Tuhin Subhra De

Tail risk inter-connectivity is a significant aspect and a risk indicator that should be focused on. Many of the previous works have shown potential non-linearity in tail risk contagion. With the recent advancements in deep learning, Long-Short Term Memory (LSTM) networks have played an important role in sequential data prediction. We experiment with LASSO-based neural networks and interpretative LSTM model along with other machine learning approaches for investigating tail risk interconnectedness among the public banks of Japan. We also investigate the risk reception from large overseas banks in United States finding that medium-sized banks are more likely to receive international risks. Our studies show that LSTM-based model is an excellent fit for the scenario and total connectedness goes up during an economic crisis. The banks having larger market capitalization are more prone to emission and reception of tail risks. This is accompanied by exhibiting the impact of some major economic distresses on Japanese banking system. These results provide important information to regulators and policy makers.

In the context of developing nations like India, traditional business-to-business (B2B) commerce heavily relies on the establishment of robust relationships, trust, and credit arrangements between buyers and sellers. Consequently, e-commerce enterprises frequently employ telecallers to cultivate buyer relationships, streamline order placement procedures, and promote special promotions. The accurate anticipation of buyer order placement behavior emerges as a pivotal factor for attaining sustainable growth, heightening competitiveness, and optimizing the efficiency of these telecallers. To address this challenge, we have employed an ensemble approach comprising XGBoost and a modified version of Poisson Gamma model to predict customer order patterns with precision. This paper provides an in-depth exploration of the strategic fusion of machine learning and an empirical Bayesian approach, bolstered by the judicious selection of pertinent features. This innovative approach has yielded a remarkable 3 times increase in customer order rates, showcasing its potential for transformative impact in the e-commerce industry.