Based on the dataset of branches of China's banking institutions, this paper studies the impact of inter-regional banking integration on economic synchronization between regions in China. We find that the rise of inter-regional banking integration significantly narrows the gaps of economic growth between regions and promotes the economic synchronization between regions while major four state-owned commercial banks play a primary role. After addressing the endogeneity concern by using the instrumental variable based on city commercial banks' restructuring and mergers over years, the benchmark conclusion remains robust. When employing earthquakes and COVID-19 outbreak as risk shocks, this paper finds that China's banking system, especially major four state-owned commercial banks, can significantly share the negative shocks of disasters on disaster-stricken areas' economy. This paper deepens the research on the role of the banking system in China's macro-economy, and explains the influence of the banking system on economic synchronization between regions from the perspective of risk sharing. This paper provides evidence for the financial support for the real economy and regional economic synchronization in China.

This paper takes the outbreak of the COVID-19 at the beginning of 2020 as a quasi-natural experiment to study the impact of public health emergency on corporate cash holding decisions using a difference-in-difference model. We find that firms severely affected by the epidemic have significantly increased their cash holdings compared to other firms. The increased cash holdings mainly come from the reduction of cash flow expenses from operating activities and financing activities. In the process of adjusting cash holding decisions, firms have shown strong preventive motive, holding more cash to prevent future cash flow shortages from affecting the normal production and operation, while the agency motive for cash holdings is not obvious. Heterogeneity analysis shows that management's behavior of increasing cash holdings during the epidemic period is manifested in both state-owned enterprises and non-state-owned enterprises, and is more obvious in regions with lower marketization level. Further research finds that the epidemic doesn't cause pessimism among managers, but they express concerns about the uncertainty of firms'  future development. This uncertainty mood plays an important role in the process of public health emergency affecting firm cash holdings. Finally, research shows that the behavior of firms maintaining higher cash holdings can cause more positive market reaction.

Based on the data of China's high-tech firm from 2010 to 2019, we measure the innovation strategy implemented by firm according to the relative proportion between independent innovation and technology import, and empirically test the impact of performance-vested equity incentives on firm innovation strategy. The results show that the more stringent the performance-vesting conditions are, the more firm tend to choose the technology import strategy. The study on the interaction of contractual terms of equity incentives found that the greater the intensity of equity incentives, the shorter the exercise validity and the listed firm that grant restricted stock can play a better role in promoting the technology import strategy. Further research points out that the impact of performance-vested equity incentives on firm innovation strategy is stronger in firm with high level of information asymmetry, serious agency problem and high separation of two rights. This paper expands the optimal contract theory, analyzes the internal logic of senior management choosing different innovation strategies, enriches the relevant literature on the economic consequences of performance-vested equity incentives, and also provides policy suggestions and practical enlightenment for high-tech firm to optimize equity incentives and drive independent innovation.

Based on the newly constructed input-output model that distinguishes firm size categories (LMS model) and the structural decomposition analysis (SDA) method, this study quantitatively calculates the employment effects and determinants across three firm size categories in China's dual circulation, from the perspectives of "aggregate", "efficiency" and "increment". Empirical results show that:1) In 2007 and 2012, the employment generated by domestic demand across all firm size categories is higher than foreign demand both in total and per unit of domestic demand. 2) In respect of firm size categories, small and micro enterprises are the critical force in stimulating the employment generated by domestic demand; Meanwhile, the employment generated by per unit of final demand (both domestic demand and foreign demand) of small and micro enterprises has a more significant role in driving the employment than large and medium sized enterprises. 3) The change of the final demand scale is the key driving force for employment growth across all firm size categories. This paper demonstrates the practical significance of actively expanding domestic demand in promoting China's employment growth under the new development pattern of "dual circulation" from a quantitative perspective, and provides scientific support for formulating reasonable and effective employment policies.

In perspective of tournament theory, this paper develops a principal-agent model with standard economic theory from the objective reality of congenital differences, to analyze the evolutional mechanism of unbalanced and inadequate development, and to design the policy logic of common prosperity. The main findings include the following. Firstly, the unbalanced and inadequate development will evolve in the trend of becoming serious without any policy regulation, which plays a negative role in common prosperity. Secondly, the preferential treatment of tournament should be applied to help the weak in the congenital disadvantageous position, but not fill in the congenital differences completely. Thirdly, the distribution mechanism based on the preferential treatment should be regulated to widen the allocation gap and increase the aggregate of tournament. Finally, the combined policies of preferential treatment and regulated distribution mechanism can resolve the problem of unbalanced and insufficient development simultaneously, and thereby promote the common prosperity.

