Jan

3

About six weeks ago, I bought a new, high-end Win XP-based workstation, which came with RAID1-configured hard disks. It cost very little considering how cheap hard disks are these days, and it turned out to be a good investment.

If you don’t know what RAID is, it is sufficient to say that RAID 1 is a pair of hard disks with duplicate information. This is handled transparently. When one dies, you get notified, and the bad one is shut down.

Last week, one of the two died, and the remaining drive worked fine. It was plugged in a replacement drive, and the system rebuilt the duplicate information. The beauty of this is that you can continue to work while this rebuilding, which can last over the span of an hour or so, is occurring.

Since 250 GB drives cost < US$100, and most PCs built in the last 3 years support RAID on the motherboard with no other hardware investment required, it’s a no-brainer. If you get a new desktop or a tower PC, you should get RAID.

Here’s a cool SW item that came with this new machine: Diskeeper (diskeeper.com). This defragments your hard disk (transparently) while you’re working. It can also defrag your Master File Table (a bit technical).

It’s a really great product, and if you need performance while your machine is updating many files (say, like tick data), it really speeds things up esp. on a multiprocessor or a multicore PC. It’s cheap and it can range from $30-50 depending on the version.

A nice hardware goodie: Linkstation (Buffalo Technologies). We have a wired network at home/office with 5 PCs and a Mac. Backup is a pain. For about $200 (newegg.com) you can get a 320 GB NAS (Network Attached Storage) drive that sits on your network and appears as a shared drive for everyone. It’s pretty sophisticated for something that cheap: everyone can have their own private area and they provide a simple but adequate backup sw (memeo) that you can install for all your clients. I no longer have to remind my kids to back up, and my spouse loved it after the HDD crash nightmare last Sept: she forgot to back up her Outlook PST and contact files. Microsoft hides them very well.

Cool feature: if you have 2 of them they will auto-backup each other. Also cool: you can plug in any spare USB hard disks you have lying around or you can back up from the linkstation to one of those. I like it!

Jan

3

I am reading Michael Panik’s Advanced Statistics from an Elementary Point of View, and I would recommend it as a second book on statistics for someone who would like to carry his statistical education a little bit further, and who is not partial to such things as measure theory, partial differential equations, and differential manifolds — topics taught beyond the usual first year advanced calculus class. I find that reading such books keeps my mind active, and leaves me with something tangible after I have read about abysmal subjects such as redistribution, egalitarianism, energy saving, organic foods, and the more stringent regulation of hedge funds to cure the coming depression cited in the papers. It also leaves me with that tangible feeling, especially after coming from a very amateur performance of PDQ Bach where half the show consists of corny anti-Bush jokes, bathroom humor, and slap stick of the kind that kindergarten children respond to.

In the second chapter, Panik discusses how to compare distributions. Let’s start with the quartiles, Q1, Q2, Q3, and Q4, which divide a distribution into four equal parts.

Q1 is the point below which 25% of the observations lie, Q3 - Q1 is a measure of dispersion where the middle 50% lie, etc.. (Q3-Q1)/2 = QD is called the quartile deviation, SK = (Q3 -Q2)/(Q2 -Q1)/ Q3 - Q1 is a measure of skewness with the more positive values being right skewed. QD / (P90 - P10) = K is a measure of kurtosis where p90 is the 90th percentile (which 90% of observations are below).

For a sharp peaked curve of the kind that the dooms-dayists feel we have, K = approximately 0.5, and for a normal curve k = 0.25, and for a flat curve K = 0. It would be interesting to compute the 3 statistics for each day (QD, SK, and K) for the distribution of % changes in a given index like the S&P 500 or Dow Jones. We could then see if there are any predictive properties that are over and above the normal negative serial correlation from day to day, (which great books on behavioral finance still claim is not a real regularity that disproves efficient markets).

What a topsy turvy world.

Jan

3

To keep your New Year’s Resolution and maintain your new diet, don’t cut out the fat and sugar, but rather cut out the high-maintenance people in your life.

Are you hoping to keep your New Year’s Resolution and stay on a new diet or exercise regimen? If so, don’t spend too much time and effort on a list of what you should and shouldn’t eat or do.

Instead, do take out a sheet of paper and draw a vertical line down the middle. On the left side list, you should write down all the people that drain the life and energy out of you. Those are the people who are very difficult to please and easy to upset(a.k.a. high maintenance). On the right side of the list, write down the people who give you energy and are easy to please and difficult to upset (a.k.a. low maintenance).

