中国科学院数学与系统科学研究院期刊网

25 August 2024, Volume 44 Issue 8
    

  • Select all
    |
  • SUN Huixia, HUANG Song, ZHENG Tiantian
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2173-2191. https://doi.org/10.12341/jssms240295
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    A large amount of evidence suggests that companies with good ESG performance have a lower risk of collapse and stakeholder risk, thereby diluting systematic risk. However, compared to fundamental financial indicators, ESG as a non-financial indicator has not yet reached a consistent conclusion on its mechanism, dynamic variability, and heterogeneity of impact on stock systematic risk. Based on this, this paper selects data from January 2009 to November 2023 in the A-share market for empirical research. Based on the conditional CAPM model, the systematic risk $\beta$ is dynamically characterized as a linear function of ESG performance (non-financial characteristics) and company fundamental characteristics (financial characteristics). Then, the MCMC Bayesian estimation method is used to obtain time-varying estimates of $\beta$ for results analysis. The research results are as follows: First, there is a negative correlation between ESG performance and stock systematic risk, which has become increasingly strong and significant in recent years. Second, the impact of ESG performance on stock systematic risk shows heterogeneity across industries. For industries that are more affected by energy or national policies, good ESG performance helps to reduce systematic risk. Third, although ESG performance can affect stock systematic risk, investors respond less to ESG risk than to fundamental risk, leading to asymmetric investor reactions. Therefore, ESG risk can be considered a secondary risk, and its impact on systematic risk is moderated by fundamental characteristics such as market value and book-to-market ratio.
  • DU Yuxiao, HU Bin, LI Gang, LONG Lirong
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2192-2212. https://doi.org/10.12341/jssms23146
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    To analyze the cooperation behavior from the perspective of doctors and patients in the smart medical information platform, this paper constructs a random evolutionary game model and introduces random interference factors to represent the information uncertainty in the information platform. Afterward, this paper transforms the stochastic evolutionary game model of doctors and patients into a cusp catastrophe model through the limit probability density function, proving that the sudden change mechanism is implied in the behavior evolution of doctors and patients. Finally, the simulation method is used to analyze the evolution and sudden change mechanism of the behavior selection of doctors and patients in the diagnostic information platform and the interactive information platform. This paper finds that: In the smart medical information platform, the behavior selection of doctors and patients may change drastically due to factors such as perceived value and random interference; When the initial cooperation probability between doctors and patients is high if the security and reliability of information platform are poor, doctors and patients tend not to cooperate; When the initial cooperation probability is low if the information platform security and reliability are good, doctors and patients tend to cooperate; When the initial cooperation probability is at a moderate level, the behaviors of doctors and patients in the information platform are more susceptible to the additional benefits of non-cooperation, sudden changes of behavior selection are more probable. This paper analyzes the cooperation mechanism between doctors and patients in the smart medical information platform by combining evolutionary game theory and cusp catastrophe theory. It explains the reasons for sudden discontinuous changes in the behavior state of doctors and patients. The conclusion provides enlightenment for the research on doctor-patient cooperative behavior in the information platform and the development of the smart medical platform.
  • JIANG Xuehai, ZHENG Wanqiong, MA Benjiang
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2213-2235. https://doi.org/10.12341/jssms23274
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    In the context of the digital economy, the monopolistic behavior of platform enterprises and the design of antitrust mechanisms have become hot and difficult problems in current research. To solve this problem, a tripartite evolutionary game model among government, platform enterprises and the public was established under both static and dynamic reward & punishment mechanisms. In terms of model analysis, the possibility of mixed strategy Nash equilibrium (MNE) as an evolutionary stability strategy (ESS) under the two reward & punishment mechanisms and its system evolution characteristics were mainly discussed. It was proved that MNE under dynamic reward & punishment mechanisms may be the system ESS, and confirmed through system simulation. The simulation results indicate that under the static reward & punishment mechanism, all parties in the game will exhibit periodic strategy selection patterns, while under the dynamic reward & punishment mechanism, the system gradually stabilizes to MNE. This indicates that the existence of the dynamic reward & punishment mechanism is indeed a stability improvement compared to the static reward & punishment mechanism. Finally, it is suggested that government should develop dynamic reward & punishment mechanism, while increasing the intensity of punishment on platform enterprises and gradually reducing the intensity of rewards for the public. This approach can significantly increase the probability of compliance operation of platform enterprises and improve the expected utility of government and the public.
