Max Drawdown

When it comes down to it, finance is about risk and return. One of the purest measures of risk is max drawdown. This is pretty much the worst-case scenario: one bought at a peak (not necessarily the absolute peak) and a trough (not necessarily the absolute trough) such that the drawdown is maximum, i.e., you have maximum loss. I was curious, if one takes a look at the S&P 500 components from max drawdown only, what does it look like? The time period for this simple study spans 5 years from today, February 28, 2013. Choosing a 5 year period does unfortunately eliminate 25 of the S&P 500 right off the bat due to those symbols not having data running that far back.

S&P component	Max drawdown (5 yrs)	Std Dev (daily return)
AZO	0.131292	0.179530
MCD	0.154924	0.141842
GWW	0.191437	0.190390
ABC	0.196378	0.275510
SRCL	0.214233	0.210143
CERN	0.217581	0.338044
WEC	0.232936	0.133349
DTV	0.235117	0.235420
XEL	0.242985	0.103072
FDO	0.246084	0.184564

The above table shows the 10 components with the least max drawdown over the period. An interesting variety of businesses are represented here. It isn't just a list of defensive utilities or consumer staples. We have automotive parts retail, the Golden Arches, industrial equipment, a pharmaceutical, a pharmaceutical waste disposal, healthcare IT, two utilities, and a discount retailer. Note that although these names tend to be low beta, they are hardly low PE. Note that having the least max drawdown doesn't necessarily imply having the smallest standard deviation of daily returns.

S&P component	Max drawdown (5 yrs)
BAC	0.921133
XL	0.925845
GCI	0.932099
TXT	0.940085
HIG	0.950989
FITB	0.955382
C	0.960199
FSLR	0.962171
GNW	0.964527
AIG	0.992874

This second table shows the 10 components with the greatest max drawdowns. This list is certainly more concentrated than the first table. All except for 3 are financials/insurers/reinsurers. We have the old guard news media represented by Gannett (GCI), aerospace/defense by Textron (TXT), and renewable energy by First Solar (FSLR). If risk averseness is the game, then drawdowns is quite an interesting measure. It shows the worst-case for a single roundtrip trade. Note that trading results may be much, much worse than the max drawdown if a strategy gets repeatedly drawn down in a losing streak. Nevertheless, max drawdown gives some insight into the amount of risk we are taking on. In particular, max drawdown should give some insight into what kind of reserves should be kept. This, of course, isn't foolproof since past drawdowns do not necessarily predict future drawdowns.

Another interesting aspect of max drawdown is that there is really no relationship between max drawdown and daily returns. A regression between the two shows an R² of a mere 0.0027.

Railroads and Homes: What Railroad Stock is Most Correlated with Homebuilders

With homebuilders slowly, slowly on the mend, I was curious just what the satellite beneficiaries of any lasting recovery in homebuilding are. There are the obvious ones such as appliance makers (Whirlpool, etc.) and home improvement stores (Lowes and Home Depot). It was a shot in the dark but I wanted to see if there was anyone in transportation that was correlated with homebuilders. As a starting point, I considered railroads, which haul commodities and raw materials across the country. Calculating the correlations between leading homebuilders (TOL, DHI, PHM, LEN) and railroads (CSX, KSU, NSC, UNP) over the past 10-year span. It turns out if there is a connection, it is not at all clear. For TOL, the luxury homebuilder operating on both coasts, the correlation coefficient is very, very low and may either be positive or negative. Lennar, Pulte, and DH Horton appear to have a significant negative correlation to all four railroads.

The strongest correlation is between KSU and TOL. For NSC and TOL, note that about 1/10th of Norfolk's revenues come from hauling metals and construction materials including brick and cement.

"CSX", "KSU", "NSC", "UNP", "LEN", "PHM", "DHI", "TOL"
[[ 1.        ,  0.9227771 ,  0.96305861,  0.95654626, -0.51462037,
        -0.60367747, -0.36129913, -0.1042525 ],
       [ 0.9227771 ,  1.        ,  0.93120173,  0.96344464, -0.28884962,
        -0.44944239, -0.16605286,  0.05288716],
       [ 0.96305861,  0.93120173,  1.        ,  0.93354555, -0.38596351,
        -0.45196275, -0.20670427,  0.04849845],
       [ 0.95654626,  0.96344464,  0.93354555,  1.        , -0.44989026,
        -0.59985824, -0.3203473 , -0.08247585],
       [-0.51462037, -0.28884962, -0.38596351, -0.44989026,  1.        ,
         0.92695554,  0.93860533,  0.73458592],
       [-0.60367747, -0.44944239, -0.45196275, -0.59985824,  0.92695554,
         1.        ,  0.93068006,  0.77841425],
       [-0.36129913, -0.16605286, -0.20670427, -0.3203473 ,  0.93860533,
         0.93068006,  1.        ,  0.88519053],
       [-0.1042525 ,  0.05288716,  0.04849845, -0.08247585,  0.73458592,
         0.77841425,  0.88519053,  1.        ]])

Financials of a University System

Universities are interesting creatures financially speaking. They are relevant because they often float general obligation and revenue bonds to finance themselves. These bonds are typically tax exempt and are part of the greater municipal bond universe. As an asset, they are infrequently traded. As a business, they derive a considerable amount of revenues from their medical center operations, government research contracting, government appropriations, and finally tuition. Consider the University of California system, the largest of its kind 302k students and 136k faculty spread across 10 campuses.

The lion's share of the revenues come from the medical centers and government research grants/contracts. Actual state appropriation funding and tuitions contribute only a quarter of the total revenue.

Over the past 5 years, the tuition/fees category grew by about 61%. Medical center and government contracts revenue grew by 38% and 26% respectively. State appropriations and DOE lab contributions actually declined by 6% and 55% respectively.

The chief risk in to the business is the defined-benefit pension program (amounting to a $2B/year payout). The number of retirees receiving payments may have only increased by 13% over the past 5 years, but the amount of payments has increased by 25.6% over the same period. Two years (2008 and 2009) of massive depreciation of the assets in the plan certainly did not help. Over the same period, the income part of the plan investments (interest and dividends) declined by 41% at least partially due to the low-interest rate environment of recent years.

The California state government budget crisis led to some deferred payments to the university system in 2010-2012, amounting to around $500MM each year.