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.
In this paper, the panel data model is used to analyze the effect of mergers and acquisitions (M&A) on the provinces' economy in China. The empirical results from panel data model show that the coefficient of M&A on each province's economy is significantly positive, indicating that enterprise acquisitions have positive and promoting effect on the economic development of each province in China. Furthermore, Moran's I spatial correlation test and the spatial panel data model show that there is an obvious spatial autocorrelation in the M&A behavior and economic development of Chinese provinces and cities. While promoting the economic development of the provinces and cities where the M&A are located, under the state of economic agglomeration, the M&A of the economically better developed provinces and cities has an obvious positive effect on the economic development of the neighboring provinces and cities through the spatial spillover effect.
Conference call is an important part for the voluntary information disclosure in the listed companies. From the perspective of information asymmetry, this paper studies the impact of the conference call on stock price synchronization of the Chinese listed companies. The results of the study show that the conference call can alleviate the synchronization of stock prices even controlling the endogeneity problem. We find that the longer the Q&A session, the more stronger effect of the conference call on the stock price synchronization. From the perspective of the company's characteristic, we also find that the conference call shows a more significant impact on stock price synchronization in companies with higher information disclosure quality, lower accounting robustness, higher R&D investment, lower institutional investor holding. In addition, we study the influence mechanism of the conference call on stock price synchronization from two perspectives including the executive language characteristics and conference topics. This study provides systematic evidence for the information disclosure effect of the conference call in Chinese capital market.
Investor sentiment affects investors' value judgment, risk preference, and decision-making process. Based on the perspective of marginal cost-benefit analysis, we investigate whether and how investor sentiment, as a type of behavior bias, affects corporate tax avoidance. The results indicate that increases in investor sentiment are associated with increases in corporate tax aggressiveness. In addition, the association is more pronounced for firms which have more retail investors, which are private enterprises, and which are located in areas with weaker tax enforcement. Further evidence shows that increased tax avoidance induced by increases in investor sentiment significantly enhances firms' short-term stock return, but impairs firms' long-term value. Our focus on the impact of investor sentiment on corporate tax aggressiveness enriches the research on the determinants of corporate tax avoidance and deepens the understanding of the cost-benefit trade-off of tax aggressiveness. This paper also has practical implications for regulatory authorities and investor protection.
With further development of market-oriented exchange rate reform in China, RMB has played a more important role on the world stage, and the volatility of RMB exchange rate is also more drastic. In this context, it is of great significance to design an effective exchange rate forecasting method. When the data is sufficient, one of the solutions to the forecasting problem is to find powerful predictors. Based on a number of economic and technical variables, we construct two momentum predictor selection methods according to the momentum assumptions of out-of-sample forecasting ability and in-sample fitting ability, respectively, and compare the monthly forecasting ability of simple model selection methods for the exchange rate of USD and GBP against RMB with those of various other prevalent models. Our empirical results show that the simple model selection methods are significantly stronger than the random walk benchmark model in most cases, and compared with other competitors, the simple model selection methods use powerful predictors more frequently, and can achieve smaller forecasting error and more accurate prediction of the direction of exchange rate change on the whole. In addition, from the perspective of predictor classification and out-of-sample period division, the simple model selection methods deliver more robust performance in contrast with the rival models.
Based on the idea of secondary decomposition and ensemble learning, we build the VMD-EEMD-DE-ELM-DE-ELM model, select soybeans, wheat and rice futures listed on the CBOT exchange as representatives of international grain futures, and predict its future price trend. In view of the existing research that directly ignore the residual items after VMD decomposition, we introduce the idea of secondary decomposition to perform the EEMD decomposition and ensemble prediction of its residual items for the first time. This method can capture the rich information contained in the residual items, thereby helping to improve the model's prediction effect on the original sequence. At the same time, because of the shortcomings of the existing model which use equal weights to reconstruct the prediction results of components, we draw on the idea of ensemble learning and introduces the DE-ELM meta-learner to optimize the reconstruction weights to obtain the best overall prediction results of the model. The empirical results show that the model proposed by us has a significant predictive advantage over the existing models.
