Do Outperforming Funds Report Early? --- More Data

by Graham Giller March 08, 2010 11:15

In an earlier post we addressed the question as to whether there is any bias in the delay between a fund's month end and the performance of that month. Our cynical theory is that managers with good news to report report it early and those with bad news to report report it late.

Since September, 2009, I've been sampling the reported monthly return of the BarclayHedge Hedge Fund Index. This is a simple average of the monthly returns of all funds that have reported to the group at that time. From time-to-time during each month I've sampled the main index's reported average monthly rate of return and the number of funds that have reported. You can find this data on my blog at the page Return Index Accumulation Report. The chart below is an analysis of the error, meaning the difference between the average monthly rate of return for the entire universe reporting on the sample date and that value finally reported at the end of the month. To gauge the average scale of the bias we fit a simple model by least squares:

LaTeX Rendered by www.forkosh.com/mathtex.html

Here B is the bias, or the average error between the sampled monthly rate of return and the final monthly rate of return; S is the scale we seek to estimate; and, p is the proportion of funds reported (i.e. the number in the sample divided by the final number of funds reporting in that month). Our estimate of the average scale of the error is (51 ± 8) bp/month.

Do Outperforming Funds Report Early (Large Sample)?

Be the first to rate this post

  • Currently 0/5 Stars.
  • 1
  • 2
  • 3
  • 4
  • 5

Tags: , , , ,

Empirical

Why Would Performance Affect a Hedge Fund's Reporting Schedule?

by Graham Giller October 02, 2009 11:22

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.

Be the first to rate this post

  • Currently 0/5 Stars.
  • 1
  • 2
  • 3
  • 4
  • 5

Tags: , , , ,

Heuristics

Powered by BlogEngine.NET 1.4.5.0
Theme by Mads Kristensen | Modified by Mooglegiant



RecentComments

Comment RSS

About the Author

Graham Giller - Headshot GRAHAM GILLER
Dr. Giller holds a doctorate from Oxford University in experimental elementary particle physics. His field of research was statistical astronomy using high energy cosmic rays. After leaving Oxford, he worked in the Process Driven Trading Group at Morgan Stanley, as a strategy researcher and portfolio manager. He then ran a CTA/CPO firm which concentrated on trading eurodollar futures using statistical models. From 2004, he has managed a private family investment office. In 2009, he joined a California based hedge fund startup, concentrating on high frequency alpha and volatility forecasting. A detailed resume is available.

Pages


Disclaimer

Nothing on this site should be construed as a reccommendation to buy or sell any specific security nor as a solicitation of an order to buy or sell any specific security. Before making any trade for any reason you should consult your own financial advisor. The author may hold long or short positions in any of the securities discussed either before or after publication of an article mentioning such a security.

Copyright Notice

All post on this blog are © Copyright property of Giller Investments (New Jersey), LLC. All comments are the property of their respective authors and neither the author or this blog nor any entity associated with him are responsible for or accept any responsibility for their content. Offensive comments and spam may be removed at the authors discretion.

Data provided on this blog or through links to this blog are either property of Giller Investments (New Jersey), LLC or publicly available or derived from data that is publically available. Any data that is proprietary to Giller Investments (New Jersey), LLC is published here for the public interest and may be reproduced for private research or in public forums provided that suitable attribution and acknowledgement of ownership is made.

Privacy Policy

We use third-party advertising companies to serve ads when you visit our website. These companies may use information (not including your name, address, email address, or telephone number) about your visits to this and other websites in order to provide advertisements about goods and services of interest to you. If you would like more information about this practice and to know your choices about not having this information used by these companies, click here.