Work in progress

Baumöhl, E., Bačo, T. (2024). Zombie firms in Slovakia: the role of corporate governance, foreign ownership and CEO gender.

Existing research provides evidence on negative effects on aggregate productivity of the so-called zombie firms, yet, a unified definition of what a zombie firm actually is and how it should manifest is still missing. In this paper we utilized more than 800 000 firm-year observations to shed some light on a zombie firm presence in Slovakia. We apply three different zombie identification procedures and provide an evidence that (i) there are significant differences in investments, economic efficiency, and effective tax rates among zombie firms and their non-zombie counterparts; (ii) foreign director and the number of directors are both factors lowering the probability of firm being a zombie in almost all industry groups; (iii) female as a CEO and having the owner who serves as CEO lowers the probability of having negative equity; (iv) for small firms, having a foreign director and a female CEO is a strong preventive factor. Other results vary across industries and more importantly, over different zombie identification procedures.

Baumöhl, E., Antol, R., Výrost, T., Bačo, T. (2024). Machine Learning Meets Tax Fraud: Insights from Slovakia.

One of the most intriguing topics in the field of corporate finance is the detection of tax fraud. We consider a unique dataset of outcomes from Slovak tax authority audits, obtaining valuable insights into verified instances of tax manipulation and avoiding the misclassification problem that is common in this stream of literature. We apply artificial neural networks, random forests, XGBoost, and support vector machines to verify the extent to which we can classify tax manipulators on the basis of publicly available financial statement indicators. Our results show that the XGBoost model demonstrated the highest effectiveness, achieving an F1 score of 0.75 in the full sample, slightly lower scores within the industry groups, and excellent results in sector A – Agriculture, with an F1 score of 0.85. Our results indicate that the use of nowadays commonly known machine learning methods along with standard financial variables can provide a useful tool for tax fraud detection and, as such, can contribute to higher efficiency of tax audits.

Lyócsa, Š., Baumöhl, E. and Výrost, T. (2021). YOLO trading: Riding with the herd during the GameStop episode. Finance Research Letters (in press).

We explore the 2020 and early 2021 price variation of four stocks: GameStop, AMC Entertainment Holdings, Blackberry and Nokia. The four stocks were subject to a decentralized short squeeze that exploited the short positions of institutional investors. This investor movement was likely initiated by retail investors concentrated mostly around the subreddit r/WallStreetBets (WSB). We demonstrate that part of the next day’s price variation can be explained by an increase in activity on the WSB subreddit relative to Google searches (terms related to the event). We discuss implications for future research. 

Baumöhl, E., Bouri, E., Hoang, T.H.V., Shahzad, S.J.H. and Výrost, T. (2020). Increasing systemic risk during the Covid-19 pandemic: A cross-quantilogram analysis of the banking sector.

Over the last few decades, large banks worldwide have become more interconnected. As a result, the failure of one can trigger the failure of many. In finance, this phenomenon is often known as financial contagion, which can act like a domino effect. In this paper, we show an unprecedented increase in bank interconnectedness during the outbreak of the Covid-19 pandemic. We measure how extreme negative stock market returns from one bank can spill over to the other banks within the network. Our contribution relies on the establishment of a new systemic risk index based on the cross-quantilogram approach of Han et al. (2016). The results indicate that the systemic risk and the density of the spillover network among 83 banks in 24 countries have never been as high as during the Covid-19 pandemic – much higher than during the 2008 global financial crisis. Furthermore, we find that US banks are the most important risk transmitters, and Asian banks are the most important risk receivers. In contrast, European banks were strong risk transmitters during the European sovereign debt crisis. These findings may help investors, portfolio managers and policymakers adapt their investment strategies and macroprudential policies in this context of uncertainty. 

Baumöhl, E. and Vyrost, T. (2020). Stablecoins as a crypto safe haven? Not all of them!

We test the safe haven properties of the largest stablecoins (USDT, USDC, TUSD, PAX, DAI, GUSD) against the standard “nonstable” coins (BTC, ETH, XRP, BCH, LTC). Our dataset comprises high-frequency 1-minute data calculated as volume-weighted averages across 18 exchanges where these cryptocurrencies are traded, thus capturing the entire price movement around the world. Using a quantile coherency cross-spectral measure, we find that only TUSD, PAX, and GUSD can serve as safe havens.