Work detail

Using Model Averaging Techniques to Examine Determinants of Stock Returns

Author: Mgr. Miriama Tóthová,
Year: 2018 - summer
Leaders: doc. PhDr. Zuzana Havránková Ph.D.
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 95
Awards and prizes:
Link: https://is.cuni.cz/webapps/zzp/detail/189404/
Abstract: The predictability of stock returns has been a widely discussed topic in the financial
literature. In the presented thesis, we examine the effect of 20 possible
predictors on S&P 500 excess returns in the time period from June 1998 till
December 2016. However, traditional models examining stock returns usually
ignore the issue of model uncertainty. In order to explicitly incorporate uncertainty
about the model into the analysis, we employ two model averaging techniques,
in particular Bayesian model averaging (BMA) and frequentist model
averaging (FMA). As a robustness check we use three different combinations of
priors within BMA framework. We assess the quality of their predictions and
compare the results with the traditional methods based on model selection criteria.
We find out that among the most important variables explaining excess
returns on S&P 500 stock index are three-month Treasury bill rate, dividend
yield, term premium, payout ratio, excess returns lagged twice, and default risk
premium. These are robust across all models we have estimated. Although frequentist
model averaging provides in-sample predictions superior to BMA as
the literature suggests and it also performs better than models selected according
to popular statistical criteria, it fails to outperform the Bayesian model
averaging out-of-sample. Moreover, among the three BMA specifications, the
data-dependent hyper-g prior performs the best in-sample and out-of-sample.

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