Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Big Data Analysis with Machine Learning Techniques in Accounting Dataset from the Greek Banking System

Version 1 : Received: 2 August 2022 / Approved: 3 August 2022 / Online: 3 August 2022 (10:42:06 CEST)

How to cite: Theodorakopoulos, L.; Thanasas, G.; Lampropoulos, S. A Big Data Analysis with Machine Learning Techniques in Accounting Dataset from the Greek Banking System. Preprints 2022, 2022080083. https://doi.org/10.20944/preprints202208.0083.v1 Theodorakopoulos, L.; Thanasas, G.; Lampropoulos, S. A Big Data Analysis with Machine Learning Techniques in Accounting Dataset from the Greek Banking System. Preprints 2022, 2022080083. https://doi.org/10.20944/preprints202208.0083.v1

Abstract

The effects of the 2008 financial crisis undoubtedly caused problems not only to the banking sector but also to the real economy of the developed and the developing countries in almost all around the globe. Besides, as is widely known, every banking crisis entails the corresponding cost to the economy of each country affected by it, which results from the shakeout and the restructuring of its financial system. The purpose of this research is to investigate the consequences of the financial crisis and the COVID-19 health crisis and how these affected the course of the four systemic banks (Eurobank, Alpha Bank, National Bank, Piraeus Bank) through the analysis of ratios for the period of 2015-2020.

Keywords

Ratios; Financial Crisis; Covid-19; Big Data; Accounting Data

Subject

Business, Economics and Management, Accounting and Taxation

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