  • FENG Ling, LIN Yu, ZHONG Qunchao, WU Weiping
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2236-2256. https://doi.org/10.12341/jssms23435
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    Over the past few years, an ever-increasing number of investors have embraced the integration of environmental, social, and governance (ESG) factors within their decision-making processes. As a result, the growing popularity of investment models with the ESG investment philosophy. Recognizing the limitations of existing decision models based on this philosophy, which neglect the left-tail loss management demands of socially responsible investors, this paper comprehensively considers return volatility and losses caused by low probability extreme events. Specifically, we introduce a hybrid risk measure, combining variance and spectral risk measures, that can effectively manage both symmetric and asymmetric risks. The objective is to shape a more favorable probability distribution of terminal wealth. Then we apply this hybrid risk measure to the sustainable investment decision-making process and construct a mean-risk portfolio selection model with the hybrid risk measure and cone constraints. By doing so, this model reflects investors' social values while effectively characterizing two distinct risk features under realistic trading constraints. To determine optimal sustainable investment strategies and outline the efficient frontier, we convert the model into a quadratic-constrained convex programming problem. The findings from the numerical analysis demonstrate that the hybrid risk measure model enhances financial performance without sacrificing portfolio sustainability when short selling is prohibited, in comparison to the mean-variance model with the ESG investment philosophy. Moreover, it is observed that a positive correlation between sustainable investment performance and investors' inclination toward controlling downside risks. It is essential to note that the implementation of ESG negative screening strategies results in reduced diversification of the optimal portfolio. This reduction subsequently increases investment risk, thereby adversely impacting the performance of sustainable investments. Additionally, the weight parameters reflecting investors' risk aversion significantly impact sustainable investment performance. As a result, prudent consideration should be given to the adoption of ESG negative screening strategies, along with the selection of an appropriate weight function aligned with the investors' risk aversion, to optimize both financial and social benefits.
  • SHENG Jiliang, CHEN Lanxi, WEN Runlin
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2257-2277. https://doi.org/10.12341/jssms23105
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    Due to the non-subadditivity property of value-at-risk (VaR) when measuring tail loss risk, we propose a risk parity investment portfolio model based on conditional value-at-risk (CVaR) and provide a numerical calculation method for implementing the investment portfolio strategy. Using Sharpe ratio, maximum drawdown, and Calmar ratio as performance evaluation indicators, the risk parity investment strategy based on CVaR is compared with common investment portfolio strategies. Numerical experimental results indicate that the comprehensive performance of the risk parity strategy is more robust than the equal-weight investment portfolio strategy, the maximum Sharpe ratio investment strategy, and the global minimum variance investment strategy. Among the three risk parity strategies, the CVaR-based risk parity investment strategy has advantages in risk control, significantly improving both return and risk diversification effects. The robustness test results also suggest that the CVaR-based risk parity investment strategy can maintain stability and effectiveness in different situations.
  • WANG Yuyan, YANG Luqi
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2278-2302. https://doi.org/10.12341/jssms23447
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    The e-commerce secondary closed-loop supply chain composed of the manufacturer and e-commerce platform has been constructed. At this stage, there are two main product sales modes: Wholesale and direct sales modes, in which the recycling service can be undertaken by the e-commerce platform or manufacturer. The combination of the sales model and the recycling service strategy generates four typical operation models: Wholesale direct recycling (SM), wholesale entrusted recycling (SR), direct marketing direct recycling (MM), direct marketing entrusted recycling (MR). On this basis, the impact of risk aversion behavior of supply chain members on pricing, recycling and operation mode selection is analyzed. It is found that: 1) When the manufacturer is risk-averse, the manufacturer prefers MM mode in most cases, while the choice of e-commerce platform is affected by the combination of risk aversion and commission rate, and if the commission rate is large, the manufacturer chooses MR mode when the degree of risk aversion is low, and chooses SR mode when the degree of risk aversion is large. 2) When the e-commerce platform risk-averse, the manufacturer chooses MM mode when the degree of risk aversion and commission rate is small, and chooses SM mode when the degree of risk aversion and commission rate is large; When the e-commerce platform has a low degree of risk aversion, the optimal decision-making is shifted from the SM mode to the MR mode with the increase of the commission rate. In addition, it is found that the increase in the level of recycling compensation makes the manufacturer prefer the commissioned recycling model. Finally, the strategic interaction between the e-commerce platform in first determining the sales model and the manufacturer then in determining the recycling model is considered to explore the equilibrium models under different levels of risk aversion.
