fraud returns

Nonius · Post by **Nonius** » Mon Feb 16, 2009 10:08 pm

I've a number of return profiles of fraudulent hedge funds. Looking for more though. anyone have the returns of:

Cambridge Partners

Ashbury

Synergy

Princeton Economics

ETJ

Nidra

Sagam

?

Thanks in advance,

rgds,

Nonius

stralen · Post by **stralen** » Tue Feb 17, 2009 1:16 pm

try the returns from https://investatolsen.com/

purbani · Post by **purbani** » Sun Feb 22, 2009 11:14 pm

Hi Nonius

We have had quite a bit of success using the Hurst exponent to identify time series that are statistically 'too good to be true' or in sample perfect hindsight backtests. Method not infalible all PIPEs get flagged but correctly identified Fairfield Sentry and a number of other feeder funds. Can also be used to identify excess risk taking as in the case of Amaranth. The reason this seems to work is that the Hurst exponent is related to the alpha stable levy distribution through the fractal dimension. It is therefore a bit like an exponential distributon and highly sensitive to tail events. Sornettes method of identifying bubble risk appears to work the same way I.E things that are growing at an apparently faster than exponential rate ( think Cisco Fortune cover story in 1999 saying their projected earnings would exceed US Gdp within 5 years and more recently Amaranth ) grow to infinity in finite time which is impossible so must return to chaotic state or crash. Bottom line is Hurst not a linear function optimal is 0.55 to 0.68 anything above that indicative of excess momentum or dodgy numbers.

Would you be prepared to share some of your fraudulent time series data to test this ?

Kind regards,

Peter Urbani

peterurbaniinfiniti-capitalcom

Nonius · Post by **Nonius** » Sun Mar 01, 2009 7:29 am

Hi Peter,

that sounds interesting. my method computes fraud probabilities conditioned on a certain statistic related to returns. It appears to work well in all fraud cases we have in our DB, including all madoff feeders.

I will look into how I could share data with you; perhaps with no names attached and mixed with not knowingly fraudulent returns.

krott · Post by **krott** » Sun Mar 01, 2009 8:49 am

I'm doing factor analysis on time series. Would be fun to see how it works on your data. Have returns from a few "interesting" funds that I can share too. Email me phynancenewsalphacom

purbani · Post by **purbani** » Mon Mar 02, 2009 5:18 am

Hi Nonius

Sure a no names basis would be fine as a blind test generally better

Kind regards,

Peter

Komm.Schimpansky · Post by **Komm.Schimpansky** » Mon Mar 02, 2009 11:22 am

Added Sentry to our investable universe and the fund got the highest scoring.

Komm.Schimpansky · Post by **Komm.Schimpansky** » Tue Mar 03, 2009 11:13 am

What opinion deserves Evolution Capital Management?

Nonius · Post by **Nonius** » Wed Mar 11, 2009 6:39 am

Sentry, Kingate, and all the other Mad feeders are at the top of mine along with Bayou, Wood River, Manhattan, and Valhalla.

aaron · Post by **aaron** » Wed Mar 11, 2009 10:53 pm

Has anyone looked at applying Benford's Law? It states that the first non-zero digit of a random number should be distributed such that the frequency of k is Log(k+1) - Log(k) where Log means base-10 logarithm (or whatever base you're writing the number in). It's one of those results I find really satisfying. Obviously it depends on some assumptions about what "random" means, but it works in a lot of empirical tests.

When people make numbers up, they tend to use digits more uniformly than Benford's distribution. In fact, they tend to like 9's and 7's, which are infrequent in numbers derived from measurement.

Here are the theoretical frequencies from Benford's Law:

1
0.3010