Stefan Lyocsa
@muni.cz
Department of Finance
Faculty of Economics and Administration, Masaryk University
RESEARCH INTERESTS
My current research is concentrated around instabilities of the financial system, market risks and credit risk modeling on P2P lending markets.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Štefan Lyócsa and Neda Todorova
Elsevier BV
Erik Haugom, Štefan Lyócsa, and Martina Halousková
Elsevier BV
Štefan Lyócsa, Tomáš Plíhal, and Tomáš Výrost
Elsevier BV
Štefan Lyócsa, Martina Halousková, and Erik Haugom
Elsevier BV
Miroslav Štefánik, Štefan Lyócsa, and Matúš Bilka
SAGE Publications
Štefan Lyócsa, Petra Vašaničová, and Oleg Deev
Informa UK Limited
Štefan Lyócsa, Petra Vašaničová, Branka Hadji Misheva, and Marko Dávid Vateha
Springer Science and Business Media LLC
AbstractFor the emerging peer-to-peer (P2P) lending markets to survive, they need to employ credit-risk management practices such that an investor base is profitable in the long run. Traditionally, credit-risk management relies on credit scoring that predicts loans’ probability of default. In this paper, we use a profit scoring approach that is based on modeling the annualized adjusted internal rate of returns of loans. To validate our profit scoring models with traditional credit scoring models, we use data from a European P2P lending market, Bondora, and also a random sample of loans from the Lending Club P2P lending market. We compare the out-of-sample accuracy and profitability of the credit and profit scoring models within several classes of statistical and machine learning models including the following: logistic and linear regression, lasso, ridge, elastic net, random forest, and neural networks. We found that our approach outperforms standard credit scoring models for Lending Club and Bondora loans. More specifically, as opposed to credit scoring models, returns across all loans are 24.0% (Bondora) and 15.5% (Lending Club) higher, whereas accuracy is 6.7% (Bondora) and 3.1% (Lending Club) higher for the proposed profit scoring models. Moreover, our results are not driven by manual selection as profit scoring models suggest investing in more loans. Finally, even if we consider data sampling bias, we found that the set of superior models consists almost exclusively of profit scoring models. Thus, our results contribute to the literature by suggesting a paradigm shift in modeling credit-risk in the P2P market to prefer profit as opposed to credit-risk scoring models.
Oleg Deev, Štefan Lyócsa, and Tomáš Výrost
Elsevier BV
Zuzana Košťálová, Štefan Lyócsa, and Miroslav Štefánik
Elsevier BV
Štefan Lyócsa and Tomáš Plíhal
Elsevier BV
Katarína Lučivjanská, Štefan Lyócsa, Marek Radvanský, and Mária Širaňová
Elsevier BV
Ivan Lichner, Štefan Lyócsa, and Eva Výrostová
Elsevier BV
Štefan Lyócsa, Eduard Baumöhl, and Tomáš Výrost
Elsevier BV
Zuzana Košťálová, Eva Horvátová, Štefan Lyócsa, and Peter Gernát
Informa UK Limited
ABSTRACT In developed economies, macroeconomic indicators such as unemployment and price indices tend to predict new credit expansion. We explore whether business and consumer surveys complement traditional macroeconomic variables in predicting new household and corporate loans in the following 3, 6, 9 and 12 months. Using monthly data for Slovakia, starting in 2009 and ending in 2019, we use Bayesian model averaging to examine 102 potential credit drivers. Our results show that, with the exception of interest rates and unemployment, traditional macroeconomic variables do not seem to drive credit market development. Instead, survey-based perceptions, calendar effects and policy uncertainty show relevant predictive power.
Štefan Lyócsa and Daniel Stašek
Elsevier BV
Štefan Lyócsa and Neda Todorova
Elsevier BV
Štefan Lyócsa, Peter Molnár, and Tomáš Výrost
Elsevier BV
Štefan Lyócsa, Tomáš Plíhal, and Tomáš Výrost
Elsevier BV
Linh Phuong Catherine Do, Štefan Lyócsa, and Peter Molnár
Elsevier BV
Štefan Lyócsa, Neda Todorova, and Tomáš Výrost
Elsevier BV
Tomáš Plíhal and Štefan Lyócsa
Elsevier BV
Štefan Lyócsa, Tomáš Výrost, and Tomáš Plíhal
Elsevier BV
Štefan Lyócsa, Peter Molnár, Tomáš Plíhal, and Mária Širaňová
Elsevier BV
Štefan Lyócsa, Eduard Baumöhl, Tomáš Výrost, and Peter Molnár
Elsevier BV
Štefan Lyócsa and Peter Molnár
Elsevier BV