  • FANG Yu, BORJIGIN Sumuya
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2303-2334. https://doi.org/10.12341/jssms23531
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    In the face of the increasingly complex and changeable world economic environment, continuously enhancing the innovation ability of enterprises plays a decisive role in promoting the supply-side reform. Enterprises need to rely on capital market financing to meet their own innovation needs of a large amount of capital investment. As an important characteristic of the stock market, which has an absolute position in the capital market, stock liquidity is likely to be one of the important objective factors affecting the innovation ability of enterprises. Therefore, this paper will deeply study the impact of stock liquidity on enterprise innovation and its channels. Based on the Shanghai and Shenzhen a share listed companies as the research object, the two cities to establish dynamic panel model, using bidirectional fixed effect model to empirical analysis, through the lag effect model to analyze its lag effect, using instrumental variable method and GMM model control endogenous intermediary effect inspection at the same time, and the analysis of the heterogeneity and the corresponding robustness analysis. The results show that: 1) Stock liquidity has a positive stimulating effect on firm innovation ability; 2) The influence degree of institutional shareholding ratio accounts for more than 50% of all channels, which is the main influence channel of research; 3) State-owned enterprises pay more attention to long-term development, and their innovation is higher than other enterprises. The analysis of industry heterogeneity shows that the innovation of high-tech industry is higher than other industries. In theory, the research proves that highly liquid stock mainly stimulates enterprise innovation by increasing the proportion of institutional ownership. In reality, the research results support China's further deepening of share reform. In the process of economic operation, appropriately increasing the proportion of institutional investment and shareholding is conducive to enterprise innovation and economic development.
  • XU Yangdong, AN Quanying
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2335-2349. https://doi.org/10.12341/jssms23186
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    In this paper, a traffic network equilibrium problem with uncertain cost functions and arc capacity constraints is studied. This paper adopts the ReLU function and the vector version of Heaviside function to construct two optimal problems that their solutions are equivalent to weak vector equilibrium flows and vector equilibrium flows, respectively. Then, the objective functions are smoothed by using Moreau proximal smoothing technique. In addition, the concept of robust vector equilibrium flows is introduced, and an equivalent optimization problem is also established. Simultaneously, a smoothing algorithm is proposed. Finally, compared with other state-of-the-art algorithms, the advantages of the computational speed and obtaining more robust vector equilibrium flows for our algorithm are illustrated by numerical examples.
  • TONG Jiaqi, SONG Wanling, SONG Qiqing
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2350-2364. https://doi.org/10.12341/jssms23441
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    In this paper, we introduce the model of multi-leader-multi-follower games with preferences, propose the concept of the $\alpha$-core of multi-leader-multi-follower games with preferences, and prove the existence of $\alpha$-core in this model. This generalizes Yang and Ju's(2016) results under the utility representation to the multi-leader-multi-follower games model with general preferences. Thus, it expands the kinds of multi-leader-multi-follower games and gives a more general description of cooperative solutions of multi-leader-multi-follower games. Furthermore, it can include some continuous and non-continuous utility type multi-leader-multi-follower game models. Using the locally FS-majorized conditions, this paper proves the existence of $\alpha$-core of multi-leader-multi-follower games with preferences. Further, this paper gives the concept of weak $\alpha$-core of multi-leader-multi-follower games with general preferences when the multi-leader-multi-follower games have infinite leaders and followers, and studies its existence and finally proves the existence of weak $\alpha$-core of multi-leader-multi-follower games.
