The Curious Case of United Continental (UAL)

6 month TA on UAL

United, despite being an airline with notoriously bad service, and having a rough quarter (net loss in the first quarter widened to $213 million or $0.65 per share from $82 million or $0.49 per share in the prior year, below Wall Street consensus expectation), they recovered nicely in stock price thanks to smaller than expected loss in American Airline. In fact, they rise so much so quickly that they broke the bearish trend and coming right at the resistance level at around 23.5, as shown above.


Looking at fundamental, the huge loss in the first quarter made sense: rapidly rising fuel cost, dwelling economy leading to less passengers going on vacation or business travels, and the unexpected hit from the massive earthquake and subsequently tsunami plus radiation leak in North Japan. All these are big ticket hitter and should affect most international airlines. To add salt to the injury United is just going through merger with Continental and there will be some settling time with the integration, especially on how to cope with the unions.


So what's next? Fundamentally the travel season is beginning, and should help with their profit. Key part to watch is the oil price's movement which will have upside pressure from summer's fuel use (road trips, air conditioning, etc) and the unrest in the middle east. Downside pressure for oil includes European debt crisis and China's heavy hand on curbing inflation. The Japan case is still a mess, but rebuilding takes oil, too.


Technically if UAL breaks the second resistance at 24 then, though weak, they will be going in a bullish trend.


Disclosure: I had net short position on UAL, and no other airline exposure. And I hate United - they should go down and let someone better like Cathay to run it.

Market Fear

Have you ever wondered why the "velocity" of price drop usually far exceeds that of price gain? In other words, usually price drops much faster than price appreciation. I mean, aren't traders inherently greedy? Wouldn't they chase up what they seem to like, as much emotion as running in fear? To answer that question, we must probe at psychology of trades, in the field of behavioral finance.

There is a monumental paper titled Prospect Theory written by Daniel Kahneman and Amos Tversky from Princeton in 1979 describing human decision instead of optimal profit maximization utility theory. This paper has been quoted so much that it ranked second of all economic papers ever published. The first one was a statistical tools paper which presently have no additional value of what we have already known. So this Princeton paper can be regarded as the one of the most useful reference in finance and economics.

One of the key take-away from the paper is that people value loss much more than gain, despite equal magnitude in outcome. That is, the perceived loss in my mind for 10 bucks loss is far greater than perceived value of 10 bucks gain. And the reference point moves with one's wealth, so this greater perception of loss is present in most people regardless of the size of their wealth.

In addition, there is a tendency for people to overweight the small probability of loss, leading to a risk-averse attitude towards investment with small risk (probability of loss) and moderate reward.

Overweight of Small Probability of Loss

Notice that there is two discontinuities in the above graph, and that the perceived change of value is quite flat for a large range of probabilities in the middle. In other words, a winning chance of 40% is seemingly equivalent to 60% chance of profit in our mind, even though mathematically they are vastly different.

On the other hand people are also tend to overweight small probability of gain, leading to risk-seeking attitude towards investment with small probability of gain but moderate change of loss.

There is a third area in human psychology regarding risk and profit, and there exists several zones of comfort that is dependent on the size of one's portfolio or wealth, in which more risk is tolerable when the size of that bet is small regarding to the total size of one's wealth. This one is easy to understand, and I will not explain further.

Now let's come back to our original question. What happens when a trader see a group of sell orders showing up on exchange's quote feed? In his mind, a realization of things may go south occurs. Now we are in the region of "potential loss". Once the loss move past a trader's comfort zone (depending on the size of his trade, usually a small swing in prices will move past a day-trader's comfort zone because of the inherent large bet nature of day trading), panic will kick in very quickly. And the fear feeds on themselves as you may notice from the first graph above, that the drop of value falls rather quickly. This explains why price drop as a result of exodus sell would often be much deeper than any other euphoric rise of prices.

Synthetic CDO - what is it?

 Lately a bunch of us were discussing the notorious topic of subprime mortgage debacle, that investment banks created instruments so complicated that even CEOs and board of such banks did not understand how large the risk was. To facilitate discussion, I put together the below as a recipe to construct such monstrous innovation which are so popular and high in demand in 2007 that they went out of subprime mortgages to be packaged.

