Advancing financial crisis prediction: Big data, AI, and economic theories

This paper explores the theoretical foundations of financial crisis early warning systems, focusing on the integration of big data, ensemble learning, and various financial crisis theories. It begins with defining key concepts such as big data, financial crisis, and ensemble learning, highlighting the evolution and significance of these terms in the context of financial crisis management. The literature review covers extensive research on financial crisis early warning indicators and models, tracing the development from traditional statistical methods to advanced artificial intelligence approaches, including neural networks, decision trees, and machine learning algorithms. The paper critically assesses the current methodologies and emphasizes the necessity of incorporating big data and machine learning for more accurate and comprehensive early warning systems. Theoretical foundations related to financial crises, such as information asymmetry, behavioral economics, economic cycle theory, and contingency theory, are discussed to understand their impact on financial crisis prediction and management. The conclusion synthesizes the findings and suggests future research directions, emphasizing the integration of diverse methodologies and interdisciplinary approaches to enhance the early warning capabilities of enterprises.