금융위기 전개 과정 및 요인 분석: 복잡계와 머신러닝 방법론을 중심으로(An Analysis of the Evolution and Factors of the Financial Crisis Using Complex Systems and Machine Learning)

본 연구는 복잡계와 머신러닝 방법론을 이용해 과거 금융위기 전후 네크워크 패턴 변화, 금융위기를 예측하는 요인, 금융위기가 새로운 금융위기로 이어지는 과정을 분석하였다. 또한 이를 바탕으로 최근 상황을 진단하고 향후 잠재적 리스크 요인을 파악하였다. 본 연구를 통해 금융위기의 본질은 개별 리스크 요인보다는 시스템 차원의 문제라는 점, 최근 상황을 진단한 결과 세계적으로 금융위기가 발생할 위험성이 커지고 있음을 확인했다. 본 연구의 분석 결과는 금융위기의 전개 과정과 요인에 대한 이해의 폭을 넓히고, 우리나라의 금융안정 강화에 일조할 수 있을 것으로 기대된다. The shadow of the global financial crisis has been looming recently. Prices of almost all assets have plummeted, including stocks, bonds, digital currencies, and real estate, with some vulnerable emerging countries falling into a foreign exchange crisis. In addition, credit supply to the private sector is also slowing, and corporate defaults are increasing. Will this time be different? Or is another financial crisis on the horizon, only in a different form? This is a matter of great concern both on the international and national arenas. Therefore, this study seeks to discover valuable clues from previous cases and the use of new methodologies. We look at network patterns prior to and following the previous financial crises, factors that predict financial crises, and how one financial crisis leads to another financial crisis; and based on this, we diagnose the current situation and identify potential risk factors looking forward. This study largely consists of five parts, excluding Chapter 1 (Introduction) and Chapter 2 (Existing Research on Financial Crisis: Focusing on Causes and Development Process). In Chapter 3, we examine the relationship between the characteristics of financial crises and the network structure of financial market participants using micro data. During a financial crisis, a high degree of synchronization among heterogeneous economic agents tends to impact the characteristics of the network, resulting in a statistically significant change in the network structure. The connectivity of corporate and bank networks has been found to be very closely related to indicators of financial market risk and volatility. These characteristics were consistently found in domestic and international stock markets, Korea’s social media, and U.S. syndicated loan data.(the rest omitted)