The development of the energy internet has continuously strengthened the coupling relationship among different forms of energy sources. Energy hub (EH) is an important system form of multi-energy coordination and complementation in the environment of energy internet. In this regard, an optimal load dispatch model of EH with electricity, gas, heat and hydrogen is proposed, which aims to reduce the total cost of EH by optimizing the output of each unit under various operating constraints. The proposed EH model includes wind turbine (WT), combined heat and power (CHP) unit, gas boiler, electrolytic cell (EC), hydrogen storage unit (HS), thermal energy storage unit (TES) and electrical energy storage unit (EES). Then, a data-driven two-stage distributionally robust optimization (DRO) method is used to deal with the uncertainty of wind energy. And the column and constraint generation algorithm (C&CG) is used to solve the proposed two-stage DRO model. The results show that the proposed data-driven DRO method has better robustness than that of stochastic programming method in dealing with wind power output uncertainty. And compared to the traditional robust optimization method, the proposed method can better realize the economy of load optimal dispatch of EH. Therefore, the proposed optimal load dispatch model plays an important role in coordinating the economy and robustness of EH in energy Internet environment.
COVID-19 epidemic is a major global public health emergency that rarely happened in a century. China has entered the normal stage of epidemic prevention and control after strenuous struggle. Epidemic prevention and control have been promoted synchronously with economic recovery. It is very important that how to realize the effective transmission for epidemic consciousness of prevention and control in the public at this stage of normalization of the epidemic. For this reason, a transmission dynamic model of consciousness of prevention and control in multiplex social networks formed by multiple channels is firstly established. Model analysis and simulation experiments are carried out to draw that it can make the consciousness of prevention and control transmit among public all the time as long as the proportion of owners with consciousness is above a critical value according to the threshold conditions for distinguishing whether the consciousness propagates. It is difficult to quickly raise consciousness of prevention and control for the public that communicating through a single channel. Online and offline multiple information channels are used in a balanced manner in order to maximize the efficiency of transmission. It can promote the transmission of consciousness of prevention and control as much as possible that scientifically and moderately increasing the number of daily communication. Once the number of public communication through multiple channels exceeds a certain limit, it will reduce the efficiency of transmission for consciousness of prevention and control.
Click framing is a tool of vicious competition in e-commerce platform. Sellers affect consumers' purchase decision by making fraudulent sales with click farming, which thus impacts the competition between sellers. A two-period game theory model is adapted to analyze the impact of click farming on the sellers' competition. The results show that, if only one seller employs click farming, when the cost of click farming is high, seller can earn more with click farming; when the cost of click farming is low, the seller without click farming can benefit from the click farming by the other seller. Therefore, even when the e-commerce platform increases the cost of click framing, a seller may still participate in click framing. If both sellers employ click farming, when the cost of click farming is high, the seller with higher "value-cost difference" is more aggressive in click farming and earns more; when the cost of click farming is relatively low, the seller with lower "value-cost difference" is more aggressive in click farming and earns more; when the cost of click farming is further low, the seller with lower "value-cost difference" is more aggressive in click farming but earns less. As a result, the e-commerce platform needs to focus on monitoring the high "value-cost difference" seller under the high click framing cost, but the low "value-cost difference" seller under the low click framing cost. Furthermore, when sellers set click farming strategies, if the cost of click farming is low, the competition will fall into "prisoner's dilemma"; if the cost of click farming is high, both sellers reduce the volume of click farming but can earn more. Hence, it is not harmful but beneficial to sellers for the e-commerce platform to moderately increase the cost of click farming.
This paper investigates the coordination problem between an online platform and a retailer in the omnichannel retailing with the introduction of buy-online and pick-up-in-store (BOPS). We consider customers' cross-buying behaviors that online customers will buy extra products when picking up products in store. In the traditional revenue sharing contract (referred to as RS), the revenue is shared between the upstream and downstream members. However, considering customers' cross-buying behaviors in the BOPS retailing, the downstream retailer gains an extra revenue. Thus, we designed another mechanism-The bilateral revenue sharing contract (referred to as BRS) to coordinate the proposed system. With the BRS contract, the e-commerce platform shares its BOPS revenue with the retailer and the retailer shares its cross-selling revenue with the platform. The results show that:The BRS contract degenerates into the RS contract when the one of parameter equal to 0 and it cannot coordinate the BOPS system, while the BRS contract can coordinate the system when the margin revenue of the cross-selling is low. Furthermore, we discuss another contract, the Mixed contract (referred to as MC), based on the RS and TPT. We find that the BRS contract can coordinate the system when the margin revenue of the cross-selling is low, the MC does when the margin is not too high, and when the margin is below the medium level, whether to adopt the BRS contract or the MC contract depends on the bargain power of the e-commerce platform.
