This study empirically explores the impacts of Household Financial Literacy and user preferences on holding Central Bank Digital Currency using data from the China Household Finance Survey. Firstly, we construct a mobile payment behaviour utility model and an adoption probability model to calculate the mobile payment probabilities of different households. Then, we empirically study the relationship between the mobile payment adoption probability and the digital currency usage volume, which is an inverted U-shaped relation. The results show that the influence of user preferences on the demand for digital currency exhibits a dynamic effect. The changes in user preferences at different stages can be used to identify the bottlenecks in the promotion of digital currency.

Tax incentives, as an important auxiliary means for the government to stabilize the market, are crucial in motivating corporations to move from virtual to real. This article takes Chinese listed companies from 2008 to 2023 as the research object, analyzing the governance effect and internal mechanism of tax incentives on the over-financialization of real corporations. The results showed that tax incentives have a significant inhibitory effect on the over-financialization of real corporations by narrowing the cross-industry arbitrage gap. Compared to value-added tax incentives, income tax incentives have a stronger governance effect on the over-financialization of physical corporations. The impact of tax incentives on corporate behavior is not linear. Therefore, to avoid exacerbating financialization through a one-size-fits-all approach, value-added tax policies should be designed differently based on the level of corporation income tax preferential treatment obtained by the corporation. Finally, the subsample analysis shows that the inhibitory effect of tax incentives on over-financialization is more pronounced for non-SOEs, mature-to-declining corporations, and high-marketization regions. This study provides guidance for the government to implement tax incentives and thereby stimulate corporations to move from virtual to real.

Abstract This study provides a comprehensive review of machine learning (ML) applications in the fields of business and finance. First, it introduces the most commonly used ML techniques and explores their diverse applications in marketing, stock analysis, demand forecasting, and energy marketing. In particular, this review critically analyzes over 100 articles and reveals a strong inclination toward deep learning techniques, such as deep neural, convolutional neural, and recurrent neural networks, which have garnered immense popularity in financial contexts owing to their remarkable performance. This review shows that ML techniques, particularly deep learning, demonstrate substantial potential for enhancing business decision-making processes and achieving more accurate and efficient predictions of financial outcomes. In particular, ML techniques exhibit promising research prospects in cryptocurrencies, financial crime detection, and marketing, underscoring the extensive opportunities in these areas. However, some limitations regarding ML applications in the business and finance domains remain, including issues related to linguistic information processes, interpretability, data quality, generalization, and the oversights related to social networks and causal relationships. Thus, addressing these challenges is a promising avenue for future research.

As a strategic lever, internal carbon pricing allows enterprises to cope with potential climate-related risks and motivate their low-carbon transformation. But internal carbon pricing, still in its early exploratory stage, lacks standard criteria for optimization. The traditional goal of cost minimization or profit maximization is not directly applicable to internal carbon pricing because internal payment for carbon emissions does not affect enterprises' aggregated costs or profits. How to optimize internal carbon prices to reduce carbon emissions more efficiently becomes an important issue. This study proposed a grouped internal carbon pricing method considering both internal carbon pricing and comprehensive efficiency. We found that there exists an optimal partition of internal units with corresponding differentiated carbon prices. Compared with other internal carbon pricing methods, the average efficiency of differentiated carbon pricing was significantly higher than the average efficiency of uniform carbon pricing. We then applied the method to a large coal producer with 16 subdivisions in China. The results showed that if the enterprise adopted the grouped internal carbon pricing under its current technology level, the carbon emissions would reach an optimal level, and its comprehensive efficiency would be significantly improved.

Customer satisfaction is a matter of significant concern for businesses, as it can offer valuable insights for product or service sales strategies and improvements. However, most customer satisfaction analysis methods collect customer satisfaction data directly in the form of scales, which may deviate from the ideas customers want to express. We establish a framework that transforms customers’ natural linguistic expressions into satisfaction linguistic terms and maps the semantics of these satisfaction linguistic terms into corresponding intervals. On this basis, this paper extends the multi-criteria satisfaction analysis method to analyze the satisfaction of multiple products or services at the same time.

