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The study investigates the co-movements amongst real estate investments trust (REITs). This study examines the co-movements between the world and individual countries' REITs and the co-movements amongst country-pair REITs. This study explores the responsiveness of the REITs markets' co-movements to the 2008 global financial crisis (GFC), the coronavirus disease 2019 (COVID-19) pandemic and the Russian–Ukraine conflict.

The study employs a wavelet coherency technique and relies on data from six REITs markets over the 1995–2022 period.

The evidence shows a generally high level of coherency between the global and the country's REITs. The findings further indicate higher co-movements between some country pairs and a lower co-movement for others. The results suggest that the REITs markets increased in co-movements around the 2008 GFC, the COVID-19 pandemic and the Russian–Ukraine conflict. These increased co-movements mostly lasted for a short period suggesting REITs markets contagion around these global events. The results generally suggest interdependence between the global and the country's REITs. Additionally, interdependence is observed for some of the country-pair REITs.

The evidence indicates that REITs markets respond to global events. Thus, the increasing co-movement amongst REITs observed in this study may expose domestic REITs to global crisis. However, this study provides opportunities for minimising the cost of capital for real estate projects. Also, REITs provide limited diversification gains around crisis times. Therefore, countries need to open the REITs markets to global investors whilst pursuing policies to ensure the resilience of the REITs markets to global events. Investors should also take note of the declining geographic diversification gains from some country-pair REITs portfolios.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

This paper aims to explore the impact of green finance on the heterogeneity of enterprise green technology innovation and the underlying mechanism between them.

Using the data of China's A-share listed enterprises from 2008 to 2020 and the fixed effect model, the authors empirically explore the relationship and mechanism between green finance and green technology innovation by constructing the green finance index while considering both the quality and quantity of innovation.

The study suggests that green finance is positively related to the quality and quantity of enterprise green technology innovation, while green finance is more effective in stimulating the quality of green technology innovation than quantity. In addition, alleviating financial mismatch and improving the quality of environmental information disclosure are core mechanisms during the process of green finance facilitating green technology innovation. Furthermore, green finance exerts a more positive effect on the quality and quantity of green technology innovation with large-size enterprises, heavily polluting industries and enterprises in the eastern region.

This paper enriches the literature on green finance and green technology innovation and provides practical significance for green finance implementation.

The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.

Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.

Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.

Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.

Despite the well-recognized importance of recycled water, the study of industry-peer pressure on recycled water is relatively new. This study investigates how organizations experience and react to industry-peer pressure to set recycled water targets. Additionally, this study investigates the role of board chairs involved in sustainability committees in contributing to responses to industry-peer pressure.

Using Eviews 12, this study employed a pooled logistic regression model to analyze data from 1,346 firms on Taiwan and Taipei exchanges (2017–2020).

The findings revealed that frequency-based imitation drives recycled water target-setting diffusion. However, there is no direct relationship between outcome-based imitation and recycled water target-setting. Notably, outcome-based imitation drives the adoption of recycled water target-setting of firms with board-chair membership in sustainability committees.

This study faces certain data limitations. First, this study primarily focuses on water recycling. Future research could explore other ways to reduce water usage, such as using water-efficient equipment. Second, this study gathered information solely on the presence or absence of a board chairperson on the sustainability committee. Future researchers could explore the impact of the composition of sustainability committee on recycled water target-setting. Lastly, the sample used in this study is restricted to Taiwan's corporations that existed during 2017–2020. Future researchers may consider adopting a longitudinal design in other economies to address this limitation.

The findings of this study offer several guidelines and implications for recycled water target-setting and the composition of sustainability committees. It responds to an urgent call for solutions to water shortages when pressure from governments and nongovernmental organizations is relatively absent. The number of industry peers that have already set recycled water targets is indispensable for motivating firms to set their own recycled water targets. In terms of insufficient water-related regulatory pressure and normative pressure, this study found evidence suggesting that the direct motivation for setting recycled water targets stems from mimetic pressures via frequency-based imitation. The evidence in this study suggests that policymakers should require companies to disclose their peers’ recycled water target information, as doing so serves as an alternative means to achieving SDG 6.3.

Recycled water target-setting might be challenging. Water recycling practices may face strong resistance and require substantial additional resources (Zhang and Tang, 2019; Gao et al., 2019; Gu et al., 2023). Therefore, this study suggests that firms should ensure the mindfulness of board members in promoting the welfare of the natural environment when making recycled water target-setting decisions. To reap the second-mover advantage, firms must consider the conditions in which board members can more effectively play their role. Corporations may help their chairpersons in setting recycled water targets by recruiting them as members of sustainability committees. Meanwhile, chairpersons tend to activate accurate mental models when the water conservation performance of pioneering industry peers is strong enough to indicate the potential benefits of adopting recycled water target-setting. Investors’ and stakeholders’ understanding of how the composition of sustainability committees is related to recycled water target-setting may help to identify the potential drivers of firms’ water responsibility. Investors and stakeholders should distinguish firms in terms of the board chair’s membership of their sustainability committee and focus on water-use reduction outcomes in the industry. This study provides insights into circumstances whereby chairpersons help to restore the water ecosystem.

This study explains how frequency-based and outcome-based imitation are two prominent mechanisms underlying the industry-peer pressure concerning recycled water target-setting. Moreover, this study fills literature gaps related to the moderating roles of board-chair membership in sustainability committees concerning industry-peer pressure on recycled water target-setting.

Since crude oil is crucial to the nation's economic growth, crude oil futures are closely related to many other markets. Accurate forecasting can offer investors trustworthy guidance. Numerous studies have begun to consider creating new metrics from social networks to improve forecasting models in light of their rapid development. To improve the forecasting of crude oil futures, the authors suggest an integrated model that combines investor sentiment and attention.