Make a 30 day commitment to minimize your contact with people who drain you, and increase your contact with those who energize you. You should do this because most people fall off new diets or exercise routines (or for that matter light up a cigarette) after they have had contact with people who frustrate or exasperate them. When that happens, you’ll reach for a quick fix like carbohydrates or a thick piece of meat and the last thing you’ll want to do is go exercise.

Then make a commitment to continue this indefinitely.

Jan

3

2006 gains came essentially in the last six months of the year. The question is what happens in the first half of each year as a function of the last half of the prior year? SPY monthly closes since 1993 were used to regress the first six months against the last six months of the previous year:

Regression Analysis: First six months versus last six months

The regression equation is
1st 6mo. = 0.0318 + 0.542 last 6mo.

Predictor Coef SE Coef T P
Constant 0.0318 0.0323 0.98 0.346
last 6mo 0.5424 0.3443 1.58 0.143

S = 0.0995406 R-Sq = 18.4% R-Sq(adj) = 11.0%

The first six months tend to continue the pattern of the last six months, which is somewhat bullish for the present (though non-significant). The same test for the last three months and the first three months showed weaker results.

More to the point (and since it appears likely to start with a bang), for each January since 1993, what happens in the last 15 days as a function of the first five?

Regression Analysis: last 15 days versus first five days

The regression equation is
last 15d = 0.0129 - 0.747 1st 5d

Predictor Coef SE Coef T P
Constant 0.013 0.0114 1.13 0.281
1st 5d -0.747 0.5479 -1.36 0.200

S = 0.0357813 R-Sq = 14.4% R-Sq(adj) = 6.7%

Here there is a slight (non-significant) tendency for the last 15 days to reverse the first five.

Jan

2

I only found one market open yesterday, and it was Israel, which was up 1.5%. One was also wondering what kind of predictions one could make, a la the probability that the team that scores first in a basketball game will win the game, and if this is connected. Alan Abelson says that he sees 2007 shaping up as a year like 2000, and he feels the sense of Deja Vu. He has been saying this 2002, and from 1990 to 1999, when he felt a sense of Deja Vu referring to 1987. And from 1964 to 1987, he felt a sense reminding him of 1929. It is insightful to see the techniques of the perfect lie, or propaganda that he uses to maintain his self image, presumably rather than to deceive his readers. His two favorite techniques are to say that he didn’t really expect the year to play out as favorably as it did or that his crystal ball has not been entirely accurate, and his very sagacious short selling friends who have not done too badly see the problems of our economy as discounted to an inordinate extent.

Gone is the old technique of saying that the market will go down without limit until the last excess of ebullience is gone. He also doesn’t use the technique of the Elizabethan Ghost to indicate that it is wrong to be always bullish because the up trend has a variance. The problem is the uncertainty of knowing when the mean and variance of the drift have changed. Presumably if the drift were 50% a year, rather than 10,000% a century, people would be reluctant to say that their fears over-ride it, and that they would prefer to be short.

On another note, I have received numerous letters asking me if the tendency for years ‘07 to be bearish is predictive. I point out that with the last two ’07s being 1987 and 1997, with returns of 4% and 20% respectively, and 1967 being up 20%, one should not place any reliance on a pattern with 10 observations that hasn’t worked three of the last four times. The same would be said for all the foolishness about the January Barometer. It hasn’t worked for three out of the last five years, and was random before that.

I see many year end forecasters are looking for technology spending to be up some 20% or so next year. One wonders what the best way to play this might be, given the relatively lackluster performance of technology in last year.

Finally, I am convinced that training in checkers is much better for children than chess, in that it prepares them better for the basic yes, no decisions of life that make up much of logic, electronics, and computers. It is simpler with a less confined rule base, with much more potential for generalization to the situations of life. It also has the virtue of not consuming so much time to become proficient, (now that it takes a football team and extensive technology and years of study to become even a competent chess player). As well as being better to learn, it’s also much less life threatening to eat a checkers piece rather than a chess piece.

GM Nigel Davies responds:

I believe this should be tested, and probably on a larger sample than exists on the list.

I do have one observation to add, and that is that the chess players who went on to become very successful appeared to have one thing in common. They either stopped playing altogether or relegated the game to the status of a very minor hobby, which rather confirms the chair’s hypothesis that it takes up too much time.

Vis. a vis. choking hazards, I found this prevention.

Peter Grieve comments:

The chess vs. checkers comparison brings to mind fencing vs. boxing. I’d rather that a child of mine would be a boxer, but I’d much prefer to be a fencer.

The late GM Tony Miles wrote a piece extolling the virtues of checkers, and bemoaning that the fan base wasn’t larger.

Most of the great Georgian (British) boxers were fencers also. I think this was very useful cross-fertilization.