This study examines the influence of macro-prudential policies on risk-taking among systemically important banks using unbalanced panel data from 126 commercial banks in China between 2010 and 2021. Under a difference-in-differences setup, the empirics demonstrate that macro prudence in China effectively enhances large banks’ risk prevention measures and risk-mitigation efforts. Specifically, macro-prudential policy implementation facilitates the digital transformation of banking and subsequently reduces risk-taking behavior. According to heterogeneity test results, the concerned effect becomes more significant for systemically important banks having higher capital adequacy ratios. Moreover, the most important banks with strong interbank dependence exhibit more pronounced changes in risk profiles responding to stricter capital supervision requirements. Our findings shed light on the economic consequences of monitoring strengthening from the perspective of regulatory authorities.

The fundamental goal of group decision making (GDM) is to improve consensus amongst experts and reduce individual conflicts of interest in the process of alternative selection. By analysing the social network relationships of decision-makers (DMs), such as trust and preference similarity relationships, more effective consensus-reaching mechanisms can be designed. Previous studies developed supervised classification algorithms for DM groups of more than 1000 participants on social networks. However, in large-scale social networks, the social connection relations of DMs cannot be completely investigated as human resources and costs during the decision-making process impose limitations. These DMs cannot be effectively trained to build a classification model, which is referred to as unlabelled DMs and results in a large amount of information being ignored during the model training process, leading to an unfair process that can deepen the conflict amongst experts. To address the information loss problem, this study proposes a semi-supervised learning model for social networks with incomplete trust relations, aiming to effectively absorb the preference information of unlabelled DMs into the classification model, thereby improving the effectiveness of classification management. Specifically, the cost-sensitive semi-supervised support vector machine (SVM) introduced in the inner product space is subsequently used by the DMs' classification model to accurately divide unlabelled DMs. A minimum cost adjustment consensus model is then constructed based on the DM subgroups. Numerical experiments on urban renewal were conducted to verify the effectiveness of the proposed method. The empirical and simulation results demonstrated that the proposed method can decrease total consensus costs for social-network GDM with missing trust-relationship information.

Individuals’ emotions, such as hesitation and unwavering confidence, can influence the ability of decision-makers (DMs) to make rational judgments. The emotion is always hidden in individual preference series, which is referred to as emotion soft factors, It is a prerequisite for avoiding unfavorable impacts on consensus reaching process. This study focuses on structuring a consensus model with emotion soft factors in linguistic preference time sequence. Specifically, a personalized individual semantics (PIS) learning process is implemented to obtain the personalized numerical scales of DMs’ linguistic terms. Subsequently, we propose a consensus model incorporating the consensus measurement and feedback modification phase. In the process, a grey clustering scheme is devised to mine emotion soft factors from DMs’ preference sequences and manage individuals in different grey classes. Finally, numerical examples, simulation analysis, and comparison study are presented to illustrate the influence of different parameters and justify the validity of the proposed model.

In a problem of linguistic group decision-making (GDM), various emotional characteristics of decision-makers' (DMs) behind their preference series, which is defined as emotion factor, can impact consensus reaching process. As words mean different things to different people, this study comprehensively proposes a novel consensus model with emotion factors based on personalized individual semantics. An improved grey clustering method facilitates to obtain emotion factors. Finally, the proposal model can provide more accurate judgments for moderator's management and further promote consensus.

When making decisions, individuals often express their preferences linguistically. The computing with words methodology is a key basis for supporting linguistic decision making, and the words in that methodology may mean different things to different individuals. Thus, in this article, we propose a continual personalized individual semantics learning model to support a consensus-reaching process in large-scale linguistic group decision making. Specifically, we first derive personalized numerical scales from the data of linguistic preference relations. We then perform a clustering ensemble method to divide large-scale group and conduct consensus management. Finally, we present a case study of intelligent route optimization in shared mobility to illustrate the usability of our proposed model. We also demonstrate its effectiveness and feasibility through a comparative analysis.