Financial technology (FinTech) is critical in empowering banks' reform and innovation, especially in risk management activities. This study uses textual mining methodology to collect and identify the FinTech patent applications at the city level and creatively establishes the local FinTech development index. With the annual data of 148 commercial banks from 2013—2018 in China, this paper quantitively examines whether and how local development of FinTech affects financial institutions' risk management capacity. The main findings show that the FinTech development can significantly reduce the banks' risk, and more significantly in medium-sized banks. This paper employs the amount of news on FinTech related keywords in the Baidu searching engine at the city level as the instrumental variable to alleviate the endogeneity problem and finds robust results. The empirical findings also remain robust after using other alternative measurement on main variables and controlling spatial spillover effects. The mechanism analysis verifies that local FinTech development can significantly reduce the information asymmetry, promote business expansion, and strengthen the banks' risk management ability, thus contributing to the reduction of banks' risk. Moreover, the local regulatory authority efforts and the citizens' awareness of credit consciousness will increase the power of FinTech in risk management. Under the circumstance of the emerging FinTech and its pervasive adoption in commercial banks, profoundly understanding whether and how the FinTech development affects bank risk management will prevent and dissolve the financial risks and contribute to the high-quality development in the financial industry.

To capture the "leptokurtosis and fat-tail", the long memory, and other fractal features of financial asset prices, in this paper, we use the time-varying sub-mixed fractional Brownian motion with GARCH structure to describe the dynamic change of risk asset price, besides, the martingale pricing theory is used to obtain the closed form of option price, which extends the traditional BS and fractional Brownian motion model. The US S&P500 Index Option, Korea KOSPI200 Index Option, China SSE 50 ETF Option, China Hong Kong Hang Seng Index Option, China Taiwan Index Option, and India NIFTY Index Option are used to conduct the empirical studies. Our finding shows that the time-varying sub-mixed fractional Brownian motion with GARCH structure has higher pricing accuracy than to BS and sub-mixed fractional Brownian motion model, which has particularly prominent pricing performance in the emerging markets. This research results have important theoretical and practical implication in several aspects, including the reasonable pricing of option, risk management and investment decision-making, and it also contributes to the development of multi-level capital market.

The massive product experience and feedback in open innovation community could provide the inspiration of product innovation for enterprise. However, with the explosive growth of unstructured data, traditional opinion mining technology could no longer meet the real demand in terms of effectiveness and efficiency. To this end, this paper proposes a more efficient and timely method for idea extraction using deep learning algorithms. Specifically, firstly, this paper builds a multi-embedding layer CNN model with Dropout mechanism, called ME-CNN, to enhance the local feature extraction and to identify posts containing creative ideas. Then make full use of the feature of Transformer in capturing long-distance dependencies, and the strength of CNN in capturing local semantic information, we introduce a combined model, called TF-CNN, to achieve valuable sentences excluded non-creative texts. Finally, the hierarchical aggregation clustering method (HAC) is used to cluster creative ideas. Through experiments using real data, the results show that our proposed method performs well in extracting users' creativities, which can help enterprises obtain users' ideas from open innovation community more efficiently, and provide efficient decision support tools for product innovation.

Employing a weekly sample of Chinese A shares from 2002 to 2017, this paper first computes the sector volatility index for 76 sub-sectors. Based on these indices, a global risk network among sectors is then constructed through a high-dimensional time-varying parameters vector autoregressions (HD-TVP-VAR) model. Afterwards, this paper further examines the systemic importance of financial industries in the risk network from both network topology and empirical analysis perspectives. Results show that, risk spillovers among sectors in Chinese stock market exhibit a complex network structure featured with high connectedness. Specifically, the outbreak of risk events is closely related to the changes in spillover connectedness from financial industries to real economy industries. Second, financial industries play an important role of absorbing risk, with banking being the major risk taker, while the security and future sector being the major risk spreader. Third, the increase in connectedness, transferring from risk-taker to risk-spreader, and the destruction to the stability of network equilibrium are main driving forces underlying systemic financial risk. Forth, financial industries have a greater systematic importance than real economy industries, within which, banking ranks on the top, followed by the security and future sectors. Fifth, real estate sector serves as a risk "bridge" between financial industries and real economy industries which magnifies risk from both sides. Last, high leverage and high liquidity will stimulate risk diffusion, but large scale and heavy investment will help to strengthen risk absorption.