Construction of a Mortgage Backed Securities Collateralized Debt Obligation (MBS CDO)

1. Bundle up mortgages, can be residential or commercial (or both), into a group of asset that generates revenues using mortgage payouts.

2. These mortgages can come from a variety of sources. When the good ones are taken, worse mortgages have to be considered. Most mortgages do not have good rating, and especially after the good ones are packaged away, subsequent bundles contained a lot of sub-prime mortgages, with a BBB rating (worst).

3. With these bundled up asset, securitize (i.e. issuing shares of) these bundles with the mortgages as backing. Let say there are 10 mortgages, and we can now issue 100 shares representing these mortgages. So each share contains the asset of 1/10 of an average mortgage of these ten. These shares are now called Mortgage Backed Securities. For detail you can go here in Wikipedia for a nice explanation.

4. The MBS can be traded in the market, but since there are so many different type of mortgages, trading these MBS is not very efficient. To solve this problem, a new product called Collateralized Debt Obligation is created by bundling up various MBS together, with the mortgage payment as revenue stream for these vehicles.

5. There are different seniority level (called tranches) in a CDO. The most junior ones are called "first loss" and would take all the loss if some of the mortgages go default. The most senior tranche are protected from such loss, until all the tranches beneath are defaulted. Of course different tranches are priced differently based on the risk carried.

6. For the most senior tranche, usually investment bank would add Credit Default Swap (CDS) with a small payment per year to the CDS issuer. CDS is essentially an insurance policy which the issuer would pay the purchaser a large amount if the underlying product goes default. In return, the purchaser would pay a small premium periodically (like 2.5% for good debt) depending on the risk and likelihood of default of such protected asset. CDS is liquid and can be traded in Over-The-Counter OTC market which is not regulated or monitored by the SEC. Using their statistics model, the CDS issuer (AIG being one of the biggest) considered it to be a very rare event (black swan?) for the most senior tranche to go default, as all mortgages would have to go default in the entire CDO for the most senior tranche to go busted, the risk of such tranche is low, and thus lower CDS premium.

7. Combine the protection of CDS into the CDO and now these structured product would appear very safe and have a new credit rating. A lot of them got rated AAA (best) even though ultimately the asset are mostly subprime mortgages (BBB or lowest rating).

Risk is characterized by the likelihood of such event. If the company is very unlikely to go default, then it is said to have very low risk. It was assumed that it is very unlikely for all subprime mortgages to go default, and in addition also very unlikely for the CDS issuer (AIG) failing to pay back the policy purchaser. Thus presumably these CDO products have very low risk and thus have very high credit rating (AAA, as high as US Treasury Bonds). The problem was that these rare random events are NOT orthogonal (i.e. not uncorrelated). So if one goes, the others go with it in a chain reaction fashion, and trigger further snow ball effect in a downward spiral. Thus the probability of the most senior tranche going bust is no longer a multiple 6-sigma event. A 6-sigma event is loosely borrowing the low risk meaning of six sigma quality where it guarantees that there is a chance of 99.9999999% for the product to pass. Noted that such faulty assumption of orthogonality had already brought down Long Term Capital Management (LTCM, a hedge fund created by Nobel Laureates including the creator of the industry standard Black-Scholes model for option pricing) back in the 90s. Thus it is not entirely honest for the credit agencies to just blame on wrong assumption of their models. They would not, and should not have missed this.

Of course there were lots of profit taking involved (for instance the credit rating agency will get a handsome “registration transaction” fee if the product get a good rating, thus creating incentive to bless the product; in turn if the products have good rating the banks can sell them at a better price).

But that’s just CDO. The demand of these vehicles was so high (high yield and low risk, who doesn’t want it?), they actually went out of mortgages to be packaged as CDOs because there were only a limited number of physical American home buyers. Actual MBS and their CDOs were soon sold out to foreign investors. I am not joking. To satisfy demand, the investment banks created Synthetic CDOs which had no physical backing (i.e. does not have any attachment to anything in the real world) but were just bets against each other with the MBSs described above as reference (**), without actually owning any MBS. In contrast a CDO has real MBS asset backing.

