Date of Award

Summer 2011

Document Type


Degree Name

Doctor of Philosophy (PhD)


Business Administration-Finance

Committee Director

Licheng Sun

Committee Member

Larry Filer

Committee Member

Kenneth Yung


This dissertation uses macroeconomic variables. In the first essay I use macroeconomic variables to determine if these variables affect the market's returns and volatilities, and in the second essay I examine whether the 11-month returns can be explained by these variables.

Using macroeconomic variables and forecasts of these variables on a quarterly basis from the Survey of Professional Forecasters, I first develop macroeconomic surprise variables. The macroeconomic surprise variables are then modified by dispersion of forecasts to adjust for surprises from uncertainty. Dispersion adjusted forecast surprises have not been used extensively in the literature. I also use a monetary shock variable. The market index I look at is the S&P 500. Among the results obtained from OLS regressions are that S&P 500 returns are influenced by inflation surprises and S&P 500 volatilities are influenced by industrial production surprises. Based on extant theory, macroeconomic variables are supposed to influence asset prices. This paper contributes to identifying variables that previously were not seen as responsible for affecting asset markets.

Macroeconomic variables are also used to study the 11-month returns in the other January effect. The other January effect was folklore up until Cooper, McConnell Ovtchinnikov (2006) confirmed it with statistical evidence. There has been a lot of controversy about the other January effect. Essentially, if the return in January of a particular year is positive (negative) the next 11-month return will be positive (negative). I show that a simple macroeconomic variable such as term premium which is the difference of long term interest rate and short term interest rate can be equally effective as a predictor for the next 11-months of returns. In addition to replicating the original study on a different time period, I show that the other January effect is not as predictive as expected, the results show that the other January effect is driven by negative Januarys and that positive Januarys do not predict returns. In addition conditioning, the January returns on various macroeconomic variables shows that the January effect works for the market value weighted index and not the market equal weighted index.


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