Daily Speculations 

The Chairman
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12/05/2005
Five Variations on the Economics of Location
Dr. Alex Castaldo adds:
Performance of stock indexes for 6 lowtax states:
SPX Start StartDate Cur CurDate PrcChg PrcChg Arizona 100 12/31/1994 514.94 12/5/2005 414.94% 175.55% Nevada Not available North Carolina 100 1/2/2002 125.70 12/5/2005 25.70% 9.56% South Carolina 100 12/31/2001 148.33 12/5/2005 48.33% 10.19% Florida 100 12/31/1994 131.82 12/5/2005 31.82% 175.55% Texas 100 12/31/1994 347.52 12/5/2005 247.52% 175.55%
Four out of the five available indexes outperformed the S&P over the same time period.
Data from Bloomberg LP.Kim Zussman speculates:
Andrew Moe notes:
One problem is that the K function is based on circular measures. The benchmark is K(t) = pi * (distance)^2. We deal in "flatland" by comparison, as our ranges only go up and down.
But an adaptation may be to count how many points (say closes) occur within n points of a given trigger day. For example, how many closes occur within 10 points of a new 20 day low? Or how many within 10 of a round number. Then compare that to random. Lots of variations here.
Another adaptation would be to take expiration into account. Instead of saying how many cases within n days, you keep on counting instances until x number of days have passed since price has been in the study range. I have this nagging feeling that somehow this count should supplant pi in the K function, but no real clarity on the matter yet.
Jim Sogi comments:
"Universal Geometry of Circles", Chapter 11, gives the formula to compute the diameter and chords of circles in quadrance (distance squared) thus avoiding use of pi. as well as the spreads. The use of the xy coordinate would be apt for charts of prices and time in respect to the circles and clusters of spatial geometry. The quandrances can be analyzed statistically for significance of clustering as in Ripley's K, but with simplified computations. Not sure if this refers to chart formations or actual geography, but would work on lat=long as Cartesian.
Kim Zussman comments:
The cost of transport would seem to relate to historical location of centers of commerce adjacent to shipping: ocean ports, lake ports, and rivers. Later, this was supplanted somewhat by railroads, and eventually highway and airport hubs. Locations of transport for industrial goods attracted manufacturing and trading enterprises, which brought opportunities and jobs so populations swelled.
Most of the great cities are on advantageous harbors, but this is based on heavy industry. The recent trend is on informational commerce, which does not dictate particular geography and can be accomplished, with the aid of low cost mail and shipping, more remotely than in the past. Thus a trend toward many industries locating away from port cities, to areas attractive for employee family life (examples include Google, Microsoft, and Amgen).
One investment thesis is that the gradient between coastal port city real estate prices and that in suburbs/rural areas should decline over long periods. Of course cities serve numerous other social purposes, including facilitation of s#xual commerce, which will likely preserve much of the premium.
What about prior inverseradial advantages of the denizens of Wall Street? Whereas in prior eras there were great informational advantages to physical association with the machine, now statarbs everywhere have easy access to market data. The only barriers now are delocalized desire, the ability to find discrepancies, and willingness to take your chances and test your strength against all the other maniacs. Nature abhors a gradient.
Dr. Alex Castaldo adds:
Additional details are found in:
Economic clustering and urban form: The case of Hamilton, Ontario
{We want] to consider the firm population of a [geographic] sector and to test for firm clustering, considering complete spatial randomness (CSR) as the alternative hypothesis. Using as input the location coordinates of the firms, the method commonly used for such analysis is to estimate an empirical univariate Kfunction. Intuitively this function provides the number of firms within a given distance of a randomly selected firm. The estimated K function is then compared to a set of simulated K functions for the same number of firms that are constructed under the CSR assumption. The test is simple in its conception but computationally intensive. Details for the estimation of the K function are provided in Bailey and Gatrell (1995) and Cuthbert and Anderson (2002).
While the univariate K function provides an explicit test for the spatial dependence among firms of the same type, it is not useful in studying the colocation of firms that belong to different sectors. An extension of the univariate K function is the bivariate function, which tests for interdependence in clustering between two patterns in space. The function provides the expected number of points of one type from a randomly selected point of the other type within a specified distance. For a more detailed description see Bailey and Gatrell (1995)
Sushil Kedia adds:
Investing involves transporting expectations and risks of the future to the present moment.
The rent is the cost of investing. Cost of invested capital, or the opportunity cost of the invested capital, is the rent. In any economic enterprise, so long as the cost of the enterprise is less than the incentives of the enterprise it remains viable. Opportunity cost of investing is the amount of risk one undertakes in the venture of a particular investment.
Thus the analogous economics such as Von Thunen's Economic Location Theory brings about layers/concentric circles of risk (volatility) organized farther and farther consistent with an optimality with the cost of investing, which could be a function of the cost of transacting (commissions, slippage, impact cost, average contango etc. etc.) the investment.
High transportation cost /quickly perishable tradegenerating highfrequency trading programs, or the day traders, are closest to the city centre.
The viewbased countingoriented speculators' bets of a small time frame  a few days  are next in the circle.
Mediumterm 'trendseeking' specinvestors' bets form the next circle.
The lowest cost, lowest volumeproducing investors' bets are farthest in the circle.
The optimal rent of a particular layer would be deciphered from time to time by a function of the amount of risk perceived and the amount of drift/move in prices that one expects to capture from a particular pool/shoal of stocks. Say for example, if the optimal rent estimates were highest for the coal producing companies during the FWW or if it was the steel companies near the SWW or if it was the Software/Tech producing firms towards 1999Y2K and the Oil companies in 20022005 it is essentially a similar reflection as the Economic Theory of Location of Von Thunen. Sectors getting the highest rent (allocations/attention), not necessarily highest rewards is but a function of the perceived maximum optimal point of cost of investing and expected returns. By a similar set of tautomerisation it is possible to see that sector rotations over shorter time frames are also running on the theory of Economic Location.
Dave Whitesel comments:
Two interesting modernday examples where this theory is applied is Blockbuster and Starbucks. Blockbuster seems to be a magnet for Starbucks; where you see one you will often find the other within line of sight. Recently "The Undercover Economist" did a study on Starbucks with respect to rents. Interesting study though not very complete. The primary goal of these placements is surrounding ingress and egress points to specific populations. Therefore you might find two Blockbusters or Starbucks very close together. These placements are not random; they are targeting known paths of trafffic, with a goal of surrounding destinations.
Rob Fotheringham remembers:
About ten years ago I was working for a retail auto parts chain (about 650 retail stores in the Western U.S.), that had a real estate department dedicated to finding and securing optimal store sites. They studied demographics and traffic patterns and other indicators but could not predict successful locations better than their competitors could (i.e. AutoZone). So they did the logical thing and opened up a store right across the street from a newly opened, very large AutoZone, and it quickly became their highest volume store. Imitative behavior in this regard seems common. For example, a couple of years ago, while trying to attract a grocery store chain to our small city, we were told that by several major chains that we did not have sufficient population density to justify even a small store (3540k square feet). Soon thereafter WalMart declared their intent to build a superstore on the same site, whereupon Home Depot inquired about the land across the street, and the major grocery chains developed interest.