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The Feasibility of Predicting Financial Crises using Machine Learning: Selected Regression Algorithms and Macroeconomic Data

The Feasibility of Predicting Financial Crises using Machine Learning: Selected Regression Algorithms and Macroeconomic Data

The Feasibility of Predicting Financial Crises using Machine Learning: Selected
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The Feasibility of Predicting Financial Crises using Machine Learning: Selected Regression Algorithms and Macroeconomic Data Paperback - 2024

by Markhovski, Julia

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  • Title The Feasibility of Predicting Financial Crises using Machine Learning: Selected Regression Algorithms and Macroeconomic Data
  • Author Markhovski, Julia
  • Binding Paperback
  • Condition Used - Good
  • Pages 114
  • Volumes 1
  • Language ENG
  • Publisher Grin Verlag
  • Publication date 2024-03-04
  • Bookseller's Inventory # 3389003657.G
  • ISBN 9783389003657 / 3389003657
  • Weight 0.35 lbs (0.16 kg)
  • Dimensions 8.27 x 5.83 x 0.27 in (21.01 x 14.81 x 0.69 cm)
  • Category Computers - Languages / Programming
  • Quantity available 1

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From the publisher

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.

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