Quant funds who bet on high-quality stocks, while at the same time shorting those stocks that are over-priced, have been under-performing the stock market (WSJ). This under-performance has come in part as a result of poor balance sheet stocks being pulled along by the momentum train of the last few months. The performance gap has even widening recently as short squeezes have pushed weak stocks higher, just as those with brighter prospects have done worse. Some feel that the under-performance of higher quality stocks may be an indication that the recent move is running out of steam, and that the market may be due for a correction. Breadth and other overbought/oversold indicators continue to flirt at times with high levels and cause concern among the bulls - but then again, they did so one month ago as well. Nonetheless, a correction, even if mild and short-term, could be in the cards as it seems an increasing number of participants have started watching and waiting for their overbought biases to be confirmed, including some of those who continue to be long in the market. Time will tell, but the next month could be interesting, and telling, as we move into October.

According to the recent TIM (Trade Ideas Monitor) report, over the last five trading days, institutional brokers are continuing to becoming more bearish (see note below, previous post, or the youDevise website for additional information on the TIM report). For the five trading days ending June 4, the number of short ideas as a percentage of new ideas sent to investment managers increased 41.35%, compared to 35.88% just a week ago. The TIM reflects which direction brokers are expecting a stock to move over the next 1-3 weeks. The TIM Long-Short Index, measuring the total number of long ideas compared to the total number of short ideas sent to clients, decreased 35.4%, further highlighting negativity and bearishness from brokers. As for individual securities, Palm (PALM), Freeport McMoran (FCX), and Petroleo Brasileiro (PBR) were the stocks most recommended as shorts by institutional brokers.

Note: The Trade Idea Monitor (TIM) is an application for measuring "ideas from authors (mainly brokers) to recipients (mainly buy-side clients)" (see youDevise website). It is used by institutional brokers to send long and short equity and ETF trade ideas to clients. The TIM Report is based on the number of real-time equity trading ideas sent to over 4,300 equity sales people, sales traders, and analysts at over 300 institutional brokerage firms to more than 100 hedge funds, quant funds, and investment managers.

Given the recent "black swan" events in the market, quant funds that have relied on longer-term trading strategies have suffered (see Reuters article). As a result, many quants are now focusing on higher-frequency strategies that are executed quickly, both to get into and out of positions. No doubt that such an increase in programmed algorithmic trading is contributing to already elevated levels of market volatility. Ironically, many traditional quant funds operate more efficiently in stable market environments, causing such funds to suffer under the recent higher levels of volatility.

The change in trading duration is needed in part since many of the longer-term strategies are no longer valid given the changing market landscape, which due to company failures and shifting regulations, seems to be changing nearly everyday. Modelers using intelligent trading systems, especially supervised systems like neural networks that require extensive historical data in order to learn market patterns, are finding it a challenge to train their systems given the changing market dynamics and subsequent lack of relevant data. The tracking errors have also been significant enough to cause many funds to scale back their use of leverage, putting further pressure on quant funds that rely on borrowed money to juice returns.

While some quant funds could potentially go out of business given current losses, there is no doubt that many other quant traders are seeing this as an opportunity to create new algorithms not yet adopted by the larger quantitative trading community. I image the next great algorithms and trading strategies - which we will not hear about for a few years - are begin developed and deployed as we speak. If there is one thing many quants like more than money, it is a good challenge. The market has certainly provided the challenge, along with some unique opportunities.

Trend following managed futures funds have outperformed hedge funds this year, gaining 8.9 percent year-to-date (see WSJ article). Hedge funds have lost almost 19 percent during the same time frame. Managed futures funds often use quantitative trading algorithms to spot market trends, at which point a long or short position is quickly taken in futures or other derivatives. Managed futures underperformed between 2003 to 2007 when volatility was lower, but have since done better as volatility increased. The ability to quickly initiate an "unemotional" short selling signals has also added to recent gains. Ironically, the reduction of risk by some traders and hedge funds has resulted in less liquidity and larger price swings, both of which have allowed managed futures to outperform hedge funds who have traded in many of the same markets.

A recent New York Times article follows up on a previous discussion (see blog post) regarding modeling and risk management. While financial engineering and quants will continue to receive some criticism for the recent problems in the markets, along with development of generally inadequate risk management models, the NY Times article further explores whether it was the models or human failure that are to blame for the current financial situation. There is no doubt that some models, especially black box models, have created some of the problems, but we cannot really fix the problem until we know the root causes. Is it simply a matter of not having sophisticated enough modeling techniques, or are poor assumptions, inadequate data, and lack of oversight also to blame?

