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This study investigates how firm-specific exposure to COVID-19 influences capital structure choices, a topic of significant importance due to the pandemic’s unprecedented economic disruption. We leverage a novel text-based measure of firm-specific COVID exposure to explore these dynamics.

Our research adopts an innovative text-based approach to measure firm-specific COVID exposure, developed by prior studies, which analyzes earnings conference call transcripts using advanced machine learning algorithms. The analysis is based on a comprehensive sample of US firms spanning over 20 years. Various statistical techniques, including firm-fixed effects, propensity score matching, entropy balancing and instrumental-variable analysis, are employed to ensure robust results.

Our findings indicate that firms with higher COVID exposure significantly alter their leverage, favoring debt financing over equity financing. This effect is less pronounced in larger and more profitable firms as well as those more vulnerable to climate change risk. The unique impact of COVID on leverage is contrasted with other infectious diseases, which do not exhibit similar effects.

Our study’s originality lies in its application of a novel text-based metric, borrowed from existing research, to measure firm-specific COVID exposure, marking a significant advancement in the field. This method provides a timely and precise assessment of exposure, offering insights that traditional metrics cannot capture. It is the first study to document the significant role of COVID exposure in determining corporate leverage, enhancing the understanding of capital structure dynamics in the context of unprecedented global disruptions.

This paper aims to unveil the greenwashing intention of green bonds issuing in Chinese enterprises through the lens of stock pricing efficiency.

Drawing on data of Chinese listed companies during 2012–2021, this study uses a difference-in-differences method to study how and through what mechanisms issuing green bonds impacts stock pricing efficiency.

Issuing green bonds lowers stock pricing efficiency, verifying the greenwashing intention of green bonds in China. Potential mechanisms underlie the increased investor attention and sentiment resulting from the information disclosures about corporate green and low-carbon development. This greenwashing issue is more pronounced in firms facing lower financing constraints, having stronger relations with the government, and located in highly marketized regions. In the context of uncertainty surrounding economic policies, especially trade policies, issuing green bonds can signal a weakening of the greenwashing effect.

The quality of information disclosure should be emphasized to ensure a substantive commitment to environmental responsibility signaled by green bond issuance, thereby mitigating greenwashing concerns.

Regulators and standard-setters should improve the issuance system for green bonds and promote the sustainable development of the green bond market through formulating unified certification criteria for green bonds and implementing a stringently periodic reporting system.

First, to the best of the authors’ knowledge, it is the first study to draw on the quality of information disclosure and the perspective of stock pricing efficiency to identify whether firms issuing green bonds engage in greenwashing. Second, the study uncovers the black-box underlying this greenwashing issue through investor attention and sentiment and examines further the moderating role of economic policy uncertainties.

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

This study investigates clean energy, commodities, green bonds and environmental, social and governance (ESG) index prices forecasting and assesses the predictive performance of various factors on these asset prices, used for the development of a robust forecasting support decision model using machine learning (ML) techniques. More specifically, we explore the impact of the financial stress on forecasting price.

We utilize feature selection techniques to evaluate the predictive efficacy of various factors on asset prices. Moreover, we have developed a forecasting model for these asset prices by assessing the accuracy of two ML models: specifically, the deep learning long short-term memory (LSTM) neural networks and the extreme gradient boosting (XGBoost) model. To check the robustness of the study results, the authors referred to bootstrap linear regression as an alternative traditional method for forecasting green asset prices.

The results highlight the significance of financial stress in enhancing price forecast accuracy, with the financial stress index (FSI) and panic index (PI) emerging as primary determinants. In terms of the forecasting model's accuracy, our analysis reveals that the LSTM outperformed the XGBoost model, establishing itself as the most efficient algorithm among the two tested.

This research enhances comprehension, which is valuable for both investors and policymakers seeking improved price forecasting through the utilization of a predictive model.

To the authors' best knowledge, this marks the inaugural attempt to construct a multivariate forecasting model. Indeed, the development of a robust forecasting model utilizing ML techniques provides practical value as a decision support tool for shaping investment strategies.

We develop a Markov model of curling matches. This enables strategic and econometric analyses to be performed alongside computer simulation work.

We develop a Markov model of curling matches, parametrised by the probability of winning an end and the probability distribution of scoring ends. In practical applications, these end-winning probabilities can be estimated econometrically and are shown to depend on which team holds the hammer as well as the offensive and defensive strengths of the respective teams. Using a maximum entropy argument, based on the idea of characteristic scoring patterns in curling, we predict that the points distribution of scoring ends should follow a constrained geometric distribution.

We provide analytical results detailing when it is optimal to blank the end in preference to scoring one point and losing possession of the hammer. Statistical and simulation analysis of international curling matches is also performed.

Our work adds to the theory and application of sports analytics, especially Markov models, and to the econometric and strategic analysis of curling matches.

This paper aims to advocate for a paradigm shift that prioritizes a human-centered approach in the pursuit of innovation during crises, urging a departure from the prevailing dominance of the technology-centric approach. The incorporation of emotional capabilities as a dynamic capability is posited as a pivotal contribution, in harmony with the tenets of Society 5.0 and imperative for establishing a robust knowledge management foundation. This research underscores the significance of the human-centered approach, portraying women as exemplars in a novel paradigm of innovation development amid crises.

This research uses the framework of knowledge management for innovation to focus on the challenges presented by complex crises, now considered the new normal. The study employs a distinct, human centric approach to explore the nexus of gender, opportunities and innovation, during crises, with an emphasis on the founders’ emotional capabilities and resources as catalysts for innovation development.