This study first creates investor attention variables using Baidu search indices and investor sentiment variables for medium sulfur crude oil (SC) futures by collecting comments from financial forums. The authors feed the price series into the NeuralProphet model to generate a new feature set using the output subsequences and predicted values. Next, the authors use the CatBoost model to extract additional features from the new feature set and perform multi-step predictions. Finally, the authors explain the model using Shapley additive explanations (SHAP) values and examine the direction and magnitude of each variable's influence.

The authors conduct forecasting experiments for SC futures one, two and three days in advance to evaluate the effectiveness of the proposed model. The empirical results show that the model is a reliable and effective tool for predicting, and including investor sentiment and attention variables in the model enhances its predictive power.

The data analyzed in this paper span from 2018 through 2022, and the forecast objectives only apply to futures prices for those years. If the authors alter the sample data, the experimental process must be repeated, and the outcomes will differ. Additionally, because crude oil has financial characteristics, its price is influenced by various external circumstances, including global epidemics and adjustments in political and economic policies. Future studies could consider these factors in models to forecast crude oil futures price volatility.

In conclusion, the proposed integrated model provides effective multistep forecasts for SC futures, and the findings will offer crucial practical guidance for policymakers and investors. This study also considers other relevant markets, such as stocks and exchange rates, to increase the forecast precision of the model. Furthermore, the model proposed in this paper, which combines investor factors, confirms the predictive ability of investor sentiment. Regulators can utilize these findings to improve their ability to predict market risks based on changes in investor sentiment. Future research can improve predictive effectiveness by considering the inclusion of macro events and further model optimization. Additionally, this model can be adapted to forecast other financial markets, such as stock markets and other futures products.

The authors propose a novel integrated model that considers investor factors to enhance the accuracy of crude oil futures forecasting. This method can also be applied to other financial markets to improve their forecasting efficiency.

This paper examines the extreme dependence and asymmetric risk spillovers between crude oil futures and ten US stock sector indices (consumer discretionary, consumer staples, energy, financials, health care, industrials, information technology, materials, telecommunication and utilities) before and during COVID-19 outbreak. This study is based on the rationale that stock sectors exhibit heterogeneity in their response to oil prices depending on whether they are classified as oil-intensive or non-oil-intensive sectors and the possible time variation in the dependence and risk spillover effects.

The authors employ static and dynamic symmetric and asymmetric copula models as well as Conditional Value at Risk (VaR) (CoVaR). Finally, they use robustness tests to validate their results.

Before the COVID-19 pandemic, crude oil returns showed an asymmetric tail dependence with all stock sector returns, except health care and industrials (materials), where an average (symmetric tail) dependence is identified. During the COVID-19 pandemic, crude oil returns exhibit a lower tail dependency with the returns of all stock sectors, except financials and consumer discretionary. Furthermore, there is evidence of downside and upside risk asymmetric spillovers from crude oil to stock sectors and vice versa. Finally, the risk spillovers from stock sectors to crude oil are higher than those from crude oil to stock sectors, and they significantly increase during the pandemic.

There is heterogeneity in the linkages and the asymmetric bidirectional systemic risk between crude oil and US economic sectors during bearish and bullish market conditions; this study is the first to investigate the average and extreme tail dependence and asymmetric spillovers between crude oil and US stock sectors.

This study explores the influence of the coronavirus disease 2019 (COVID-19) career experience on the investment behavior and risk tolerance of chief executive officers (CEOs). Specifically, this study focuses on CEOs' abilities to allocate financial assets and maintain solvency.

This study adopts a comprehensive approach to analyze financial assets and asset-to-liability ratios. Financial data and individual information of CEOs from listed companies are collected from 2020Q1 to 2021Q4, along with statistics on confirmed COVID-19 cases. Instrumental and alternative variables are used to examine the robustness and endogeneity of the research, ensuring a thorough analysis.

A significant positive correlation is revealed between CEOs' COVID-19 career experience and their capacity to effectively allocate financial assets. However, COVID-19 has a negative effect on firm performance in terms of solvency. These findings contribute to the empirical evidence linking the pandemic to company performance, representing part of the initial research in this area.

The study suggests that the implementation of potential policy implications, such as loose monetary policies and tax and fee reduction measures, may alleviate the tax burden on listed companies.

This study aims to examine how environmental, social and governance (ESG) initiatives and ISO 14001, which is an internationally agreed standard to set out the requirements for an environmental management system, affect firm performance in the context of the Industry 4.0 supply chain.

The authors develop a new chance-constrained network data envelopment analysis (DEA) in the presence of non-positive data to estimate innovation, operational and profitability performances for three main relation groups (suppliers, partners and customers) in Microsoft's supply chain.

Results of this study show the following: (1) the application of ISO 14001 will reduce profitability but increase overall performance (OP); (2) ESG implementation has a convex U-shaped influence on profitability and OP, which means that firms will benefit when ESG investment goes beyond a particular level; (3) the nonlinear U-shape is presented in the E and G components, but not in the S of the individual ESG initiatives, and (4) only specific subcomponents of S and G in the subcomponent of individual ESG initiatives are nonlinearly connected to OP. Research's results reveal that the customer group has a higher performance value than the other two groups, which suggests that this group will create competitive advantages for Microsoft.

Overall, the authors provide an insightful viewpoint into supply chain management by examining the ESG initiatives, ISO 14001 and performances of Microsoft's supply chain.

Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.

Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques.

The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL).

It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies.

The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.