Τίτλος:
Modeling of Crisis Periods in Stock Markets
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
We exploit a recent computational framework to model and detect financial crises in stock markets, as well as shock events in cryptocurrency markets, which are characterized by a sudden or severe drop in prices. Our method manages to detect all past crises in the French industrial stock market starting with the crash of 1929, including financial crises after 1990 (e.g. dot-com bubble burst of 2000, stock market downturn of 2002), and all past crashes in the cryptocurrency market, namely in 2018, and also in 2020 due to covid-19. We leverage copulae clustering, based on the distance between probability distributions, in order to validate the reliability of the framework; we show that clusters contain copulae from similar market states such as normal states, or crises. Moreover, we propose a novel regression model that can detect successfully all past events using less than 10% of the information that the previous framework requires. We train our model by historical data on the industry assets, and we are able to detect all past shock events in the cryptocurrency market. Our tools provide the essential components of our software framework that offers fast and reliable detection, or even prediction, of shock events in stock and cryptocurrency markets of hundreds of assets. © 2021, Springer Nature Switzerland AG.
Συγγραφείς:
Chalkis, A.
Christoforou, E.
Dalamagas, T.
Emiris, I.Z.
Περιοδικό:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Εκδότης:
Springer Science and Business Media Deutschland GmbH
Λέξεις-κλειδιά:
Bitcoin; Computer programming; Financial markets; Investments; Regression analysis, Clusterings; Computational framework; Copula; Crisis detection; Financial crisis; Financial portfolio; Investment risks; Market downturn; Probability: distributions; Stock market, Probability distributions
DOI:
10.1007/978-3-030-92121-7_5