Blockchain technology is triggering the transformation of supply chain finance, and credit transmission function is most widely used in supply chain finance. Consider a three-tier supply chain consisting of a fully funded manufacturer, a capital constrained distributor and a capital constrained retailer. There are two ways to solve the financial dilemma of the distributor and the retailer, one is the combination of trade credit and supply chain finance, and the other is the blockchain supply chain finance driven by credit transfer function. First, this paper uses the Stackelberg game method to describe the traditional supply chain financing model and the blockchain supply chain financing model. Second, the backward induction method is used to optimize and solve the financing models, and the optimal wholesale price, distribution price and order quantity decisions of the two financing modes in the equilibrium state are obtained. Third, the sensitivity analysis of key parameters such as the retailer's initial capital amount and the time value of enterprise capital are conducted. The two models of traditional supply chain financing model and blockchain supply chain financing model are compared. The results show that:The expected profits of the manufacturer, the distributor, and the retailer are increasing, decreasing, and decreasing with respect to the decrease of the retailer's initial capital amount. Compared with the traditional supply chain financing model, the blockchain supply chain financing model creates value for the supply chain. And when the time value of the capital is high, blockchain supply chain finance creates greater value for the supply chain, and achieves a win-win situation among the manufacturer, the distributor and the retailer. This study demonstrates the operational advantage of the supply chain finance model driven by blockchain technology, and provides decision supports and management implications for the development of blockchain supply chain finance.

The group recommender system has become an important tool of social platforms to provide personalized and satisfied products or services for groups. However, existing methods of group recommendation mainly focus on improving the personalized recommendation methods, not only ignoring the interaction of users and groups, but also neglecting the dynamics of user preferences and group preferences. These interaction process and dynamic evolution are essential to group recommendation. Therefore, this paper proposes a dynamic group recommendation method based on the co-evolution of user preferences and group preferences to model the dynamic interaction between users and groups. Specifically, we model the user preferences as a weighted aggregation of user historical preferences and group influence, and model the group preferences as a weighted combination of group historical preferences and new members' preferences. Finally, we aim to predict users' joining behaviors and group consumption behaviors. We also carry out extensive experiments using real data to evaluate the effectiveness of our model. The experimental results show that the proposed model not only achieve better performances on predicting both joining and consumption behaviors, but also is robustness.

With the increasing scale of large-scale online supermarkets, it has to set new warehouse layouts to stock enormous kinds of goods, e.g., multiple warehouses in one city, multiple warehouses in multiple cities. As a result, a customer's order with multiple types of items is separated into several suborders, which are picked and packed in different warehouses, and then delivered to the customer independently. It will bring about some challenges (high cost, high pollution, high disturbance to customers) for green and healthy development of e-commerce. The package consolidation for split orders among multiple warehouses is an important way to solve this problem, and also an urgent task to improve the performance quality of split order fulfillment. This paper aims at the decision-making problem of package consolidation for split orders in large-scale online supermarket. In order to improve the scientificity and efficiency of online generation of package consolidation schemes for split orders, it focuses on decreasing the difficulty of problem solution, synthetically applying the theories of combination optimization and state-space searching. A two-stage online decision-making method of intelligent optimization for split order package consolidation is proposed to solve three key issues of whether split orders can be consolidated and packaged, which warehouses can be selected as package consolidation centers for each order, and which goods can be consolidated and packaged. In the first stage, the main influencing factors of the split order package consolidation are summarized and the decision rules are designed to decide whether to consolidate. In the second stage, for the split orders that need to be consolidated and packaged, the state-space search algorithm is proposed to generate the alternative package consolidation schemes, and the qualitative control strategies are transformed into control rules and incorporated into the scheme generation process, thereby reducing the solution space. Finally, numerical experiments show that the decision-making method of package consolidation can effectively reduce the fulfillment cost of split orders of online supermarket, and the theoretical results can provide a reference for operational activities of online supermarket or other B2C e-commerce enterprises with serious order splitting situation.