The now famous Goldman Sachs Abascus 2007 (where they created a product so that John Paulson could bet against Goldman's own customers) was a special company (SPV) registered in Cayman Island constructed by these synthetic CDOs "referencing the performance" of a pool of MBS. In turn this vehicle was sliced up into tranches which investors can buy at different price and yield. You can find the details of this company in this Goldman Sachs presentation. Can you imagine being the accountant who filed tax for such “company”?

** An example is the weather future, where farmers can buy to hedge against the risk of their crop yield in case bad weather happens. Obviously you cannot claim or ask for delivery of good/bad weather in such a derivative. The future is just tracking weather as its reference. Weather or mother nature does not care or owe anything to either side of the future trade. It is like gambling which team will win in a soccer match. Actually a lot of ETFs are created this way, without owning any real stocks but just “tracking the performance of the underlying”. Following the same logic if panic sets in again, ETFs may go busted before the real stocks do as the counterparty can no longer fulfill their payment duty, regardless of what may happen to the real companies/market.

For a time CNBC hailed these sophisticated products as the latest great invention being “manufactured in Wall Street”. Anyway the investment communities as a whole were and still are drinking the same kool-aid. If there was no demand, there would be no supply. There were simply too much money out there seeking a place to go. If we are to do a witch hunt, the target would probably be regulator (SEC and Fed) and the credit rating agencies.

Technical Analysis verses Fundamental Analysis

One can view the market as a complex man-made system with evolving dynamics which are intertwined with each other. Unlike natural system however, attempt to model it is can be more difficult than existing systems in nature in the way that human is involved as part of the dynamics, and thus a seemingly successful model may soon be exploited and be factored in as another input dynamics, thereby altering the whole system.

There are unlimited ways to cut through the mustard, but in general we can categorize the majority ones into two: technical analysis and fundamental analysis. I would consider market emotion as part of TA. Some count it as the third one. Anyway here is what I think of these two type:


For a graph technician who works in the field of technical analysis, he does not need to know what the company, which the stock represents, does. In fact, it is better not to know, because the extra information will skew his judgment. In performing TA, the technician would mainly consider historic trade transaction data consisting of price movements and volume. And that's it. There are a lot of derived data created from these pair, the most famous being the candle sticks, moving averages and stochastic oscillators, depending on your taste, but fundamentally, these bells and whistles are all coming from these two simple entries: price and volume, on different horizon or time frame.

Now arming with these tools the technician can plot support and resistant levels, determining trend and then make his projection -- at this point this is really more art than concrete mechanics.

Comparing to TA, an analyst performing fundamental analysis has a lot more data to consider. Let me throw in the important ones: company past earnings, debt/liabilities, cash flow, market outlook, credit and the list goes on and on. To put a price on it, there are a few generally accepted models, like the discounted cash flow and the discounted dividend model, with the premise that assuming the company will exist forever, what is the present value of the company considering all the future revenue? There are many weak points in this assumption, but let me point out the one that academia likes to avoid: in calculating the discounted future value, how do you know what the discounted rate (most use the inflation rate) is? Usually they would resort to historic average, but is that it? Can't I just take the historic average of P/E ratio of S&P and assume the board market will drift back to this mean, assuming that these are cointegrated? Wouldn't life be much easier then, ha. But even assuming the rate will remain relatively constant, and that the company would not deviate from its past performance too much, this projected price is only the backbone of price estimation. Overloaded with all the information from earning reports, the analyst must decide and pick the important ones to calculate how much the company is worth based on his projection from the mountains of data. Popular metric includes Return On Equity (ROE), Economic Value Added (EVA) and Price per Earning ratio (P/E), comparing to the company's peers in the industry. These are all levers and knobs to adjust the projection. Unlike TA, these are actually based on sound (at least on the surface) financial theories to calculate how much a company is worth.

In the end as you may have figured, it is all approximation and guess work. I would not say this is logical guess work because you cannot prove any of the above really work. Let's not inflate and rape the rigor of logic.