Search results

We investigate the interconnectedness between the financial sectors and new energy companies in China from the perspective of the multilayer network, and analyze the static and time-varying characteristics of the multilayer network at system and company levels, respectively.

We employ the multilayer network containing the realized volatility (RV here after) layer, the realized skewness (RS here after) layer and the realized kurtosis (RK here after) layer. The three realized indicators adopted to construct the multilayer network are generated by the intraday trading data from 2012 to 2022.

(1) Different layers have different characteristics, and can provide supplementary information. (2) Banks tend to play the role of risk transmitters on the whole, while the insurances and new energy companies tend to play the role of risk receivers on average. (3) The connectedness strength of financial sectors and new energy companies varies over time, and climbs sharply during the major crisis events. The roles of financial sectors and new energy companies may change from risk transmitters to risk receivers, and vice versa.

We adopt three realized indicators to construct the three-layer network, which provides a more comprehensive perspective for understanding the connectedness between the financial sectors and new energy companies in China.

Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of RASCC is challenging due to its complex composite nature and nonlinear behavior.

This study comprehensively evaluated commonly used machine learning (ML) techniques, including artificial neural networks (ANN), random trees (RT), bagging and random forests (RF) for predicting the CS of RASCC. The results indicate that RF and ANN models typically have advantages with higher R2 values, lower root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) values.

The combination of ML and Shapley additive explanation (SHAP) interpretable algorithms provides physical rationality, allowing engineers to adjust the proportion based on parameter analysis to predict and design RASCC. The sensitivity analysis of the ML model indicates that ANN’s interpretation ability is weaker than tree-based algorithms (RT, BG and RF). ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

Corporate site visits increase labor investment efficiency.

Our empirical model for the baseline analysis follows those of Jung et al. (2014) and Ghaly et al. (2020).

We show that corporate site visits are associated with significantly higher labor investment efficiency; more specifically, site visits reduce both over-hiring and under-hiring of employees. The effect of site visits on labor investment efficiency is more pronounced for firms with higher labor adjustment costs, greater financial constraints, weaker corporate governance and lower financial reporting quality. We also find that site visits mitigate labor cost stickiness.

First, while the literature has suggested how the presence of institutional investors and analysts may affect labor investment decisions, we focus on institutional investors and analysts’ activities and interactions with firm executives. We provide direct evidence that institutional investors and analysts may use corporate site visits to improve labor investment efficiency. Second, our study adds to a line of recent studies on how corporate site visits reduce information asymmetry and agency conflicts. We show that corporate site visits allow institutional investors and analysts to influence labor investment efficiency. We also provide new evidence that corporate site visits reduce labor cost stickiness.