The performance of decision making by artificial intelligence has exceeded the capability of the human being in many specific domains. Countries like China and the USA have promulgated artificial intelligence development strategies and action plans to encourage the applications of artificial intelligence. In the artificial intelligence decision-making process, the inherent black-box algorithms and opaque system information lead to highly correct but incomprehensible results, which hinder the further development of artificial intelligence. For the commercialization and popularization of artificial intelligence, the need for explainability of intelligent decision-making is becoming more and more urgent. It is necessary to study the transformation of black-box decision-making into a transparent process and establish trust between humans and machines. From the perspective of system application and decision beneficiaries, this paper focuses on the domestic and foreign-related research on four aspects:The basic concepts of explainable artificial intelligence decision-making, explanation methods of black-box models, applications of explanation methods in high-risk domains and explanation methods evaluation. Meanwhile, we state insights into future research and development trends.

Using A-share listed companies as a sample, this paper investigates peer effects in corporate cash dividends policy from the perspectives of propensity and amount of dividend payments. We find robust evidence that firm's cash dividend payment propensity is significantly influenced by the policies of their industry peers. Moreover, peer influence on cash dividend payment is more pronounced among high-growth and low-cash flow firms. Meanwhile, we find little evidence about peer effects in dividend payments amount. These results indicate that under the background of semi-mandatory dividends rules, firms with high growth and low cash flow have to consider their peers' dividend policy and respond properly to obtain refinancing qualifications. However, what firms focus on is not their peers' payout amount but their propensity to pay dividends. Further research shows that information learning, industry rivalry and CEO reputation concerns are main driving factors of peer effects of corporate cash dividends policy. This paper helps us get better understanding of corporate payout policy in special institutional background and enriches corporate peer effects research.

In recent years, the rapid development of shadow banking not only benefits the economic society, but also brings certain risks to the stability of the financial market. It is of great practical significance to deeply study the risk transmission channels of shadow banks and to put forward appropriate regulatory countermeasures for timely resolving financial risks. Through the structural vector autoregression (SVAR) model based on directed acyclic graph (DAG), the dynamic risk transmission among the shadow banks and commercial banks, money supply, securities/bond market, real estate and macro-economy has been investigated in this work, and the risk contagion of shadow banks from three transmission channels:Monetary policy, asset price and real economy has also been studied. The results demonstrate that, whether short-term or long-term, the risk of shadow banks is mainly affected by monetary policy channels, that is, shadow banks will amplify the transmission effect of monetary policy to a certain extent, and when commercial banks are subjected to risk shocks, shadow banks will be more likely to be exposed to risk. In the short term, the risk of the shadow banks will be transmitted to the real economy through the exchange rate, while in the long term, the risk of shadow banking will be conducted to the bond market through the asset price channel.

This paper studies a supply chain consisting of a manufacturer, a retailer, and consumers with low-carbon environmental awareness, in which the retailer is the leader and need to choose among three possible channel structures:A pure physical channel, a pure online channel, and dual channels. This paper builds a supply chain model considering the differences in both the unit carbon emission difference and channel operating cost between physical channel and online channel, and focuses on studying the retailer's channel strategy choice. We show that the unit carbon emission difference between physical channel and online channel is one of the significant factors driving the retailer's optimal channel strategy choice. In particular, given other factors, the retailer's optimal channel strategy will change from pure physical channel, to dual channels, and to pure online channel, as the unit carbon emissions level of physical channel relative to online channel increases. Meanwhile, the effect of unit carbon emission difference between channels on the retailers' channel selection strategies will be moderated by the difference in channel operating costs between channels. Furthermore, this paper also analyzes the retailer's optimal pricing decisions and find that when the retailer chooses a dual-channel strategy, the retailer should reduce the optimal retail price of the physical channel with the increase of the unit carbon emissions level of physical channel relative to online channel and the optimal online retail price depends on the physical channel's unit carbon emission level relative to the online channel. Further, the retailer can influence the manufacturer' optimal wholesale price by adding new online channel, thereby increasing the retailer' profits.

