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Trading volume and expected stock returns: a meta-analysis
Bajzík, Josef ; Havránek, Tomáš (advisor) ; Červinka, Michal (referee)
I investigate the relationship between expected stock returns and trading volume. I collect together 522 estimates from 46 studies and conduct the first meta-analysis in this field. Use of Bayesian model averaging and Frequentist model averaging help me to discover the most influential factors that affect the return-volume relationship, since I control for more than 50 differences among primary articles such as midyear and type of data, length of the primary dataset, size of market, or model employed. In the end, I find out that the relation between expected stock returns and trading volume is rather negligible. On the other hand, the contemporaneous relation between returns and volume is positive. These two findings cut the mixed results from previously written studies. Moreover, the investigated relationship is influenced by the size of country of interest and the level of its development. Besides the primary studies that employ higher data frequency provide substantially larger estimates than the studies with data from longer time periods. On the contrary, there is no difference among different estimation methodologies used. Finally, I employ classical and modern techniques such as stem-based methodology for publication bias detection, and I find evidence for it in this field. 1
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A Meta-Analysis of the Estimates of the Armington Elasticity
Bajzík, Josef ; Havránek, Tomáš (advisor) ; Polák, Petr (referee)
Josef Bajzík Abstract We examine determinants of Armington elasticities throughout history and nations employing 3,524 observations from 42 studies. We conduct meta-analysis using Bayesian model averaging approach to test the most influential factors. We explore more than 30 variables and compare our results with previous summarizing articles. In this thesis is, for instance, the first comparison of employment of different type of models in this area. Finally, we find out that the level of aggregation of the data used for estimation matters as well as the power of the currency. On the other hand, we discover that there is no significant distinction between long-run and short-run estimates. Moreover, we test for publication bias and we find evidence for it in this field.
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