This research utilizes mixed methods; qualitative findings driven from AI analyses reveal women’s positive approach toward innovation development in adversity, showcasing the influence of their emotional resources in their innovation pursuits. The subsequent quantitative findings, derived from a sample of 464 tech-founders navigating complex crises, emphasize the role of emotional capabilities as activators of opportunity exploitation for enhancing innovation development during crises, particularly among female founders.

The potential for future research lies in exploring diverse emotional dimensions, employing various measures and methodologies. Envisioning upcoming studies that extend our findings across institutional, national and crisis contexts, emotional capabilities and skills may emerge as critical assets relevant to all entrepreneurs, transcending gender boundaries. This paper’s framework sets the stage for promising avenues at the nexus of gender and emotional capabilities in the innovation pursuits, shaping entrepreneurial performance in both challenging and stable conditions.

This research contributes significantly in several key areas. Firstly, it explores innovation development and knowledge management within Society 5.0 during a polycrisis, emphasizing the crucial role of emotional capabilities in activating opportunity exploitation. Secondly, it champions a human-centric premise in innovation, highlighting women as role models for innovation during crises and introducing pathways to tap into external resources, ultimately enriching knowledge management. Thirdly, the innovative methodological approach using Artificial Intelligence (AI) and Natural Language Processing (NLP) to construct synthetic personas is groundbreaking. Finally, it advances effectuation, bricolage and dynamic capabilities frameworks, enriching their theoretical foundations and affirming their relevance for innovation development amid instability.

This study aims to synthesize the literature on shopping experiences of visually impaired consumers (VIC). The review probes the extent of the research done, highlights the shopping experiences of VIC documented in the existing literature, pinpoints gaps and provides directions for future research.

This study uses Arksey and O’Malley’s framework to conduct a scoping review. Articles related to VIC shopping experience were collected from Scopus databases. In total, 27 articles that met the inclusion criteria were selected for the review.

The results were divided into three sections. The analysis section conveys the extent of the research relevant to the methodology, study context, geographical location, timeline and citation analysis. The emerging themes portray the experiences VIC encounters while shopping. Finally, the consultation process revealed the current perspectives of VIC.

This review collates the existing literature and identifies the research gaps. These research gaps provide directions for future research. This study creates awareness of VIC shopping needs.

To the best of the authors’ knowledge, this is the first study to review the literature on VIC’s shopping experience.

In the realistic multi-project scheduling, resources are not always shared among multiple projects, nor are they available to perform activities throughout the planning horizon. Besides, according to construction technology, some architectural jobs cannot be interrupted for any reason. However, these characteristics of resources and activities have not been fully studied, which may lead to the reduction of engineering quality and the failure of scheduling work. Therefore, this paper aims to model a multi-project scheduling problem with the above characteristics and provide an effective method to meet the actual needs of the construction industry.

A three-phase CPLEX with quota auction mechanism (TPCP–QAM) is developed to solve this problem, which significantly improves the solving performance of CPLEX by adjusting the search strategy and implementing a distributed procedure. In this approach, resources are dedicated to individual projects through a global coordination mechanism, while each project is independently scheduled by a local scheduling algorithm.

(1) For the proposed problem, CPLEX 2019's default search strategy. (Auto) is far inferior to another search strategy (Multi-point) in optimizing the project total cost and average resource capacity. (2) Compared with other two algorithms, TPCP–QAM has obvious advantages in the multi-project total cost (MPTC) and CPU time, especially for large-size instances. (3) Even though the number of non-working days may not be changed for the protection of labor resources, managers can reduce MPTC or shorten the multi-project total makespan (TMS) by appropriately adjusting the distribution of non-working days.

This paper fulfils an identified need to investigate how to complete a multi-project portfolio with the minimum cost while ensuring engineering quality under a practical multi-project scheduling environment.

The purpose of this paper is to construct an index for systemic risk in China.

This paper develops a systemic risk index for China (SRIC) using textual information from 26 leading newspapers in China. Our index measures the systematic risk from 21 topics relating to China’s economy and provides narratives of the sources of systemic risk.

SRIC effectively predicts changes in GDP, aggregate financing to the real economy and the purchasing managers’ index. Moreover, SRIC explains several other commonly used macroeconomic indicators. Our risk measure provides a helpful monitoring tool for policymakers to manage systemic risk.

The paper construct an index of systemic risk based on the information extracted from newspaper articles. This approach is new to the literature.

This paper aims to enhance the predictability of stock returns. Existing studies have used investor sentiment to forecast stock returns. However, it is unclear whether high-frequency intraday investor sentiment can enhance the forecasting performance of low-frequency stock returns.

Thus, we employ the MIDAS model and the high-frequency intraday sentiment extracted from the Internet stock forum to forecast Chinese A-shares returns at daily frequency.

The results illustrate that high-frequency sentiment data are better than daily sentiment data in predicting daily stock returns, and the sentiment in non-trading hours has been proved superior to those in trading hours.

First, our study adds to the growing literature on investor sentiment. We are the first to construct a proxy for high-frequency investor sentiment using intraday postings collected from Chinese Internet stock forum. Second, we confirm that sentiment in non-trading hours has a stronger predictive ability than those in trading hours. Third, we also contribute to the performance comparison of MIDAS-class models. The good performance of U-MIDAS is confirmed in our empirical applications.