Here Goldman Sachs settled down around 11 AM. Then between 2 and 3 PM it wandered off plummeting to the strike at the close.

BIDU just took off, but we didn’t think it would pin, did we?

Google is often a good pin candidate. Look at what it did today:

Apple likes to wander, but look how well behaved it was after 1 PM.

CME was quiet but not pinned. Look at the chart.

Hang Seng (Hong Kong): +2.09%

Australia: +3.41%

Nikkei 225 (Japan): +0.88%

Europe at mid-day:

FTSE 100 (UK): +1.58% at 11:15 AM

DAX (Germany): +1.77% at 12:15 PM

CAC 40 (France): +1.88% at 12:15 PM

Europe up nicely, perhaps on news of 14 month high in new car registrations.

SPY after hours: 1.0% at 6:21 AM

QQQQ after hours: 1.7%

Economic Indicators: June Consumer Price Index (CPI) comes out at 8:29 AM EST. CPI is half of a percent lower than last year.

The Empire Manufacturing report for June comes out at 8:29 AM too. It shows manufacturing in New York State and is expected to decline.

Industrial production is expected to show a June drop of 0.6% after Mya’s drop of 1.1% when announced at 9:14 AM EST. Capacity utilization is also expected to decline to 67.9% from 68.3%.

9:00 AM Abbott Labs (ABT), drug company, has Q2 earnings call.

9:15 AM Yum! Brands, Inc. (YUM), fast food restaurants, has Q2 earnings conference call.

10:00 AM Gannett (GCI), newspaper publisher, has Q2 earnings call.

11:50 AM Best Buy Co., Inc. (BBY), electronics retailer has a webcast from the Oppenheimer & Co. Consumer, Gaming, Lodging & Leisure Conference.

1:30 PM KLA-Tencor (KLAC), designs and manufactures equipment for the semiconductor industry, briefs analysts.

Australia: +1.90%

Nikkei 225 (Japan): +2.34%

Asia up as a knock=on effect of US Monday return?

FTSE 100 (UK): +0.56% at 10 AM

DAX (Germany): +0.77% at 11 AM

CAC 40 (France): +2.31% at 11 AM

Europe up moderately.

S&P 500 (Globex): 0.0% at 5:21 AM

Nasdaq (Globex): 0.1%

Economic Indicators: June retail sales comes out at 8:29 AM EST. Retail sales are 10% lower than last year. The consensus estimate is that June sales are +0.4% over May.

The Producer Price Index (PPI) for June comes out at 8:29 AM too. It is expected to show a 1% rise over May. Core PPI is up 3% year over year.

Business inventories is expected to show a 0.8% drop in June over May when announced at 9:59 AM EST. The inventory to sales ratio has shot up to 1.44 since sales have fallen by 15%, while inventories only by 5% year over year.

Goldman Sachs (GS) before the market opens, Intel (IITC) after the close and DELL has a call before the open, Johnson and Johnson (JNJ), and Yum! Brands(YUM) after the market closes, and Altera Corp (ALTR) after the close announce earnings.

Around 2 PM is a good time to look for pins in the above stocks, though AAPL and MA move around a lot.

Edge has many fascinating essays written by giants in their field, well worth browsing.

Nassim Taleb wrote Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets and The Black Swan: The Impact of the Highly Improbable.  These first two I have read and found very interesting.  The Edge essay excerpted here  summarizes some points from The Black Swan . Dynamic Hedging: Managing Vanilla and Exotic Options (Wiley Finance) was his first book on managing an options book.

1) Avoid Optimization, Learn to Love Redundancy. Psychologists tell us that getting rich does not bring happiness—if you spend it. But if you hide it under the mattress, you are less vulnerable to a black swan. Only fools (such as Banks) optimize, not realizing that a simple model error can blow through their capital (as it just did). In one day in August 2007, Goldman Sachs experienced 24 x the average daily transaction volume—would 29 times have blown up the system? The only weak point I know of financial markets is their ability to drive people & companies to “efficiency” (to please a stock analyst’s earnings target) against risks of extreme events.

