Excess return predictability in the U.S : where does it come from?
|Author:||Nkamanyi Martin, Chatutie1|
1University of Oulu, Oulu Business School, Department of Finance, Finance
|Online Access:||PDF Full Text (PDF, 0.8 MB)|
|Persistent link:|| http://urn.fi/URN:NBN:fi:oulu-202006182507
Oulu : C. Nkamanyi Martin,
|Publish Date:|| 2020-06-22
|Thesis type:||Master's thesis
This thesis explores predictor variables and returns predictability in the U.S. market from 2001.M7–2018.M12. The aim is to investigate whether price-dividend ratio, earnings price ratio, variance risk premium, term spread, long-term rate of return and inflation rate explains one-month in advance excess return in the U.S during the estimation period. Predictability results are limited over a shorter horizon to avoid some complexity that comes with higher estimation frequencies. The findings in this thesis are restricted to the in-sample test and sub-period analysis. The methodology employed uses a linear factor pricing model, where these six variables are tested to justify the excess stock return.
The primary research findings focus on the second split sample regression results, which does not include data from the 2008 and 2009 global financial crisis. It presents earnings price ratio, price-dividend ratio, and variance risk premium as one-month ahead predictor variables of excess return. The regression coefficients of these variables are statistically significant and robust in predicting excess stock return in the U.S. Among these variables, variance risk premium is the most robust predictor variable across sub-periods and models. Model 4, which is based on step-wise regression, shows that price-dividend ratio and variance risk premium account for 23% of variations of excess stock returns. Empirical results for term spread, long-term rate of return, and inflation rate show poor performance. Their regression coefficients are always statistically not significant in both the full and split-sample regressions.
Because the findings in this work lack out-of-sample test support, it appears to be a weakness. It is, therefore, not possible to generalize these findings. Therefore, it encourages further work to determine whether to employ these variables in the U.S. and other regional markets as predictor variables.
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