Ian J. Wright and Dr. Keith Vorkink, Business Management

Investors, economists and financial analysts are constantly seeking to develop models that may help them anticipate what the stock market will do and how it functions. They do this so that they may take advantage of any market anomalies as soon as they appear and try to make “easy money.” Various pricing models (see Sharpe (1964), Litner (1965) and Fama and French (1993) for example1) have been and continue to be constructed that try to predict returns, and variables suspected to be correlated with returns have been included in these models in an attempt to determine specific predictors of returns.

It is quite natural to investigate the potential predictability that political variables such as the partisan composition of the United States Presidency give to returns in the stock market, due to the large role the government can play in the state of the economy. Various studies have attempted to uncover whether any statistically significant correlations between political variables and returns exist (for one example see Santa-Clara and Valkanov (2003)), with varying results. Some researchers claim no such significant relationships exist, while others claim that returns are higher during gridlock, others during Democratic administrations, and so forth. There has been no answer that has been generally accepted by the financial world.

Due to the conflicting results and lack of a generally accepted relationship between, in particular, the partisan composition of the U.S. Presidency and returns in the U.S. stock market, I decided to take a new approach in investigating this relationship in hopes of shedding additional light on the puzzle. Of the studies done above, most have employed a method of estimation called Ordinary Least Squares (OLS), or variations of it. In hopes of finding firm and robust results, I decided to use a method of estimation known as Generalized Method of Moments (GMM), which encompasses OLS as a special case, but, in general, is more broad in its capabilities and possibilities for use in estimation.

I obtained data for the project from the CRSP (Center for Research in Security Prices) database using the years 1946 to 2005, and chose models of estimation based on Newey (1988), Fama and French (1993), Santa-Clara and Valkanov (2003) and the suggestions of my mentor. These details are fully discussed in the completed work, which is available from the author upon request (as mentioned below).

My original plan was to only perform and obtain the results from GMM estimation, with no a priori hypothesis as to whether or not returns would be significantly affected by political variables, due to the conflicting voices presented in the recent literature. However, as I began to do GMM estimation I discovered that the choice of statistical software package I planned to use was an issue needing to be addressed, which I had not originally anticipated. I found that due to limitations of the pre-coded functions present in my initial statistical software package of choice (Stata), I could not follow my original plan, which was to implement the estimation procedure of Newey (1988) using this package. Further, as I sought to obtain other routines that had been programmed to implement GMM estimation, never was I able to get one to provide estimates as desired. As a result, the process became more involved and I was required to go into the “guts” of the construction of the GMM estimator given by Newey (1988) and write the functions that would perform the desired estimation from scratch in an alternate software package (Matlab). In terms of my learning and growth this turned out to be far more effective than having “the computer do all the work”, although it was more difficult along the way. Further, these issues led me to go ahead and perform OLS estimation and present and compare both sets of results in the completed work. OLS significance tests were also reported, although the GMM standard error estimates were not obtained. I give special thanks and acknowledgement to Bradley J. Larsen, class of 2008, Brigham Young University, for his assistance in the coding process.

Results showed that GMM estimates did not confirm OLS estimates of the presidential variable coefficients and no significantly consistent correlation between the partisan composition of the U.S. Presidency and returns in the U.S. stock market was shown to exist. However, GMM and OLS estimates of some of the control variables were consistent with one another, which was expected a priori, and further showed the weak predictive power of the presidential variables. Curious as to why I had obtained different results than others had previously, I spoke with one economics faculty member who told me, “Welcome to empirical work”, teaching that depending upon the data set, models and observations employed, varying results may be obtained. I also did some estimation using congressional variables, which is not reported here for sake of brevity.

Due to the lack of significant results, further work down this avenue will probably not be pursued. However, were it to be done, it is recommended that GMM estimates of the standard errors be obtained so that additional significance tests can be performed.

The complete 37-page work, entitled “Comparing Estimation of OLS and GMM in a Variation on the CAPM Model Involving Political Variables”, is available from the author upon request.

References

• A complete list of references is available from the author upon request. Those listed below are not comprehensive by any means, and can be considered, in some ways, the bare minimum.
• Fama, Eugene F. and French, Kenneth R., (1993), “Common risk factors in the returns on stocks and bonds,” Journal of Financial Economics, Vol. 33, No. 1, 3-56.
• Litner, John, (1965), “The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets,” Review of Economics and Statistics, Vol. 47, 13-37.
• Newey, Whitney K., (1988), “Adaptive Estimation of Regression Models Via Moment Restrictions,” Journal of Econometrics 38, 301-339.
• Santa-Clara, Pedro, and Valkanov, Rossen, (2003), “The Presidential Puzzle: Political Cycles and the Stock Market,” The Journal of Finance, Vol. 58, No. 5, 1841-1872.
• Sharpe, William F., (1964), “Capital asset prices: a theory of market equilibrium under conditions of risk,” The Journal of Finance, Vol. 19, 425-442.