Indeed some systems tend to optimize—therefore become more fragile. Electricity grids for example optimize to the point of not coping with unexpected surges—Albert-Lazlo Barabasi warned us of the possibility of a NYC blackout like the one we had in August 2003. Quite prophetic, the fellow. Yet energy supply kept getting more and more efficient since. Commodity prices can double on a short burst in demand (oil, copper, wheat) —we no longer have any slack. Almost everyone who talks about “flat earth” does not realize that it is overoptimized to the point of maximal vulnerability.

Biological systems—those that survived millions of years—include huge redundancies. Just consider why we like sexual encounters (so redundant to do it so often!). Historically populations tended to produced around 4-12 children to get to the historical average of ~2 survivors to adulthood.

Option-theoretic analysis: redundancy is like long an option. You certainly pay for it, but it may be necessary for survival.

By “optimize” I think Taleb means trying to squeeze all the return out a trade by using maximum leverage – a dangerous practice.

2) Avoid prediction of remote payoffs—though not necessarily ordinary ones. Payoffs from remote parts of the distribution are more difficult to predict than closer parts.

A general principle is that, while in the first three quadrants you can use the best model you can find, this is dangerous in the fourth quadrant: no model should be better than just any model.

How do you know if you are in the fourth quadrant? That is where you are subject to the appearance of Black Swans, rare events that can wipe you out. Since such an event may happen only once every ten years, you don’t know when it can strike. So Taleb never sells naked options. He is always a buyer or spreader.

3) Beware the “atypicality” of remote events. There is a sucker’s method called “scenario analysis” and “stress testing”—usually based on the past (or some “make sense” theory). Yet I show in the appendix how past shortfalls that do not predict subsequent shortfalls. Likewise, “prediction markets” are for fools. They might work for a binary election, but not in the Fourth Quadrant. Recall the very definition of events is complicated: success might mean one million in the bank …or five billions!

The real problem with the analysis of Long Term Capital Management was that they did not look far enough into history. Taleb argues that it doesn’t matter how far back you look, it will not tell you the limits of possible events in the future.

4) Time. It takes much, much longer for a times series in the Fourth Quadrant to reveal its property. At the worst, we don’t know how long. Yet compensation for bank executives is done on a short term window, causing a mismatch between observation window and necessary window. They get rich in spite of negative returns. But we can have a pretty clear idea if the “Black Swan” can hit on the left (losses) or on the right (profits).

The point can be used in climatic analysis. Things that have worked for a long time are preferable—they are more likely to have reached their ergodic states.

5) Beware Moral Hazard. Is optimal to make series of bonuses betting on hidden risks in the Fourth Quadrant, then blow up and write a thank you letter. Fannie Mae and Freddie Mac’s Chairmen will in all likelihood keep their previous bonuses (as in all previous cases) and even get close to 15 million of severance pay each.

This is a typical problem of misalignment of interests. Traders at hedge funds or prop desks have an incentive to take large risks since if they win, they get paid big; if they lose, they have to find another job, but the payoff is very asymmetric.

6) Metrics. Conventional metrics based on type 1 randomness don’t work. Words like “standard deviation” are not stable and does not measure anything in the Fourth Quadrant. So does “linear regression” (the errors are in the fourth quadrant), “Sharpe ratio”, Markowitz optimal portfolio, ANOVA shmnamova, Least square, etc. Literally anything mechanistically pulled out of a statistical textbook.

My problem is that people can both accept the role of rare events, agree with me, and still use these metrics, which is leading me to test if this is a psychological disorder.

The technical appendix shows why these metrics fail: they are based on “variance”/”standard deviation” and terms invented years ago when we had no computers. One way I can prove that anything linked to standard deviation is a facade of knowledge: There is a measure called Kurtosis that indicates departure from “Normality”. It is very, very unstable and marred with huge sampling error: 70-90% of the Kurtosis in Oil, SP500, Silver, UK interest rates, Nikkei, US deposit rates, sugar, and the dollar/yet currency rate come from 1 day in the past 40 years, reminiscent of figure 3. This means that no sample will ever deliver the true variance. It also tells us anyone using “variance” or “standard deviation” (or worse making models that make us take decisions based on it) in the fourth quadrant is incompetent.

