“…is a slow trading fund, investing in commodities, currencies, bonds, and stock indices, and is designed to deliver an attractive return at relatively low volatility. …RIFF, started 13 months ago, did well during its first nine months but has been challenged by the turbulence of this fall, during which its returns were disappointing“.

There’s also generic information available at www.wsj.com obtained by WSJ from RIFF marketing materials:

“RIFF is a modestly-leveraged, slow-trading, global futures fund designed to provide substantial risk-adjusted returns, uncorrelated to US and global equity markets and with medium to low correlation to other asset classes… Targets holding times between nine and 12 months… The RIFF system is completely automated, with the exception of part of actual trade execution. Proprietary algorithms evaluate investment opportunities regularly in an effort to improve the portfolio“.

All of the above makes a good case for a dynamic factor analysis of RIFF returns.

We used fund monthly returns from inception through Dec. 2009 to measure its exposure to several dozen of various market, currency and commodity indices. The system identified the following index combination as the most credible representation of the funds historical factor exposures:

Although we don’t really know what positions the fund actually held, the result represents a straightforward directional strategy: short Credit and lately Energy, long Gold and Emg Mkts. The leverage is not very high; 100% at most. Notable deleveraging of exposures occurred during market turbulence in Aug-Oct 2008. It is possible that some significant restructuring occurred during that time as RIFF suffered significant losses. We show that, despite this, the major exposures remain the same (Gold, Credit, EM) with a slight reversal of other exposures (Equity and Commod).

The credibility of the analysis is very high and the portfolio of systematic exposures (“style” line in chart below) tracks the fund (“total”) very well.

There’s a reason why we performed our analysis through Dec 2009. Once we expanded the date range and added the first two months of 2010, the analysis has shown major exposure shifts and the quality of the analysis started deteriorating very quickly. This could indicate many things including potential rapid restructuring of the fund in the first days of 2010. Such exposure shifts could be related to the recent news that Renaissance management is mulling a shut-down of both RIEF and RIFF.

First, we expanded the time period from our previous post. The chart below now includes excess return information for five full years from 2004 where we’ve highlighted three months: July ’07 and ’08, which have come under heavy scrutiny for alleged insider trading and July ’06, where outperformance was notably higher than both July ’07 and July’08. Over the five year period (2004-2008) these three months were the only months when the Galleon fund significantly outperformed its peers—an average technology hedge fund in the index.

Clearly, July performance numbers for each of the three years are deemed as statistical outliers (regardless of the legal connotation) which potentially could distort any further analysis of Galleon returns. In addition, the complaint mentions July 2007 $4M gain from trading Hilton—not a technology stock—which could also “contaminate” our analysis. Given the facts above we decided to remove all three July observaions as outliers.

Next, we proceed with a dynamic forensic analysis of the Galleon Technology fund’s returns similar to the one performed in our previous post using MPI Stylus™ and its DSA engine. The results of this analysis are presented in an exposure chart below. Note that this chart does not show actual holdings, but allocations to different factors that best explain the returns of the fund.

We note quite stable long exposure to several Dow Jones technology sectors. The exposure to foreign technology companies is represented by the MSCI All Country ex. US Tech index, indicating positions in ADRs, foreign stocks, or simply sensitivity to foreign markets through investing in U.S. stocks. Short exposure to PowerShares QQQ ETF is supported by the fund’s SEC filings according to which the fund maintained at times a significant position in QQQ put options. Both the cash exposure (about 60%, an indication of net 40% market exposure) and the size of the short position (30-40%) are similar to our earlier results. At the same time, these results are slightly different from the ones in the previous post, which is expected given that we removed three very large outliers.

It should be noted that because of the removal of outliers the statistical quality of the analysis improved significantly. The R-squared statistic determining the quality of fit is 89% and the Predicted R-squared, MPI’s proprietary cross-validation statistic, is 79%. Such a high quality regression is more typical for a large, diversified mutual fund. The quality of fit is also illustrated in the performance chart below where an exposure-weighted portfolio made of factor indexes (called a “Style” or “Tracking” portfolio) closely tracks the fund’s actual performance in-sample (“Total”).

Note that the tracking is exceptionally good through the middle of 2006 where the fund and the tracking portfolio lines begin to deviate slightly despite the removal of outliers. Nevertheless, both the pattern of performance and its magnitude are captured very well throughout the entire five-year history.

While the results of this analysis are very intriguing and somewhat unexpected, the study itself carries very important lessons for investors. First, it shows that analysis of hedge fund returns is a delicate, iterative process requiring careful examination of residuals. If outliers cannot be explained by any available portfolio information they could warrant removal or winzorisation. More importantly, removal of several large “alpha” outliers allowed us to show that in the remaining periods this quintessential high-turnover arbitrageur behaved more like a diversified mutual fund, with returns mimicked by a few long-term directional bets. And while massive computer-generated trading of Renaissance RIEF resulted in such an immediately apparent pattern, in the case of Galleon, the long-term directional pattern became visible only after identification and removal of several exceptional returns.

