent.

It's now three month's since we started trading a portfolio designed to replicate the performance of hedge funds by picking those stocks with maximum exposure to the dynamic trading risk factor.

The portfolio's performance peaked in early August, and has been in a drawdown since then. It was hit hard on the 1st. September, when all the financial stocks dramatically underperformed the market. However, it is still profitable after fees.

UPDATE 03/15/2010: The performance chart has been replaced with the one for this current year. As you can see, the system is currently in a small drawdown.