Kurtosis measure how fat the tails of a probability distribution are. That is, how many large moves does oil or US dollar or a stock make. That 70 – 90% of the kurtosis comes from one day in the past forty years is fascinating. Does it mean that it can be ignored? That the normal distribution is actually better than we thought? Taleb would scream: NO!

7) Where is the skewness? Clearly the Fourth Quadrant can present left or right skewness. If we suspect right-skewness, the true mean is more likely to be underestimated by measurement of past realizations, and the total potential is likewise poorly gauged. A biotech company (usually) faces positive uncertainty, a bank faces almost exclusively negative shocks. I call that in my new project “concave” or “convex” to model error.

Do not confuse absence of volatility with absence of risks. Recall how conventional metrics of using volatility as an indicator of stability has fooled Bernanke—as well as the banking system.

9) Beware presentations of risk numbers. Not only we have mathematical problems, but risk perception is subjected to framing issues that are acute in the Fourth Quadrant. Dan Goldstein and I are running a program of experiments in the psychology of uncertainty and finding that the perception of rare events is subjected to severe framing distortions: people are aggressive with risks that hit them “once every thirty years” but not if they are told that the risk happens with a “3% a year” occurrence. Furthermore it appears that risk representations are not neutral: they cause risk taking even when they are known to be unreliable.

It does sound more frequent to say “3% per year” rather than “once very thirty years”. I had to do the calculation to see that it was the same. P = 3% / year = .03 / 252 = 3 / 25,200 = 3 times in 10 years.

The Goldman Sachs researchers go on to say that 75% of the “Standard & Poor’s Index stocks have withdrawn guidance”. It doesn’t say if that is the S&P 100 or S&P 500. So if the company withdraws guidance, analysts have to make up their own minds what earnings are going to be. That would increase uncertainty. Furthermore, if the analysts are revising their estimates very often, that shows that they are very uncertain about earnings.

The author, Steven Sears, makes it easy to test this hypothesis this quarter since he has listed their recommendations. These companies have said their earnings will be better than expected: Marvel Technologies (MRVL), Mylan (MYL), Best Buy (BBY), Symantec (SYMC), Altria (MO), Northern Trust (NRTS), AutoZone (AZO), and TJX. He goes on to say that one should buy calls on these companies before the earnings come out.

First Two Data Points

Northern Trust announced earnings yesterday before the market opened. On Friday, NTRS closed at 63.06, on Monday it closed at 58.15 and Tuesday it opened at 51.79 went as high as 56.23 and as low as 49.78 and closed at 56.17. That is down $2 from Monday’s close so buying options Monday and selling Tuesday would have been a losing trade. The implied volatility dropped also after earnings were announced.

Altria (MO) announced earnings this morning. It’s trading range so far is 16.46 – 16.84. Not volatile enough to profit from a straddle bought last night.

Two trades doesn’t negate the argument. This is just the first two data points.

Buy Straddles

He also suggests buying straddles on those companies that “have retracted earnings guidance”. Included in this list are Molex (MOLX), Johnson Controls (JCI), Applied Materials (AMAT), Abercrombie & Fitch (ANF), Legget & Platt (LEG), Fortune Brands (FO), Coventry Healthcare (CVH), BJ Services (BJS), and Union Pacific (UNP).

Buy Puts

The last group of stocks he discusses are of companies that have lowered earnings guidance. For those he suggests buying puts. This certainly makes sense if you own the stock. It it would be profitable as an option trading strategy, we will have to see. This group of stocks contains: Texas Instruments (TXN), Ingersoll-Rand (IR), Eaton (ETN), International Paper (IP), National Semiconductor (NSM), Sandisk (SNDK), Illinois Tool Works (ITW), Massey Energy (MEE), Parker Hannifin (PH), Estee Lauder (EL), Time Warner (TWX), CSX, and Norfolk Southern (NSC).