Daniel Li, PhD contributed to this research.

Daniel Li
Michael Markov

In the recent insider trading scandal involving the founder of Galleon Group, Raj Rajaratnam, the government used wiretaps to secretly record his phone conversations and those of his alleged accomplices. In the complaint, government prosecutors present an insider trading case against Rajaratnam and several other executives for illegally profiting from trading stocks and options of Hilton, Google, Akamai and others.

The full text of the complaint is available on the DOJ website and reads like a detective story.

There is little doubt that wiretapping is a highly effective investigative tool, but we decided to try some less intrusive methods. With $20M in profits allegedly pocketed in the scheme, we thought that there might be a possibility that the effect of these transactions could show up in Galleon’s hedge fund returns.

As a first step, we take a closer look at the actual numbers in the complaint. Below are the facts of the alleged profit from trading in 2007-2008. The rest of trading (e.g., AMD) hasn’t produced meaningful profit.

• July 3-5, 2007 trading in Hilton stock resulted in a profit of about $4M (pp.15-16 of the complaint)
• July 17-19, 2007 trading in Google stock and options resulted in profit of $8M (pp.17-19)
• July 31–Aug 6, 2008 trading in Akamai stock and options resulted in profit of $3.5M (pp.23-25) A detailed analysis of Akamai stock prices and positions listed in the complaint point to about $4.5M P&L gain booked on July 31, when Akamai stock dropped 25%. The realized gain could differ as positions were closed in early August when the stock price was steady.

The October 20th article in The New York Times puts the current assets of the Galleon Technology Fund at $350M. Assuming that the technology fund lost about 50-60% in assets over the past two years, and given the profits shown in the trades above, one should observe spikes of about 1-2% per month over the fund’s systematic, market driven return.

First, we examine the fund’s monthly returns over two years, 2007-2008, where we highlighted the periods of alleged fraud in 2007 and 2008.

One can observe a 5% return in July 2007 and 2% in July 2008 but there’s no conclusive pattern of anything questionable going on during these periods. Essentially, the pattern of the fund’s monthly returns seems random.

The next obvious step would be to distill or filter out systematic market or strategy-driven return. Once that is done, the remainder would more likely reflect security selection picks. A reasonable choice for such a procedure would be to subtract the monthly returns of an appropriate benchmark from the monthly returns of the Galleon Technology fund. In the chart below we show the return differential between the fund and the CISDM Technology Index (representing average returns of hedge funds in the Technology category). The results are striking: the only two periods when the Technology fund significantly outperformed the index are the two months mentioned in the complaint.

Of course, the magnitude of excess performance spikes is 2-3 times higher than what we expected to see and the results are completely dependent on the choice of index. Still, this qualifies as a good initial sanity check.

However, what if a fund had an obscure strategy and a reasonable benchmark wasn’t easily available? In such a case, a forensic analysis of the hedge fund returns could provide an answer. Using MPI Stylus™ and its Dynamic Style Analysis model (“DSA”) we attempted to reverse-engineer the Galleon Technology fund’s returns.¹ After an exhaustive search of thousands of potential combinations of factors and sector indices, the model selected only a handful of relevant factors that best mimic the return behavior of the fund over a two-year period from 2007-2008. The results of this analysis are presented in exposure chart below. Note that this chart does not show actual holdings, but allocations to different factors that best explain the returns of the fund.

The apparent exposure to the Tech and Biotech Dow Jones sector indices, as well as the amount of leverage determined by MPI’s DSA model, are supported by the scant information about the fund’s strategy. It is widely known that the fund made long and short bets on software and hardware companies and maintained a moderate short position of 30-40%. The latter is confirmed by our analysis showing average negative exposure of about 40%. The credibility of the above analysis is also supported by MPI’s proprietary cross-validation statistics. The cash position of roughly 70% could indicate either hedging or margin position. In more generic terms it tells us that the fund had about 30% net long exposure.

Note that the factor exposures are exceptionally stable and an exposure-weighted portfolio made of these indexes (called a “Style” or “Tracking” portfolio) closely tracks the fund’s actual performance in-sample as shown in the cumulative growth chart below.

It should be noted that even though the fund’s strategy was primarily short-term arbitrage, a significant portion of its returns could be explained by directional sector bets and easily replicated with a handful of ETFs if anyone wanted to.

Finally, we compute the return differential between the fund and its dynamic in-sample tracking portfolio (“Style”). These returns are shown in the chart below and are called “Selection” returns as they could be attributed to non-systematic factors such as skill, luck or… insider trading.

We readily observe that both July’07 and July’08 returns are outliers on the positive side. Their magnitude is in the range of what one would expect to see based on the information presented in the complaint. Interestingly, if these two months are taken out of consideration, our analysis shows that the fund manager had unimpressive stock selection results in 2008. Obviously, not every spike in selection return is a suspect of insider trading and the prosecution is yet to prove their case. At the same time, this case adds another important dimension to routine analysis of a portfolio manager’s selection skill: what if it’s neither luck nor skill but something else? An analysis like this one helps investors to frame their questions and precisely position them in time.

