Sergey Zhuk

Assistant Professor, Department of Finance, University of Vienna

Phone: +43-1-4277-37505

Department of Finance
University of Vienna
Oskar-Morgenstern-Platz 1
1090 Vienna, Austria

Research Interests:
Financial Economics: Behavioral Finance, Bubbles, Liquidity and Market Microstructure

[CV], [SSRN]

Behavioral Finance / Asset Pricing

Revealing Downturns” with Martin Schmalz [Online Appendix] shows with a simple Bayesian learning model that prices should respond stronger to information in downturns than in upturns.
Review of Financial Studies, forthcoming

Attention Allocation and Credit Quality” with Mike Mariathasan explains the counter-cyclical lending standards and endogenous risk build-up during market booms with a simple rational inattention model, in which banks trade off the number of processed loan applications with the precision of their loan review.
ES Philadelphia 2018, 
Annual 3rd CEPR Symposium London 2018 

Signaling through Timing of Stock Splits” with Maria Chiara Iannino. In the paper we construct a dynamic structural model of stock splits consistent with several empirical observations. By estimating the model we estimate the size of the nominal share price preferences and the signaling costs of stock splits.
ES World Congress Montreal 2015

Speculative Bubbles, Information Flow and Real Investment” studies heterogeneous beliefs speculative bubbles in a setup in which information flow is endogenous and is determined by real investment. The arising bubble can mitigate learning externalities, however is usually too small at the beginning of the bubble episode and too big around the peak.
ESEM Toulouse 2014

Liquidity and Market Microstructure

Inventory Risk with Persistent Liquidity Shocks”. I develop a tractable dynamic model of inventory risk with a simple closed form solution for the optimal behavior of intermediaries. By applying the model to the case of positively auto-correlated order flow (commonly observed in the data), I show that the usual estimates of inventory risk component are biased.