The Feasibility of Predicting Financial Crises using Machine Learning: Selected Regression Algorithms and Macroeconomic Data - Brossura
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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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The Feasibility of Predicting Financial Crises using Machine Learning
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Taschenbuch. Condizione: Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 112 pp. Englisch. Codice articolo 9783389003657
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The Feasibility of Predicting Financial Crises using Machine Learning : Selected Regression Algorithms and Macroeconomic Data
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Codice articolo 9783389003657
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Taschenbuch. Condizione: Neu. The Feasibility of Predicting Financial Crises using Machine Learning | Selected Regression Algorithms and Macroeconomic Data | Julia Markhovski | Taschenbuch | Englisch | 2024 | GRIN Verlag | EAN 9783389003657 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 128816215
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