Forecasting Four Business Cycle Phases Using Machine Learning: A Case Study of US and EuroZone
Understanding the business cycle is crucial for building economic stability, guiding business planning, and informing investment decisions. The business cycle refers to the recurring pattern of expansion and contraction in economic activity over time. Economic analysis is inherently complex, incorporating a myriad of factors (such as macroeconomic indicators, political decisions). This complexity makes it challenging to fully account for all variables when determining the current state of the economy and predicting its future trajectory in the upcoming months. The objective of this study is to investigate the capacity of machine learning models in automatically analyzing the state of the economic, with the goal of forecasting business phases (expansion, slowdown, recession and recovery) in the United States and the EuroZone. We compared three different machine learning approaches to classify the phases of the business cycle, and among them, the Multinomial Logistic Regression (MLR) achieved the best results. Specifically, MLR got the best results by achieving the accuracy of 65.25% (Top1) and 84.74% (Top2) for the EuroZone and 75% (Top1) and 92.14% (Top2) for the United States. These results demonstrate the potential of machine learning techniques to predict business cycles accurately, which can aid in making informed decisions in the fields of economics and finance.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Regional Synchronization during Economic Contraction: The Case of the U.S. and Japan
Two decades of studies have found significant regional differences in the timing of transitions in national business cycles and their durations. Earlier studies partly detect regional synchronization during business cycl…
Development of a Forecasting and Warning System on the Ecological Life-Cycle of Sunn Pest
We provide a machine learning solution that replaces the traditional methods for deciding the pesticide application time of Sunn Pest. We correlate climate data with phases of Sunn Pest in its life-cycle and decide wheth…
Machine learning in business process management: A systematic literature review
Machine learning (ML) provides algorithms to create computer programs based on data without explicitly programming them. In business process management (BPM), ML applications are used to analyse and improve processes eff…
Federated LearningManagementSystematic Literature ReviewCold-Start Forecasting of New Product Life-Cycles via Conditional Diffusion Models
Forecasting the life-cycle trajectory of a newly launched product is important for launch planning, resource allocation, and early risk assessment. This task is especially difficult in the pre-launch and early post-launc…
Business Cycle Synchronization in the EU: A Regional-Sectoral Look through Soft-Clustering and Wavelet Decomposition
This paper elaborates on the sectoral-regional view of the business cycle synchronization in the EU -- a necessary condition for the optimal currency area. We argue that complete and tidy clustering of the data improves …
ClusteringTime SeriesTime Series Analysis