Appropriately upgrading of product quality is an important operating method for manufacturer to maintain his competitive advantages. Similarly, how to keep repurchasing customers and attract new customers must be solved by the retailer in his marketing activities. From the perspective of consumer's repurchasing behavior and retailer's differentiated pricing, this paper considers a two-stage pricing strategy with upgrading product. Using game optimization method, we obtain the optimal decision under different strategies and give the conditions of different pricing strategies. We further analyze the impact on product price, manufacturer and retailer's profits by enlarging market share, the quality level of upgrading product. The results show that the increasing of market share and the quality level of upgrading product can bring more benefits to retailer/manufacturer under certain conditions. Finally, numerical analysis is used to further confirm and deepen the results and corresponding managerial implications are given according to these proposed results.
With the rapid development of e-commerce, various online platforms launch consumer credit services to attract more customers. This new consumption payment method, in turn, affects the e-commerce supply chain members (including online platforms and suppliers)' selling format selections. Under different selling formats (i.e., reselling model and agency selling model), this paper considers an e-commerce supply chain with one platform and one supplier, and builds the Stackelberg game models in which the platform provides cash payment and credit payment for consumers, respectively. We investigate the impacts of credit payment factor and commission rate on the optimal decisions, expected profits, and selling format selections of the platform and supplier. Our main results suggest that:(i) Under the reselling model, the platform's expected profit under credit payment is always higher than that under the cash payment, while the supplier's expected profit under the credit payment would be lower if the credit payment factor is small; (ii) Under the agency selling model, both the platform and supplier can earn more profits under the credit payment. Moreover, their expected profits and commission rate satisfy the inverted U-shaped relationship under the credit payment; (iii) Under the cash payment scenario, it is beneficial for both the platform and suppliers to adopt the reselling (agency selling) model if the commission rate is intermediate (high). Under the credit payment scenario, however, employing the reselling model always benefits the supplier, and both of them would choose the reselling model if the commission rate is sufficiently low or high.
Most attention has been paid to the sharing platform. Under the sharing platform, not only the service time of resource varies, but also the future demand arrival information of users (such as arrival time, start time and duration) presents a high level of uncertainty. How to more effectively realize the optimal matching of resource supply and demand is the key to the operation of the platform. In order to maximize the profit of the sharing platform, we consider the problem of the real-time task assignment problem under the unknown situation of future demand information. Under the premise that the profit rules include not only variable profit per unit but also a fixed profit per unit, an online mathematical model is established. We present a unified proof framework, which can be used to prove the competitive performance ratio in different situations. Furthermore, we design and analyze an online strategy, and use the proof framework to prove the corresponding competitive performance ratio in different situations. The results show that the proposed strategy has a superior competitive performance ratio.
Mobile users usually alternatively use cellular and Wi-Fi, namely hybrid access network, to access Internet. In order to investigate the Nash equilibrium behavior of mobile users and maximize the social benefits of the system, in this paper we present a threshold policy for mobile users. Considering a system model composed of an observable system buffer and an observable connection state, by using an iteration method and a diagonalization method, we give the closed-form solutions for the expected sojourn time of a newly arriving data packet and the Nash equilibrium access threshold in a discrete-time domain. Moreover, by establishing a discrete-time queueing game model with two-stage service, we give the steady-state solution of the system model and obtain the socially optimal access threshold. Numerical results with different service rates show that the Nash equilibrium access threshold is higher than the socially optimal access threshold in a hybrid access network. Aiming to maximize the social benefits of a hybrid access network, additional sojourn cost per unit time should be imposed to mobile users based on the difference between the two access thresholds.