Considering the deficiency of existing propagation dynamics models, as well as the transmission characteristics of 2019 novel coronavirus (2019-nCoV) and prevention-control measurements, a new infectious disease model is established, which included eight compartments, i.e. susceptible, uninsulated, isolated, diagnosed, asymptomatic, cured after diagnosis, cured after asymptomatic and died of disease. In order to simulate the pressure of a large number of isolated patients on the operation of isolation points and the pressure of a large number of confirmed patients on the operation of hospitals, the saturation characteristics of state transfer parameters are considered in the model. In the aspect of parameter configuration, the time-varying characteristics of state transition parameters in different stages of epidemic development are analyzed. A parameter identification model is established and solved by Markov chain Monte Carlo algorithm. In addition, the risk indicators of epidemic transmission are established from multiple dimensions to comprehensively assess the risk of epidemic transmission. Through the case studies of coronavirus disease 2019 (COVID-19) in Wuhan and foreign countries (America and Spain), the results show that the calculated results are in better agreement with the official data and reveal the mechanism of epidemic transmission compared with other classical dynamics models.

Aiming at the problem that how to aggregate group DEMATEL evaluation information, this paper takes complex network as the breakthrough point and proposes a new large-scale group DEMATEL decision making method form the perspective of complex network. Firstly, the decision makers are regarded as nodes and a complex network is constructed by calculating the consensus degree of the decision maker's DEMATEL evaluation matrix. Secondly, the network condensation degree and subgroup condensation degree are designed to measure the node weights and subgroup weights. Finally, group DEMATEL evaluation matrix is aggregated by the network density operator and the multiple attribute decision making is implemented by using the DEMATEL method. One numeral example is used to illustrated the feasibility and rationality of the proposed method. The results show that this method can aggregate large-scare group DEMATEL evaluation information more comprehensively and accurately, and the stability of this method is more better.

As green logistic emerged as a new trend, green vehicle routing problem (GVRP) has received wide attention from related fields, but literature reviews on the latest research of GVRP remain rare. Awared of this fact, this paper is intented to review several typical GVRP models and their solving algorithms. Firstly, an elementary GVRP model and several fuel consumption/carbon emission measuring methods are briefly described. Secondly, according to the optimization of environmental benefit and the composition of objective function, GVRP models are classified into tree types, i.e. fuel consumption/carbon emission minimization VRP, comprehensive cost minimization VRP and multi-objective VRP. Each model is discussed from four aspects, namely optimization objective, factors influencing fuel consumption/carbon emission, measurement models of fuel consumption/carbon emission and constraints. Then, some solving methods about GVRP such as exact algorithms, heuristic algorithms and metaheuristic algorithms are briefly introduced, and several widely used metaheuristic algorithms are analyzed. Finally, through presenting new applications of GVRP in just-in-time logistics distribution, cold chain logistics distribution, electric vehicle logistics distribution and joint logistics distribution, this paper points out the growing trend of theory and practical method of GVRP.

To solve collision-free path planning problem for automated guided vehicles (AGV), an improved A^* algorithm with time factor was proposed to reduce turning time. In addition, combining this new approach with time window and priority strategy could help to transfer the concept of the dynamic collision-free path planning for multiple AGVs into practice. Firstly, the improved A^* algorithm was used to statically plan the path of each AGV with least turning times. Secondly, the arrival time and safety time interval of path nodes were analyzed, and priority was dynamically assigned to multiple AGVs based on power and path performance. Combining with the time window model, the collision problem for AGVs is solved and system efficiency is improved. The results of the case study show that this algorithm not only guarantees the path optimality, but also solves the problem of multiple turning times caused by the traditional A^* algorithm. It can effectively realize the system scheduling without repetition and conflict, and proves its good adaptability and robustness in the dynamic environment.

“Blockchain+” business model empowers companies with practical significance, but lacks systematic and quantitative review guidance. This paper takes domestic and foreign literature data as the source, combines descriptive statistics, cooperative analysis, co-occurrence network analysis and cluster analysis to explore the current research status and research hotspots, and taps future research trends and evolutionary trends. It shows:1) Domestic and foreign research started late, but the research situation is gratifying. Domestic research monographs are obvious, but foreign research cooperation is profound; 2) It emphasizes the integration of Internet of things, big data and other emerging technologies, and effectively empower business model innovation research; 3) There are significant differences between domestic and foreign research. Domestic focus on financial research, foreign research explores of multi-industry business model innovation and subversion. Finally, a “blockchain+” business model research framework is constructed. The research ideas and conclusions of this paper are enlightening to academic research and practical application of “blockchain+” business model.