Jan

1

The median stock return (not counting dividends) among the S&P 500 was 11% for the year 2006. One quarter of the 500 stocks had returns above 25.0%, and one quarter had returns below 1.8%, giving a semi-interquartile range of 23.2%. This compares to a semi-interquartile range of 26.6% in 2005.

Year 2005 2006
% of Stocks with Returns greater than 25% 21% 25%
Median 2.2% 11.0%
Bottom 25% of Stocks Returns -5.6% 1.8%

Therefore it appears there was more cross-sectional variability in 2005 than 2006. The top 10 stocks in 2006 were:

Name of Company Symbol Return
Allegheny Tech ATI 152%
Terex TEX 118%
Nvidia NVDA 103%
OfficeMax OMX 96%
Big Lots BIG 91%
Celgene CELG 78%
DirecTV DTV 77%
BellSouth BLS 74%
Hercules HPC 71%
CB Richard Ellis CBG 69%

The top company, Allegheny Tech, now says it produces “specialty materials,” but years ago was known as a stainless steel maker. I see also that flat-rolled products account for 38% of their sales. I know the stock from my days as a finder, when it sold at 80% of book on average, whereas it now sells at 740% of book, after deficits in 2002-204, and zero-ish earnings in 2001-2002.Terex, the second top, produces off-road trucks. Except for Nvidia, which designs and markets three dimensional graphics processors, many of the top ten companies are in industries of the kind favored by the Sage, industries that produce relatively humdrum products, and that economic theory, based on the rate of profit for competitive industries with few barriers to entry, would not anticipate as creating too great a rate of return.

The ten worst companies were WFMI, APOL, ADC, YHOO, JBL, AMD, SNDK, EBAY, BSX, and NOVL, all down 30% to 40%. These are companies, with the exception of Whole Foods, that are research intensive, producing products that should have a high price to weight ratio, involving software or electronics.

In previous studies we have found that the ten worst-performing S&P 500 companies in a year perform much better in the next year than the ten best-performing companies. That study has to be updated and tested carefully however, to make sure that it is not a phenomenon of the cut-off, or of one or two outliers, or of great performance from low-priced shooting stars. Despite this, based on the preliminary review, I predict the ten worst stocks have a higher a priori expectation for the next year than the ten best.

Jan

1

For the S&P 500 index, December 2006 was the seventh consecutive up-month. Looking at monthly returns since 1950, there were ten occasions of up-streaks of seven or more months, (the second column is the number of consecutive up months):

Stem-and-leaf of streak N = 276
Leaf Unit = 0.10

117      000000000000000000000000000000000000000000000000000000000000000+
(63) 1   00000000000000000000000000000000000000000000000000000000000000
96   2   0000000000000000000000000000000000000000000000
50   3   0000000000000000000
31   4   000000000000
19   5   000
16   6   000000
10   7   000
7    8   0000
3    9   0
2    10
2    11  00

Returns for the month after seven ups was higher than for all months, but not significant:

Two-sample T for month if up seven vs. all monthly returns

  N Mean Standard Deviation Standard Error Mean
Month if up Seven 10 0.0160 0.0437 0.014
All Monthly Returns 683 0.0073 0.0408 0.0016

 

Month if up seven T=0.62

And in the current case, month eight will be January, which is historically a good month also (although again this is not significant):

Two-sample T for January vs. all monthly returns

  N Mean Standard Deviation Standard Error Mean
January 56 0.014 0.0480 0.0064
All Monthly Returns 683 0.0073 0.0408 0.0016

 

Finally, here is a regression of January with prior Decembers (since 1973), which shows a trend towards reversal (but is not significant):

Regression Analysis: January vs. the previous December

The regression equation is: January = 0.0221 - 0.303 previous December

  Coefficient Standard Error Coefficient T P
Constant 0.0221 0.0095 2.33 0.026
Previous December -0.3027 0.2631 -1.15 0.258

 

S = 0.0504452
R-Sq = 4.0%
R-Sq(adj) = 1.0%

Jan

1

I am in Tucson at the moment and was out doing some research for future Adventures in Retailing, when I dropped into a Circuit City store. Tucson appears to have a severe labor shortage and this manifests itself in a lower quality labor force. I was looking at a giant bank of HDTVs to get a closer look at what’s been driving the retail market of late, when I noticed that the grass on the field in whatever bowl game was being televised didn’t look like grass at all. It didn’t even look like grass in a good DVD transfer. It was green clumpy stuff.

The 12-year-old kid who was posing as a salesman came over to me and asked if I had any questions. I asked (politely, of course), “What is the source of the signal on all these TVs? You do know that grass doesn’t look anything like that?” There was actually a time when Tucson didn’t have grass, but now it does. Too much of it. He seemed perplexed, but answered, “DirecTV” and then got away from me as quickly as possible.

