Posted by Glenn Alpert
In an effort to better understand
drivers behind the stock prices of major defense contractors, I undertook a
study correlating their stock prices with major economic indicators.IHS Jane's A&D Analyst Matthew Bell (09/2011)
These companies included Lockheed Martin, General Dynamics, Northrop Grumman, and Raytheon. Since 1990, Lockheed merged with Martin Marietta to become Lockheed Martin, and Northrop merged with Grumman to become Northrop Grumman. When searching for historical stock prices for these two companies, I was able to find records dating back to 1990, but I assume that the stock price which predates the mergers should reflect the stock price of the acquiring company.
Stock Price = b0 + b1DEF + b2JIPI
+ b3JM3 + b4MS + b5W+ μ
The dependent variable represents
the stock price of a given Defense company at the beginning of January for a
given year.
DEF – Defense spending (yearly
budget) – b1
JIPI – January Industrial
Production Index for defense capital goods – b2
JM3 – January M3 Survey for new
orders of defense capital goods – b3
MS – Military sales to foreign
countries – b4
W – War – b5
ANALYSIS OF RESULTS
GENERAL
DYNAMICS
There is not much change in the
stock price during war or peace (although Tolstoy would be a good read). One
could infer that GD makes more “support” equipment and maintenance services,
rather products or services consumed or used exclusively during conflicts.
LOCKHEED
MARTIN
During wartime, [8.6(W)], Lockheed’s
stock can be expected to rise significantly.
During peacetime, [-.593(DEF)],
the government will contribute fewer dollars to Lockheed’s earnings (in other
words, the government will buy less from Lockheed). U.S. Foreign Military Sales are
key to Lockheed’s bottom line.
NORTHROP
GRUMMAN
War [23.25(W)] has a massive
impact on Northrop Grumman’s stock price.
Northrop Grumman does not appear to run its own large scale manufacturing operations [-2.3(JIPI)]. During peacetime, Northrop Grumman concentrates more on Foreign Military Sales [.001(MS)] than wartime [.0005(MS)]. Another way of looking at this is that during a period of time where the US government is buying NG products, it shifts its business development efforts overseas.
Northrop Grumman does not appear to run its own large scale manufacturing operations [-2.3(JIPI)]. During peacetime, Northrop Grumman concentrates more on Foreign Military Sales [.001(MS)] than wartime [.0005(MS)]. Another way of looking at this is that during a period of time where the US government is buying NG products, it shifts its business development efforts overseas.
RAYTHEON
War actually decreases demand for
Raytheon products [-4.3(W)] (it would make more sense to build and install
massive radar and satellite systems before you need them rather than in the
middle of a conflict). Raytheon runs its own robust
manufacturing operations [2.88(JIPI)].
CONSIDERATIONS
There are some considerations to
keep in mind regarding the results of these regressions. First of all, I highly suspect
that the “Independent variables” are not completely independent of each other.
The overlap would cause some distortion in the equation results.
Secondly, the stock price data was
for the first recorded date in January of the given year, and the M3 and IPI
data was end-of-month data for January. In retrospect, it may have been a good
idea to use IPI and M3 data from December of the previous year, but the change
should not be drastic, since manufacturing trends tend to increase or decrease
gradually from month to month the majority of the time.
Thirdly, the Defense budget data calculated
on a yearly basis, so this could skew the correlation. Monthly DoD spending
data would add accuracy to the equations.
In addition, another idea for
this project was to test the data for the recession years to see if there was a
significant impact on the other variables. The data includes three recession
years, but it would be difficult to test recession vs. non-recession years with
only three data points (‘08, ‘09, and ’10). One would to leave out Defense
spending from the equation, then just measure manufacturing data (stock price =
Bo + IPI + M3)
This exercise provided additional
insight into the stock price of US defense companies. While the data did
provide some insight, we should remember that other factors affect stock
prices, and this is reflected by R-squared figures ranging from the mid-50% to
mid-90% range. Of course, further study is necessary.
No comments:
Post a Comment