In view of the problem of operating room congestion or idleness caused by the mismatch between the number of patients in each department and the relevant resources in the operating room, we studied the scheduling plan for elective patients in the operating room from the perspective of postoperative resource sharing. The problem consists of two sub-problems, one is how to establish a matching mechanism between departments, and the other is how to arrange operating rooms for elective patients. The specific work is divided into the following parts:First, we apply the rank sum ratio method to evaluate the work intensity of the department, and build a matching mechanism model among the departments with different work intensity to determine the cooperation combination of departments and the flow direction of resources; second, on the basis of department cooperation and changes in department resource service capacities, we further consider the service capacity constraints of multiple resources (department resources and hospital public resources), and construct the model of operating room planning under the resource sharing to maximize the usage of available resources and meet the patient's surgical needs; third, we use the actual data from an upper first-class hospital in a certain province to conduct numerical experiments. We compare the numerical results with the operating room planning under resource service capability constraints of a single department, and the effectiveness of operating room planning proposed in this paper is verified from different perspectives.
In this paper, the problem of vessel scheduling and refueling strategy in container liner shipping with perishable assets was studied considering the difference and discount factors of port fuel price. A mixed integer nonlinear programming model was established, where the total liner shipping route service weekly cost was minimized. A set of piecewise linear secant approach was applied to the original model. Taking the AEU6 route served by CHINA COSCO SHIPPING GROUP as an example, a large number of numerical values of 1 000 scenes were used for simulation calculation. The results show that the proposed model outperforms the existing model, where proposed by Dulebenets & Ozguven (2017), in terms of the total route service weekly cost on average decreased 1.52\% as well as the total asset decay cost on average decreased 1.05\%, respectively. It indicates that the proposed model will help to decrease the total asset decay cost and the total liner shipping route service weekly cost. It makes more flexible for the liner shipping company to design the vessel schedule and make the decision on refuel strategy. It improves the punctuality rate of liner shipping. It makes easier for the liner shipping company to sign a win-win contractual agreement with the marine container terminal operator. The research conclusion can provide scientific reference for the liner shipping company to make decisions on operation in liner shipping with perishable assets.
The introduction of white list system in subway security inspection is a feasible method to improve the speed of subway security inspection. However, its low sampling rate design raises concerns about the lack of the capacity of terrorism related explosion protection of the white list channel. It is necessary to further optimize the security inspection process of the white list system. Combined with the current subway security problems existing under the process of "one-size-fits-all" white list system, this paper first analyzes the explosion casualty loss prediction model of the security hall and waiting hall, on this basis, this paper constructs a sequential game model based on the white list system between the subway security department and the terrorists, and obtains the perfect Nash equilibrium of the sub-game. The results show that the sampling rate of the white list channel is positively correlated with the security and the cost of subway security inspection, and negatively correlated with the speed of subway security inspection; The larger the proportion of white list passengers, the worse the overall passenger flow suitability of subway security inspection; The speed and security of the white list channel are significantly related to the decision of terrorists. In order to improve the security capability of the white list channel, the security department can optimize the basic parameters design of the white list system, promote new technology, coordinate multi-departments, and so on. In order to further control the losses caused by terrorist attacks, the subway department needs to improve the investment level of joint prevention and control, enhance the awareness of mass prevention and control, strengthen the publicity of the control of contraband, strengthen the level of passenger waiting for inspection and waiting for the train, set up the inspection link, and so on.
In order to improve the interpretability of the complexity of the regional innovation system, the "B-L" reaction model in complexity science is introduced to increase the description dimension of the regional innovation system from 3 to 4 dimensions. Based on the idea of synergy, the logistic dynamic analysis model is constructed to determine the threshold conditions for the collaborative evolution of regional innovation systems. Based on China's statistical data from 2013 to 2017, the thresholds of 31 provincial regional innovation systems in Chinese mainland are calculated, 31 provinces are divided into five levels, and the empirical research on the evolution trajectory of order parameters is conducted. Research shows that the threshold of China's regional innovation system tends to be polarized. Most provinces and cities with strong innovation capabilities have higher thresholds, and a few have lower thresholds; provinces and cities with weaker innovation capabilities have the opposite. Based on the results of empirical research, regional innovation development strategies are proposed from four aspects:Identify regional differences, improve the industrial system, strengthen the positioning of the main body, and establish institutional policies.
