Selling by hedge funds is still putting pressure on the market (see WSJ article). As we have discussed over the last month (see posts here and here), many redemption requests by hedge fund investors are now meeting their waiting periods, causing many funds to sell assets in order to raise cash. As quoted by Gregory Horn, president of Persimmon Capital Management:

"In mid-October, redemption levels were in the 5% range but all of a sudden now it's cranking up to as high as 25% for some funds."
Certainly not good news for hedge funds, but maybe even worse news for the market. With continued forced selling, it is unlikely the market will quit trying to find a bottom. Hedge funds will continue to sell every rally, increasing volatility. As long as the VIX continues to spike and stay at elevated levels, and we continue to see the "punch-in-the-stomach" late day sell-offs after nice rallies (both of which I suspect are indications of further hedge funds selling), we will continue to be in a volatile holding pattern between 850 and 1,000 on the S&P. Unfortunately, it is difficult to know exactly when the selling will quit, as the selling and redemption requests are tied together in what is becoming a volatile catch-22 pattern that is feeding upon itself. The other day I heard an analysts say it was "too late to sell, but too soon to buy." Until hedge fund investors believe the former, it is unlikely any investors will quit believing the latter.

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.

Weakness In Credit Card Debt Offerings

Posted by Bull Bear Trader | 11/06/2008 08:40:00 AM | , , , , | 0 comments »

For the first time since 1993, credit card companies were unable to sell bonds backed by customer payments (see Bloomberg article). Top-rated credit card-backed securities maturing in three years are selling at spreads of 475 basis points over Libor, compared to a spread of only 50 basis points less than a year ago. Given higher unemployment, leading to potentially higher credit card use and an inability to pay, lenders are expecting higher default rates for 2009. American Express is already accessing the Fed commercial facility program, as well as cutting 10 percent of its work force. Bank of America, JPMorgan, and Citigroup all rely on the debt market to fund their credit card portfolios, and could also subsequently be impacted by higher spreads and lower liquidity.

It appears that efforts by the central bank to encourage investors to purchase corporate debt are not having much success (see CNN Money article). While it is often the case that companies are hesitant in the fourth quarter of a fiscal year to purchase debt for fear of creating problems on their balance sheets, this year the policy decisions of the Fed also appear to be having an impact. Currently, the Fed is offering a much lower borrowing rate than the market, with rates as low as 1.55 percent for three-month paper. The market is offering closer to 2.6 percent for similar debt. Until the market rates are lower, or the Fed rates become higher, it is not likely that investors will take the extra risk of buying corporate debt.

In a seemingly unrelated story, automakers are apparently unhappy with the $25 billion in loans they are set to receive for making more fuel efficient cars, with paperwork and administrative hurdles delaying the money (see Reuters article). As a result, the industry is continuing to burn through cash at a faster pace, causing GM to warn that the industry is now "near collapse," requiring further assistance. New aid is now being demanded, possibly up to another $25 billion in loans. The difference is that now these loans would come with no strings attached, with the expectation is that the companies would use the money to pay retiree health care obligations.

As the current financial crisis continues to unfold, one can expect that similar market circumstances (interest in cheaper Fed debt) and stimulus requests (taxpayers covering operating costs) will continue. At some point the response to such request will have to be no, and the results of such decision will have to be felt. Unfortunately, the longer that requests are accepted and government intervention occurs, that longer it will take to separate business from government and allow the free markets to get back to doing what they do best - rewarding with cheaper capital those companies that are managed well and properly positioned, while punishing those that aren't.

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.