Research at the IMF found that quantitative methods underestimated defaults for subprime borrowers, at times often relying on computerized credit-scoring models instead of human judgment (then again, I am not sure Moody's or S&P were much better or more timely, but I digress). On the other hand, economists at the Fed concluded that risk models had correctly predicted that a drop in real estate prices of 10-20 percent would be bad for subprime mortgage-backed securities (not a surprise), but that analysts themselves assigned a very low probability of this happening. In fact, the Fed study might be at the heart of the problem - human behavior.

As mentioned in the article, asset prices depend on not only our belief, but the belief of others. In an "efficient" market the participants expect that the true (or near true) price is reflected, even if the belief of one person is far from the efficient value. Of course, it is hard to model the beliefs of the market, so often the beliefs, hopes, or profit motives of one person may come into play, with at times disastrous results. The problem is compounded when risk management models are assumed to follow some natural law, when in fact both the theory that defines the model, and the inputs provided to the models, are more stochastic in nature.

As I have argued before, we need risk models, even those based on imperfect mathematics and assumptions, but we must always take into consideration what could happen if we are wrong. If our assumptions are too optimistic or too pessimistic, what is the fallout? We should be asking ourselves how confident we are in mathematics used AND the assumptions made. Are there assumption scenarios that could bring a company to its knees? What models are best to help understand all the possible outcomes that we should be worried about? These are the questions risk managers need to continue to ask. Yet in many instances blind black box models with fixed assumptions were trusted. Unfortunately, I suspect that even with the recent problems and consequences hanging over our heads, asking which models and assumptions point to the highest profits or lowest levels of regulatory capital will once again start to be considered. After all, its just human nature. Of course, avoiding pain is also a natural human instinct. Maybe there is a lesson to be learn there as well when the next bailout is voted on.

We have heard the old computer adage "garbage in - garbage out" to highlight how even a sophisticated computer program will produce nonsensical output if provided nonsensical input. The world of risk management is no different. The quants on Wall Street that are hard at work developing the next best trading and risk management systems are not perfect, but their job and measured performance becomes even more difficult when they are given bad information (see NY Times blog article).

On the surface, the goal of the risk management quants seem simply - tell me how much of the portfolio is at risk, and then tell me how much I need to sell, or how much capital I need to set aside so that I can sleep at night. In a sense, prepare me for the 100 year flood. Yet the 100 year floods seem to be occurring more often. Why is this? One possible reason is the "garbage in - garbage out" phenomenon, the problem of which is exacerbated as the markets continue to become more complex. Recent case in point, Lehman Brothers. As talk continued about a potential failure with Lehman, it became almost impossible to tell what their exposure was. Who are the counterparties? What are the default rates? What are the recovery rates? And most frightening of all, was does this new product even do? If companies cannot even understand the products they are selling, how can one expect to develop an adequate risk management system to help protect against the 100 year flood when it is not clear that water damage is even the problem, or that the strength of the levees is even important?

There is no doubt that some systems on Wall Street were provided optimistic data and assumptions, or had smoothed-out historical data in order to reduce the number of times the warning bells sounded, ultimately keeping companies from scaling back positions or redeploying capital to less profitable areas. But I suspect that there was an equal number of firms that diligently tried to provide the best information possible, but were simply in the dark. Why is this the case? There are no doubt a number of reasons, many of which are financial, but the separation between those that develop such risk management systems from those that develop products that need to be managed is not helping the situation. The information gap most likely goes both ways as the financial engineers are unaware of the workings of the risk management systems, while the risk managers are blind to the real exposures of the complex structured products that are being purchased and sold.

As more retail investors enter the markets, institutional trading continues to rise, and the securitization and engineering of products increases, both volatility and the values at risk will continue to affect markets. As new products are offered to the markets, it is essential that those who develop such products are on the same team as those who manage the risk. Risk management truly needs to be an enterprise-wide proposition, with incentives in place to reward those who properly managing risk, just as they are in place for those who engineer and sell the latest structured product. Performance on Wall Street is measured in money. If risk managers start to become rewarded in a similar manner, or if the risk management of new products begins to influence how new products are rewarded, we may then begin to find that the garbage provided to risk managers will begin to smell a little better.