  • BAI Fusheng, WEI Yutao, ZOU Dongchi
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2365-2383. https://doi.org/10.12341/jssms23292
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    This paper presents a surrogate optimization method for the expensive black-box multi-objective optimization problems, which balances local and global searches using multiple sampling strategies in iterations. Under the framework of the surrogate optimization, this method employs a radial basis function interpolation model to approximate. During the iterations of the algorithm, a local search assisted target value strategy is implemented, in which multiple approximate Pareto fronts are constructed, multiple vectors filling the gaps of the approximate Pareto fronts are selected as the candidates of the target value, a new target value is determined based on the evaluated decision vectors and the existing target values, and a local search is undertaken around the corresponding candidate of the sample point. Moreover, a clustering method is used in the surrogate optimization sampling to enhance the diversity of the sample points. Numerical experiments on 58 standard test problems consisting of both low- and high-dimensional ones, as well as two practical problems, demonstrate the effectiveness of the proposed algorithm.
  • PENG Juanjuan, TAN Hao, LONG Qingqi, CHEN Xinge
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2384-2411. https://doi.org/10.12341/jssms23356
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    For the two-sided matching problem with the picture fuzzy preference information, the picture fuzzy two-sided stable matching decision-making approach based on the improved TODIM method is proposed considering the bounded rationality of matches and the incompleteness of the matching criteria. First, this paper defines a mixed picture fuzzy distance measure based on Euclidean distance and arc cosine, which can avoid the defects caused by a single distance measure and improve the discrimination between two picture fuzzy numbers. Second, this paper considers the incompleteness of matching attribute information, and constructs the optimization model by combining the proposed mixed distance and maximum deviation method to determine the attribute weight information of each matching subject. Third, from the bounded rationality behavior of matching subjects in the decision-making process, this paper constructs a stability-based two-sided matching decision-making optimization model by using the improved TODIM method based on the picture fuzzy mixed distance. Finally, based on the doctor-patient two-sided matching problem on the online medical platform as the case study, the stable two-sided matching scheme is obtained by using the proposed method. The effectiveness and stability of the method are further verified through comparative analysis and sensitivity analysis in different scenarios.
  • LIU Weihua, LU Yizhen
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2412-2428. https://doi.org/10.12341/jssms23475
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    Fresh products face the dual pressures of time-varying demand caused by freshness change and logistic service uncertainties. Efficient collaboration between the supply chain and logistic services is crucial for devising optimized operational strategies. This paper analyses the impacts of pricing, preservation strategy and logistics uncertainty of fresh-product supplier and retailer on the supply chain based on consumers' time-varying utility function by constructing a Stackelberg game model. The coordination issues in fresh supply chain under centralized and decentralized decision-making are explored. The study reveals that, from an overall supply chain perspective, considering logistic uncertainty leads to a significant decrease in supply chain efficiency. For supplier, in situations of lower logistic uncertainty, profit is greater under the baseline scenario (without logistic uncertainty); it leads to an increase in positive demand and thus to greater supplier profitability when logistics uncertainty exceeds a certain threshold. Regarding retailer, profit is higher in the presence of logistic uncertainty when the product has a shorter sales period, while in situations with a longer sales period, profit is higher under the baseline scenario. As the profit-sharing ratio increases, the efficiency of the fresh product supply chain improves and steadily approaches 100%, indicating that profit-sharing contracts can achieve supply chain coordination.
  • XU Shaodong, LI Yang, BIAN Ce
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2429-2457. https://doi.org/10.12341/jssms23257
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    In the era of big data, high-dimensional survey data with mixed types of covariates brings challenges to heterogeneity analysis and its variable selection. This paper proposes a novel sparse clustering method, and discusses its application by taking the China Education Panel Survey and the social survey of "Thousands of People and Hundreds of Villages" as examples. This paper proposes an adjusted DBI criterion to measure the importance of covariates, uses different penalty parameters to control the weights of different types of covariates, and obtains the optimal clustering results and significant covariates. At the theoretical level, this paper demonstrates the variable screening consistency of the proposed method. At the numerical experiment level, a series of simulation experiments are designed in this paper to verify the good performance of the proposed method in terms of clustering and variable selection. The results of empirical data also show that the clusters divided by the proposed method have a high degree of discrimination, which is convenient for researchers to characterize each group; At the same time, the selected variables have important practical meanings. Without losing information, the dimensionality of the data is reduced, and the interpretability of the model is increased. The sparse clustering analysis proposed in this paper realizes the joint analysis of mixed types of covariates in high-dimensional survey data, which greatly improves the utilization rate of information.
