Energy futures have become important as alternative investment assets to minimize the volatility of portfolio return, owing to their low links with traditional financial markets. In order to make energy futures markets grow further, it is necessary to expand expectations of returns from trading...

There is no denying the role of empirical research in finance and the remarkable progress of empirical techniques in this research field. This Special Issue focuses on the broad topic of "Empirical Finance" and includes novel empirical research associated with financial data. One example...

Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine, particularly, an intelligent computer program. Machine learning is an approach to realizing AI comprising a collection of statistical algorithms, of which deep learning is one such...

Recent developments in the natural gas industry warrant new analysis of related issues. Environmental, social, and governance (ESG) investments have accelerated the shi􀀴 away from coal as the dominant source of electricity. Its low environmental impact, reduced volume, and broad availability...

A longstanding objective of managers is to reduce risk to their businesses. The conventional strategy for risk reduction is diversification; however, evidence for the effectiveness of diversification remains inconclusive. According to Organizational Portfolio Analysis, firms are viewed as...

Proper credit-risk management is essential for lending institutions, as substantial losses can be incurred when borrowers default. Consequently, statistical methods that can measure and analyze credit risk objectively are becoming increasingly important. This study analyzes default payment data...

Housing prices in China have been rising rapidly in recent years, which is a cause for concern for China's housing market. Does bank credit influence housing prices? If so, how? Will the housing prices affect the bank credit system if the market collapses? We aim to study the dynamic...

We propose a novel approach that combines random forests and the wavelet transform to model the prediction of currency crises. Our classification model of random forests, built using both standard predictors and wavelet predictors, and obtained from the wavelet transform, achieves a demonstrably...

We study the dependence structure of share price returns among the Beijing Bank, Ningbo Bank, and Nanjing Bank using copula models. We use the normal, Student's t, rotated Gumbel, and symmetrized Joe-Clayton (SJC) copula models to estimate the underlying dependence structure in two periods: one...

This paper proposes a novel approach, based on convolutional neural network (CNN) models, that forecasts the short-term crude oil futures prices with good performance. In our study, we confirm that artificial intelligence (AI)-based deep-learning approaches can provide more accurate forecasts of...