This paper studies back-propagation (BP) neural network for continuous transportation network design problem under stochastic Origin-Destination (OD) demand. Assuming that the demand between every OD follows some distribution, the paper trains the neural network by the obtained training samples via Monte Carlo simulation, and then applies the trained BP neural network to predict the total system travel time. Using Nine-node network as a test network, this paper compares distributions of total travel time by BP neural network and Monte Carlo simulation when OD traffic demand respectively follows uniform, normal and log-normal distributions. The results show that the BP neural network method can obtain a better distribution of the total system travel time when solving the transportation network design problem, and can yield a more accurate, narrower range than the traditional Monte Carlo simulation calculation. Besides, the distribution of total travel time is more like the normal distribution when OD traffic demand is respectively subject to the above three distributions.
To improve the accuracy of interval forecasting, a VECM-CoinSVR hybrid model considering the cointegration between the upper and lower bounds for interval-valued forecasting is proposed. Vector error correction model (VECM) is firstly employed to fit the original time series so as to obtain the prediction result and residual error series of VECM. Secondly, the cointegration vector between the upper and lower bounds of the residual error series is obtained by using cointegration test, then the cointegration vector and the historical data of residual error series are treated as the input of the support vector regression considered cointegration (Coin-SVR) to obtain the prediction result of the residual error series. Finally, the final prediction of VECM-CoinSVR is obtained by combining the prediction result of VECM and the prediction result of the residual error series. To verify the effectiveness of the proposed model, the interval forecast hybrid model is used for empirical research on the price forecasting of beef, mutton and live chicken in the national market. Compared with the three single models (VECM, SVR, Coin-SVR) and based on the criteria MAPE, MSEI, and UI, VECM-CoinSVR has significantly higher prediction accuracy. By comparing with the point forecasting result of the interval center time series, the point that interval forecasting can yield a better result than point forecasting is further demonstrated.
In order to improve the efficiency of process quality monitoring and reduce the cost of quality control, aiming at many kinds of shifts that may occur in the process, this paper constructs an EWMA chart with time-varying parameters, which dynamically adjusts the parameters of control chart according to process sampling. And Markov chain method is used to calculate the APL value, which is used to evaluate the monitoring efficiency of control chart. According to the dispersion of multiple assignable shifts to be monitored, the calculation methods of quality control cost for a certain range of shifts and multiple separate shifts are given, and the multi-objective optimization design model of EWMA chart with time-varying parameters is constructed with the APL and unit product quality cost as the objective functions. Two numerical examples are used to illustrate the application of this optimization design method. Finally, the optimization design method is compared with several EWMA charts and multiple assignable shifts control charts. The results show that the multi-objective optimization design proposed in this paper is significantly better than the existing EWMA charts and multiple assignable shifts control charts.
This study proposes a consensus reaching method for large-scale group decision-making problems with overlapping subgroups under social network environment. In this method, subgroups have interrelationships and experts use hesitant fuzzy information to express preferences. First, we use a community detection method to cluster experts into several subgroups and introduce a weight-determination method for subgroups based on link strength and majority principle. Next, we propose a new consensus measurement method, namely, the maximum consensus sequence mining algorithm. Then, a consensus reaching method is given based on the maximum consensus sequences. Finally, we use an example regarding demonstration project selection of urban stormwater to verify the validity and rationality of the proposed method. Comparative analysis is also provided to show the advantages of our consensus reaching method.
Group attitude cooperative control of multiple rigid bodies system is investigated in this paper. The attitude of rigid bodies is described by modified Rodrigues parameters. The multiple rigid bodies system is constructed with several subgroups in it, and the switching topologies are denoted by block adjacency matrix and block Laplacian matrix. The definition of group attitude cooperation is provided, and distributed control input is designed. It is proved that the topologies being connected is the sufficient and necessary condition for multiple rigid bodies system to achieve group attitude cooperation. And the system reaching group attitude cooperation is not influenced by the dwelling time of each topology. Lyapunov stability theory is applied in the theoretical analysis. Computer simulation shows the correctness and effectiveness of the method proposed, and the simulation results show that if the switching topologies are not connected, the system cannot reach group attitude cooperation.