Quick web research indicates that DirecTV is facing legal action for their degraded signal, and Circuit City is down about 25% relative to Best Buy over the past year. The executive at Circuit City who decided to use DirecTV to feed his sets should be jailed as an economic criminal for probably knocking a good several bips off the GDP. The folks who actually know what grass looks like, went out and looked in the stores, and did the pairs trade (BBY vs. CC) months ago. And are now sitting fat and happy.

To boost the local economy, I bought a rather expensive CD that contains what I have decided is the ‘Second Greatest Song of All Time.’ I’ll be writing about that at some later date.

Circuit City aside, Tucson has got lots of great retailing. And I visited a Waffle House. None of the patrons had on any of the $360 men’s shirts that they were hawking at the new luxury shopping mall. The waffle house sucked, but (as always) Tucson has got amazing food and it is much cheaper than the fake Manhattan version of the same stuff.

P.S. I caught a few minutes of Cramer on CNBC in my hotel room. Not bad. Good stuff on limit orders. He also praised speculation while disembowelling a stuffed bird. I guess I’m late to that party, too.

Jan

1

Firstly, John Maynard Keynes in Chapter 12 of The General Theory of Employment, Interest and Money says that:

Worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally.

I found a reminder of this dictum last week, in fourth down decision-making in the NFL.

[A few years ago David Romer - an economist at UC Berkeley - posted a paper on-line examining the decisions NFL coaches made on fourth down. Romer’s paper found via an examination of expected benefits and costs that NFL coaches were not making optimal decisions. Specifically, NFL teams should be going for it more often on fourth down, rather than to punt or attempt field goals.

… So NFL coaches are told by Romer that it makes more sense to go for it more frequently on fourth down, and the response is that they go for it a bit less often. What explains this reaction?

…the reason why coaches fail to heed Romer’s wisdom is that coaches do not wish to undermine their reputation in the coaching fraternity …]

As my second point, you wrote in Education of a Speculator:

No one doubts that the public must lose at the races in order to pay for the prize money, stable care, employees, and upkeep of the land, building, and equipment … If they bet like other members of the public, they know they’ll lose.

And:

The questions on most multiple-choice standardized tests are arranged roughly according to the number of students who answer them correctly– allowing a casebook example of contrarian thinking. The answers to earlier (easy) questions will be obvious to the crowd (most students); the answers to those later in a section will always be unexpected. In other words, the answers to earlier questions will always be attractive, and those later in each section will always be unattractive.

In the last paragraph of a blog post on Thursday, Steven Levitt noted why in a current competition his optimal strategy is to deviate from the strategy of other participants:

…Knowing the top bettors all pick lots of home underdogs, it means that this week I have no choice but to bet against home underdogs. I need to make sure my picks are as different as possible from the picks of the people in front of me. I gain a lot in dollars if I do much better than the people in front of me. If I do much worse, it doesn’t cost me much.

Similar, behavior optimizes chances of winning an NCAA office pool. In NCAA office pools most people pick the heaviest favorite to win, overlooking how most others pursue the same strategy. The compound probability is what matters, namely the odds of that team winning the tournament as well as the odds of winning the pool when that team wins. Under these conditions, a contrarian approach is often warranted.

Jan

1

The article Please Give at the Office on page 18 of Monday’s Barron’s discusses an article by Baruch Lev, Christine Petrovits and Suresh Radhakrishnan, which found that “for every tax-deductible dollar the average corporation gives to charity, it should expect profits to rise by roughly $2-$3.” As one might expect, the connection between increasing sales and charitable giving is especially strong for consumer-oriented companies, presumably because of the good will effect.

…there is a lesson in all this for the way we perceive the behavior of people in markets. According to the “Chicago school” of economics, consumers seek to maximize their own utility according to narrow self-interest. By contrast, the less well-known “Austrian school” allows for the possibility that consumers might be motivated by broader social interests. If corporate charity improves human welfare, is it a cynical comment on capitalism that companies might seek profits by catering to those interests? Or is it faintly inspiring?

Jan

1

 I read a blog post this week that has some good examples of ever-changing cycles (and also displays the availability heuristic). The author of the post describes it as “perfectly rational” behavior to extrapolate from the recent past as a guess for the future.Former Fed. Chairman Alan Greenspan would also endorse the phrasing. In a May 2001 speech he said:

The longer an economy expands at a solid rate, the more people are likely to project that rate forward, eroding previous caution. This is a perfectly rational response. If people were accustomed to a three-year business cycle, they would exhibit far greater caution going into the third year of an expansion than if their normal experiences tended more to ten-year cycles [Read More]

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