The disruption of supply chain due to Covid-19 and the war in Ukraine, render the prediction of agricultural output a determinant factor of economic life. We consider the predictability of agricultural output based on a set of explanatory variables, that include agricultural input, prices and...

We explore the relationship between the returns of 45 dry bulk shipping company stock prices and the main 15 commodities that bulk carriers transport. Using a principal component analysis to reduce the dimensionality of the commodities dataset and a panel methodology, we find that a change in...

Purpose: This paper studies the problem that occurs on material purchase quantity in price uncertainty situation. Larger buying quantity when the price at high will increase the purchase amount while smaller buying quantity could risk the inventory level. The decision on the purchase quantity of...

We study the out-of-sample forecasting performance of 32 exchange rates vis-a-vis the New Taiwan Dollar (NTD) in a 32-variable vector autoregression (VAR) model. The Bayesian approach is applied to the large-scale VAR model (LBVAR), and its (timevarying) forecasting performance is compared to...

Accurately predicting Customer Lifetime Value (CLV) is paramount in optimizing customer relationship management. This study introduces a novel deep learning approach, employing a neural network model to forecast CLV for a leading international e-commerce retailer. This investigation delves into...

This paper investigates the impact of news media information on improving short-term GDP growth forecasts by analyzing a large and unique corpus of 12.4 million news articles spanning from 1991 to 2018. We extract business cycle-related sentiment from each article using an annotated dataset from...