This study proposes a novel loss function for neural network models that explores the topological structure of stock realized volatility (RV) data by adding Wasserstein Distance (WD). The study shows that the proposed loss statistically significantly improves the forecast accuracy of neural...

This study aims to determine how payment disturbances in managers' earlier entrepreneurial practices (PDMs) predict corporate default. Classical financial ratios have often failed to predict the default of micro-, small- and medium-sized firms with high accuracy, and therefore, the extant...

Determining which variables afect price realized volatility has always been challenging. This paper proposes to explain how fnancial assets infuence realized volatility by developing an optimal day-to-day forecast. The methodological proposal is based on using the best econometric and machine...

In this paper, we propose a simple dynamic mortality model to ft and forecast mortality rates for measuring longevity and mortality risks. This proposal is based on a methodology for modelling interest rates, which assumes that changes in spot interest rates depend linearly on a small number of...

The new energy industry is strongly supported by the state, and accurate forecasting of stock price can lead to better understanding of its development. However, factors such as cost and ease of use of new energy, as well as economic situation and policy environment, have led to continuous...

Literature shows that both market data and fnancial media impact stock prices; however, using only one kind of data may lead to information bias. Therefore, this study uses market data and news to investigate their joint impact on stock price trends. However, combining these two types of...