CDS Notional Value Less Than Expected

Posted by Bull Bear Trader | 11/04/2008 07:06:00 PM | , , | 0 comments »

As reported at the Deal Book blog, the total size of credit default swaps outstanding on corporate, government, and asset-backed securities is "only" $33.6 trillion, smaller than the previous estimates of +$50 trillion. The largest CDS dollar amounts were written against debt for Merrill Lynch, Goldman Sachs, Morgan Stanley, GE Capital, Countrywide, GMAC, and government debt in Turkey, Italy, Brazil, and Russia. Nonetheless, much of the notional value has been reduced through hedging. The weekly reports are being offered by the Depository Trust and Clearing Corporation, and are expected to also include trade volume and turnover in future reports. Expect more transparency going forward. A little sunlight is the best disinfectant.

Nassim Nicholas Taleb, author of the Black Swan, is using the impact of extreme events mentioned in the Black Swan to help the hedge fund he advises, Universa Investments L.P., benefit in October (see WSJ article). Separate funds in the Universa's Black Swan Protection Protocol were up between 65 percent to 115 percent in October alone. The fund has a strategy of buying far-out-of-the-money put options on stocks and stock indexes. Most of the time the fund will take small loses when nothing unusual happens, but occasionally a black swan even occurs (such as the recent 20% market decline in one month), causing the gains to be extraordinary. In addition to using deep out-of-the-money puts, the fund has a strategy of keeping more than 90% of its assets in cash or cash equivalents, and is believed to break even or only incur small losses while waiting for the next black swan event. The fund recently made huge profits buying cheap puts on the S&P 500 and AIG, with the S&P puts increasing in value over 50-fold. While profitable during times of extreme volatility changes, one has to wonder how often such changes and moves will occur. Even a previous fund that Taleb was involved with had to shut down in 2004 after lower volatility caused returns to suffer and investors to flee. But then again, a 50-fold increase in a few trades gives you time to wait for the next event. You just need to be patient, and of course, know where to look as you wait for the worst to happen. Easier said than done.

An recent article in the WSJ discusses the AIG risk models developed by Gary Gordton (note, Gorton, not Gordon as originally posted), a professor at the Yale School of Management. The headline of the article boldly states "Behind AIG's Fall, Risk Models Failed to Pass Real-World Test." Yet, did the models really fail? Gordton's models were developed to gauge the risk of AIG's credit default swaps, but according to the article, "... AIG didn't anticipate how market forces and contract terms not weighted by the models would turn the swaps, over the short term, into huge financial liabilities." The quote is interesting in that it highlights what may be at the heart of AIG's problems. As a result of its ignorance on whether the short-term collateral risk needed to be considered, or its belief that such risk was not something to be worried about, AIG made a decision to not have Gordton assess these threats - even stating later that it knew his models did not consider such risk. So this begs the question once again. Did the models really fail (as approached by Gordton and approved by AIG), or was it more of a lack of understanding of the very products they were modeling? I know some will ask what's the difference - in the end the models were incomplete - but the distinction is significant.

In hindsight, it is easy to point fingers and wonder exactly what risk AIG was even trying to manage. But the real problem here seems to be less about one particular modeler getting it wrong, or developing incomplete models, and more about management ignoring to consider some risk while putting faith in the very same models that were not designed to give the level of confidence or enterprise-wide coverage that is being used to engender confidence. Even the WSJ article (in the body of the story) mentions how "Mr. Gordton's models harnessed mounds of historical data to focus on the likelihood of default, and his work may indeed prove accurate on that front. But as AIG was aware, his models didn't attempt to measure the risk of future collateral calls or write-downs, which have devastated AIG's finances." Of course, this did not keep AIG from trading as though it did, and therein lies the problem. The failure here is less about modeling, or even risk management, and more about corporate management and decision making. Yet, the perception that the problem is with modeling is widespread. Even Warren Buffett is quoted as saying "All I can say is, beware of geeks .... bearing formulas." But what is the alternative? Shall we abandon all risk modeling and simply use our gut instincts? Should we just take risk off the table completely? I don't believe so. While better risk management models should continue to be developed, maybe a little humility is a good place to start. Understanding a company's limitations is key to uncovering its strengths and protecting against its weaknesses.