What was presented in this quick analysis of a suspect fund obviously cannot be used as evidence in court. Clearly, wiretapping as a prosecutor’s tool wins here hands down. But, for investors with no access to their investments’ positions (forget about wiretapping!) and struggling to reconcile their funds’ performance with common sense and market moves (Performance is too good to be true? Why are the returns so smooth? Why the fund is up when everyone else is down? etc.) a quantitative forensic analysis remains the only viable option. Investors should be and can be smarter than wiretaps.

1. For those unfamiliar with our methodology we refer to the Research Center and other posts in this blog for details.

Using “traces in the sand” we will try to recreate the pieces of the performance puzzle that are otherwise hidden from the investors. These traces represent the fund’s monthly performance numbers, which frequently the most the investors would get from a fund. The chart below represents a dynamic analysis of the fund using Russell style indices and MSCI EAFE as a proxy for Int’l equities.

Although the exposures remain similar to the ones in our 2007 study, one could observe a profound change: the fund is behaving as if it’s net neutral or net short. This could be inferred from the sum of short exposures (about 90% in midcap growth) being about the same or greater than the sum of long exposures above the X-axis. That’s quite a change as compared to exposures 3-4 years ago which indicated a 100% net long allocation. No wonder the fund missed the recent market rally.

Moreover, in April alone, midcap growth stocks (short exposure in REIF) delivered twice the performance of large cap stocks (long exposure) which is reminiscent of the statement about the volatility in the fund conference call. Basically, our analysis of the fund’s returns shows that capitalization is the most dominant factor of the fund’s performance. The chart below translates it in the language understood by finance professionals: performance attribution. Based on our analysis, RIEF April losses are dominated by its net short exposure to midcap growth stocks.

As a reference we provide the following chart that shows how well the portfolio based on exposures “Style” is replicating (in-sample) the fund (“Total”).

Please note, at no time in this analysis are we claiming to know or insinuate what the actual strategy, positions or holdings of this fund were; nor are we commenting on the quality or merits of Renaissance’s strategy or that of any other manager. Instead, we are seeking to demonstrate how MPI’s Dynamic Style Analysis can be used to better understand fund behavior, anticipate performance, identify risks and, possibly, replicate fund performance in certain cases.

This neither legitimizes SIB nor makes the stable value fund an immediate suspect. It is just another confirmation that investors need more disclosure on investment vehicles priced at book value. Having credit and interest rate exposure information is important. Having monthly market values would be extremely helpful. Not only it allows one to monitor the the MV/BV ratio, but more importantly, helps to uncover hidden risks using returns-based tools.

There is growing public awareness of potential risks associated with book value accounting in stable value funds. A couple of weeks ago I mentioned my blog to Eleanor Laise with The Wall Street Journal and she wrote an excellent article on stable value funds in the March 26 issue of WSJ.

1. Please see my previous blog post

As shown in the excerpt below, the SEC points to SIB’s absolute performance vs. market indexes:

“Double-digit returns… over the past 15 years” which refers to the chart in Par. 28 of the complaint. Note that these are gross returns, while net returns paid to investors were about 8%. Many hedge funds generated higher after fees returns in 1992-2006. Plus, Stanford returns were high three and five years ago, so there must be something else.

Very suspicious performance in 2008: the fund lost “only” 1.3%. Guess what? So did a quarter of all hedge funds. According to the latest HFR data, 1318 hedge funds out of 5313 reporting lost less than Stanford in 2008. And so did about 12% of mutual funds (excluding money market).

Par. 4 of the complaint states that for SIB producing the same returns of 15.71% for two consecutive years 1995 and 1996 was “impossible” to achieve if Stanford have managed a “global diversified” portfolio of investments. Here’s the exact quote from the complaint:

So when did identical returns for two consecutive years become a red flag? This seems more a sign of transparent reporting. If someone were to concoct a fraud, the first thing they would do is to change at least one decimal to make it less suspicious! And by the way, what should be considered a red flag–is 1bp difference sufficient? How about 5bp difference?

First we should note that it is statistically “probable” and “possible” to obtain the same investment return for two consecutive years even when one is invested in liquid market instruments. Highly diversified portfolios would increase the probability. Diversification reduces volatility overall and aggregating volatile data into annual returns further reduces volatility. In addition, if underlying assets have similar performance during these years it increases the likelihood of the portfolio achieving similar results.

We ran a quick test and found about two dozen mutual funds and hedge funds having 1995 and 1996 returns within 10bp range. Note that these are only survivors and the number of actual funds that existed at that time could be easily 2-3 times higher. There are some identical matches in consecutive year performance and for many funds 1995-96 results are within 2-3 bp range.

So is it really a red flag? Well, not by itself based on the above considerations. Whether these numbers represent real returns and are not made up requires more sophisticated analysis such as returns-based (RBSA) and other quantitative due diligence methods.