What is it about?
The three approaches we chose - construction of an optimal portfolio based on the Sharpe ratio or utility maximization, risk and investment growth analysis, distribution of portfolio participants into clusters based on accounting data, and the accuracy of a prediction model based on neural networks with the construction of an additional crisis layer - gave fairly close results. At the same time, these results cannot be called identical. What can be the explanation of such discrepancies? We believe that there is no clear gradation of CEE banks (among our selected sample with sufficient access to data) regarding the impact of both the pandemic and the war started by Russia. At the same time, the proposed method makes it possible to single out the most vulnerable market entities (such as, for example, the OTP Bank), and to achieve the maximum accuracy of prediction models. A further direction of research may be the improvement of the method based on neural networks with a possible analysis of the influence of factors similar to the Fama-French model with sufficient mathematical justification.
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Why is it important?
The CEE (Central and Eastern Europe) banking sector does not yet play a significant role in bringing the region closer to developed markets as everyone would like. There are many reasons for this. First of all, differences in the formation of this sector. If German or Austrian banks were formed over a long period of time from the bottom up with the wide involvement of local communities and cooperative business, in the CEE states, modern banks were forced to grow on the shoulders of former banking structures of the socialist camp (only some specific of Baltic countries), which had little resemblance to the modern banking institution. But the economies of the region are developing, there is a significant foreign invested capital, which in one way or another pushes the banking sector forward. If at first glance this movement appears to be unorganized, the application of a deeper modern analysis allows us to identify main strategies and market leaders, as well as the basis for its predictability. And then suddenly on the horizon there was a shock of restrictions related to the pandemic and a shock of sanctions, a sudden change in logistics as a result of the war started in Europe by Russia. It has created a completely new situation for CEE banking sector.
Perspectives
The character of model depends on algorithm type to calculate the neural network. Its non-linearity allows us to solve the problems we had earlier with the functioning of the Fama-French 5-factor model in only three cases. It is interesting to further apply this approach to other industries in order to identify the possibility of simulating a crisis by means of an additional hidden layer with a larger number of neurons. This model manifests itself best in marginal cases.
Ihor Hurnyak
Lvivskij Natsionalnyj Universitet imeni Ivana Franka
Read the Original
This page is a summary of: Central and Eastern Europe (CEE) Banking on the Stock Market: Turbulence of 2020 – 2022, Communications of International Proceedings, January 2023, IBIMA Publishing,
DOI: 10.5171/2023.4249223.
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