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The Feasibility of Predicting Financial Crises using Machine Learning

Julia Markhovski
E-Book (pdf), 109 Seiten
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Bachelor Thesis from the year 2024 in the subject Computer Science - Commercial Information Technology, grade: 1.0, Frankfurt Sch...

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Beschreibung

Bachelor Thesis from the year 2024 in the subject Computer Science - Commercial Information Technology, grade: 1.0, Frankfurt School of Finance & Management, language: English, abstract: In a world characterized by increasingly complex financial markets, the prediction of financial crises is a constant challenge. This bachelor thesis investigates the use of machine learning, in particular regression algorithms, to analyze and predict financial crises based on macroeconomic data. By building six different regression models and optimizing them using cross-validation and GridSearch, the feasibility of using these technologies for accurate predictions is discussed. Although traditional models show limited effectiveness, the integration of machine learning, especially kNN algorithms, reveals significant potential for improving prediction accuracy. The paper highlights the importance of classification algorithms and provides crucial insights for application in real-world scenarios to provide valuable tools for policy and business decision makers.

Produktinformationen

Titel:
The Feasibility of Predicting Financial Crises using Machine Learning
Untertitel:
Selected Regression Algorithms and Macroeconomic Data
Autor:
Julia Markhovski
EAN:
9783389003640
Format:
E-Book (pdf)
Hersteller:
GRIN Verlag
Genre:
Informatik
Veröffentlichung:
26.03.2024
Digitaler Kopierschutz:
frei
Dateigrösse:
3.6 MB
Anzahl Seiten:
109

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