This study investigates whether and how the two risk control strategies inhibit credit risk contagion among enterprises in the network. Learning from the corporate governance of enterprises, we propose internal and external strategies, respectively. Moreover, two improved epidemic models containing internal and external strategies strategy are established to investigate the impact of the two strategies on the risk contagion. The control effect of internal and external strategies is compared through simulation analysis. The results indicate that the enterprise's financial health, the cost and ability to apply the strategies are important factors affecting the control effect of strategies. For enterprises with general financial status (susceptible enterprise), when the costs of the two strategies are the same, they should choose external strategies. For enterprises with good financial status (immune enterprises), the external strategy is a more sensible choice. Our study provides new insights into controlling risk contagion in enterprise networks.

Environmental regulation is the principal policy for the deterioration of the ecological environment, but it can also interfere with the green development efficiency of resource-based cities. However, the relationship between regulation and development is still unclear and how to realize the sustainable development of resource-based cities under environmental regulation becomes an important topic. This paper investigates the green economy efficiency of resource-based cities using the super-SBM model based on the panel data of 98 resource-based cities in China from 2006 to 2016. Then, we empirically test the effect of environmental regulation using the SYS-GMM estimation method and threshold model. This study finds that the green economy efficiency in different categories of resource-based cities is significantly different. Environmental regulation has a significant positive effect on the green economy efficiency in resource-based cities and significantly promotes the green economy efficiency in growing cities. The policy also plays a certain role in improving the green economy efficiency in grow-up cities, but the effect is not significant. In addition, environmental regulation has a negative impact on the green economy efficiency of recessionary cities and regenerative cities. Finally, some policy suggestions are put forward to realize the sustainable development of resource-based cities.

The operational challenges of shared parking platforms include heterogeneous sharing time intervals and two types of customers randomly providing demand information. Thus, the operation of shared parking platforms necessitates a more intricate reservation strategy than those of the hotels and leasing industries. To address the problem, this study proposes reservation control strategies in a shared parking system with two types of customers and then allocates capacity among customers. Firstly, we propose a dynamic programming (DP) model with dynamic characteristics of demand information and prove that the boundary condition of the model is NP-hard. Secondly, period- and product-based decomposition models have been proposed to approach the DP model. The objective functions of the period- and product-based decomposition models are concave and supermodular, respectively. We also propose three approximation algorithms to obtain reservation strategies based on decomposition models. Finally, several numerical experiments are conducted to verify the effectiveness of the proposed models and algorithms. Extended experiments are conducted to test the robustness of the method for real-world applications. The results support reservation control in shared parking systems.

It's beyond disputed that everyone has their own unique understandings of words, which induces personalized individual semantics (PISs) attached to linguistic expressions. Social network illustrates trust relationships among group members. In linguistic social network decision making (SNGDM), social network analysis usually aids in determining the importance weights of decision makers (DMs). Actually, emotions may have an impact on trust propagation. For instance, positive emotions would strengthen trust whereas negative emotions work in a reverse way. Thus, a social network with individual emotions is firstly developed. Generally, DMs are frequently required to modify their opinions in consensus reaching process (CRP). To some extent, emotions also reflect their adjusted probabilities. Meanwhile, DM's willingness and attitude to making necessary modifications for better agreement, that is measured by effort degree, will bring to different consensus results. Under a limited cost budget, a maximum effort consensus model driven by maximizing all DMs' effort degree is proposed for instructing feedback regulation.