Texas Instruments

Texas Instruments announced earnings Monday evening. At the close of the regular trading session Monday, a share of TXN cost 17.32. Tuesday morning, it opened at 17.65. So if you had bought a 17 strike put near the close Monday, you would have been disappointed Tuesday morning. It traded yesterday between 16.61 and 17.85. So you would have been profitable at some point during the day. But that is a day trade not an earnings trade.

So far, Sears and Goldman Sachs is 0 for 3.

Goldman Sachs

Goldman Sachs looked like it would be too volatile to stay within the break even points of the 120 straddle. In fact, from 10:30 on, it traded between 120.30 and 122.96, closing at 120.42. Pretty tame, but because the Monte Carlo gave it a high probability of wandering out of range I didn’t trade it.

Apple

Apple had the best chance of staying within range of a straddle sale. It opened below 120 and went up from there. Ending the day at 123.40. I didn’t wait for it to stabilize, sold some straddles and spent the rest of the morning buying them back at a loss.

Research in Motion

Research in Motion opened between strikes and stayed between 66.75 and 69.23 all day. It closed at 68.29

First Solar

First Solar had a wild ride. Its low of the day was 142.61 and its high was 147.25. It passed through the 145 strike without noticing it.

Mastercard

Mastercard was pretty flat from 11:50 to 2:09 trading between 160 and 161. Then it shot up to nearly 165. MA closed at 162.57 I was tempted to sell a straddle, but I was glad that I didn’t.

Google

Google opened near 385 and then took off to near 400. After 11 AM it looked like it would stay within the range of 390 and 400. So I sold the 400 Call and 390 Put strangle. After 1:00 PM it looked like Google was trading very close to 391 – 392. So I sold the 390 straddle. It worked out alright, but Google wasn’t really pinned, it just stayed in the range. The most it moved was to 394, but never dropped below 390.

At the end of the day, Google closed at 392.30.

This is what I’m looking at for tomorrow.

Pinning Candidates

It looks like the top two, Goldman Sachs and Apple, are prime candidates to be pinned at 120 tomorrow. Goldman announced earnings early this week, so there shouldn’t be a lot of news out tomorrow to move the stock. That is, unless Citibank’s earnings announcement tomoorw morning shakes up all the financial stocks.

Google

Google is all over the place in after hours trading after its earnings announcement.  Who knows if it will stabilize tomorrow morning near a strike.  The open interest is wide and deep for Google options.  So we will have to see at 10AM.  It might happen that Google will have momentum tomorrow so that a more directional bet would make sense.  If it is moving up, then one could buy a call just above where it opens and sell two or three calls at the next higher strike to pay for it and make the trade close to delta neutral.

Rest of the List 

Research in Motion (RIMM), First Solar (FSLR), and Mastercard (MA) are good candidates too.  The others on the list, Autozone (AZO), Baidu (BIDU), and Chicago Mercantile Exchange (CME) don’t have very many puts. So they aren’t very good candidates for pinning.

Monte Carlo Results

What is the probability of a successful trade? Which trade is the most likely to succeed? To answer these questions, I ran the Monte Carlo simulation for each straddle. The inputs are the stock price, the straddle price, the number of trading days left, the interest rate, and the historical volatility of the stock. The output is the probability that the stock wanders past the break even point on either side.

Some of these look better than others.   The best one is Google, only a one percent chance that it will wander outside the break even points.  Of course, this isn’t correct.  The volatility of the straddle will collapse tomorrow morning making the break even points closer together.  We will have to recalculate that one in the morning.

Notice how different GS and AAPL are.  Though they are at the same stock price as can be seen in the top table, and the at the money straddles are the same value, the trades are very different.  It is because Goldman Sachs has a much higher historical volatility.   

I believe that the probabilities are upper bounds, because of the extra pinning effect of the options on either sides of the strikes.  Still, I would sell more straddles of the stock that had the lower probability.