Financial regulation is the basic requirement for financial stability. Recently, regulatory technology (Reg-Tech) has become one of the main research topics in financial stability regulation. Reg-Tech aims to use artificial intelligence technologies to realize intelligent identification and early risk warning. It is a powerful tool for assisting financial regulation informatization and high efficiency. This study aims to comprehensively review the application of smart technology in financial stability regulation, and analyze the objects and results of the technology's applications. We build a framework for the application of complex networks, knowledge graphs, machine learning, and dynamic systems in Reg-Tech. The aim is to form a clear context for its development, and serve as the support and development foundation for financial stability research. Finally, we summarize the limitations and shortcomings of current Reg-Tech developments, and discuss future research and development directions.

This paper examines the forecast horizons under the settings of two-echelon dynamic lot-sizing models with durable and perishable products, where the first echelon also has its own external demands. In the model of durable products, inventory costs are age-independent. In the model of perishable products, inventory deterioration and costs are age-dependent. On the basis of certain significant properties in an optimal solution, forward dynamic programming algorithms are developed for solving the two models of durable and perishable products. Furthermore, we establish monotone properties of production and regeneration points and provide sufficient conditions to obtain forecast horizons under two cases of durable and perishable products. By conducting several numerical experiments, managerial insights are obtained regarding the influence of integrated decision, external demands at the first echelon, perishability and costs’ parameters on forecast horizons. A rolling horizon decision-making is an easy-to-use practice in dynamic lot-sizing problems. We test the performance of the rolling horizon approach in a numerical experiment.

The linguistic information of decision makers (DMs) often implies individual preferences and behavioral traits. Reaching consensus in linguistic group decision making (GDM) requires understanding the overconfidence behaviors exhibited by DMs and implementing effective management. However, owing to the differences in individual expression and understanding, the management of the overconfidence behaviors of DMs must effectively handle heterogeneous preference information and personalized individual semantics (PIS). To solve this problem, this article manages overconfidence behaviors exhibited by DMs in linguistic GDM by considering PIS and multiple self-confidence levels. Specifically, DMs utilize flexible linguistic representation models to provide preference values over alternatives and corresponding self-confidence levels, thereby generating heterogeneous preference relations (HPRs) with self-confidence (PRs–SC). We then integrate HPRs into unified linguistic distribution expressions. In addition, we transform linguistic PRs–SC into additive PRs–SC using a PIS model. We detect and manage individuals’ overconfidence behaviors to decrease the negatively impact of overconfidence on consensus efficiency and the decision quality. Subsequently, DMs’ preference values and SC levels are utilized to generate modification suggestions to achieve a consensus. Finally, we use a loan selection on a peer-to-peer lending platform as an example to illustrate and demonstrate the usability, effectiveness, and feasibility of our model through a simulation and comparative analysis.

Security against systemic financial risks is the main theme for financial stability regulation. As modern financial markets are highly interconnected and complex networks, their network resilience is an important indicator of the ability of the financial system to prevent risks. To provide a comprehensive perspective on the network resilience of financial networks, we review the main advances in the literature on network resilience and financial networks. Further, we review the key elements and applications of financial network resilience processing in financial regulation, including financial network information, network resilience measures, financial regulatory technologies, and regulatory applications. Finally, we discuss ongoing challenges and future research directions from the perspective of resilience-based financial systemic risk regulation.

Words representing individual preferences in group decision-making (GDM) are always associated with different meanings. Consequently, mining personalized semantics of decision-makers (DMs) hidden in preference expressions, and establishing a corresponding management mechanism, is an effective way to reach group consensus through computing with word methodology. However, the aforementioned consensus-reaching process may be hindered by self-confidence. To address this limitation, this study proposes a linguistic group decision model with self-confidence behavior. First, we identified the corresponding self-confidence levels for each DM. Next, we integrated different linguistic representation models into unified linguistic distribution-based models. We then obtained individual personalized semantics based on a consistency-driven optimization method, and designed a feedback-adjustment mechanism to improve the adjustment willingness of DMs and group consensus level. Finally, we conducted a quantitative experiment to demonstrate our model's effectiveness and feasibility.