  • LI Xu, ZHANG Baoxue
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2458-2475. https://doi.org/10.12341/jssms240035
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    Technological advances have enabled us to collect a lot of complex data objects, where homogeneity structure among these objects is widely used in Statistics. However, the existing metrics of homogeneity are subject to some qualifications, such as assumptions about the moment and parameters. To overcome the limitation, this paper proposes a new homogeneity test for high-dimensional two populations. Based on the double expectation formula and the properties of characteristic functions, a new measure and its empirical version are constructed in high-dimensional cases. Furthermore, under suitable regular conditions, the large sample nature of the proposed test is established too, such as the tests proposed in this paper converge to a mixture of $\chi^{2}$ distributions under the null hypothesis and a normal distribution under the alternative hypothesis. Meanwhile, Monte Carlo simulation results show that the new methods perform better than several existing test procedures for high-dimensional data.
  • XU Hongxia, LIN Xinda, FAN Guoliang
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2476-2495. https://doi.org/10.12341/jssms23603
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    In this paper, we study composite quantile regression(CQR) and variable selection of linear errors-in-variables models where the response and multi-dimensional covariates are mixed random missing. In order to improve the estimation efficiency, we propose the CQR estimator of regression coefficients based on inverse probability weighting and measurement error correction factor. The proposed CQR estimator can not only eliminate the influence of measurement errors on estimation results, but also deal with mixed random missing data effectively. At the same time, the asymptotic normality of the proposed estimator is obtained. Furthermore, a variable selection method based on the adaptive LASSO penalty is investigated for the measurement error models with mixed random missing data. The oracle property of the proposed penalized estimator is also established. Meanwhile, Monte Carlo simulation studies and a real data analysis are conducted to demonstrate the finite sample performance of the proposed methods.
  • SUN Lirong, JIANG Chenkai, TIAN Yinghua, GUO Baocai
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2496-2514. https://doi.org/10.12341/jssms23474
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    Interval function clustering is a method used to analyze continuous high-frequency data. The existing interval function based clustering under uniform distribution cannot fully utilize the distribution information within the interval. Moreover, the assumption of uniform distribution does not conform to the distribution of many data, resulting in poor clustering performance and stability. In response to these issues, this article considers the actual situation of data distribution. Using the mean and standard deviation of the original data, we improve the existing midpoint-radius method and propose an interval function based clustering method based on generalized distribution. This method expands the range of use of interval functional clustering and better describes the distribution within the interval. And it can fully utilize and obtain the inherent features of data information, improve the effectiveness and rationality of clustering results. Using the Monte Carlo method, we calculate the internal indicator and compare the advantages and disadvantages of the proposed method with existing interval function clustering under the assumption of uniform distribution. The results show that the proposed method in this article is superior to existing interval function clustering methods under uniform distribution. Finally, the proposed method in this article is applied to cluster analysis of atmospheric pollutant concentrations in different cities. It has been verified that this method not only effectively solves practical problems, but also has obvious advantages compared to existing methods.
  • SHAO Ze, YAN Liang, LI Menghan, CAI Xia
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2515-2535. https://doi.org/10.12341/jssms23614
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    The three-parameter Weibull distribution is one of the commonly used distributions for reliability analysis. However, its non-regular issue poses challenges to the validity of large sample properties of frequentist method. Additionally, the selection of prior in Bayesian estimation also faces certain issues. In order to provide practitioners with an alternative choice, this paper applies the generalized fiducial inference to the study of the three-parameter Weibull distribution. For the interest parameters such as reliability, generalized fiducial point estimation and confidence interval are constructed and compared with frequentist method and Bayesian method. Simulation results show that generalized fiducial point estimation has smaller or comparable mean square error and shorter or comparable average interval length while maintaining coverage probability. Finally, the effectiveness of generalized fiducial inference in the three-parameter Weibull distribution is demonstrated using data on single carbon fibers strength and ball bearings.
  • ZHANG Liangyong, DONG Xiaofang, FAN Xiangjia
    Journal of System Science and Mathematical Science Chinese Series. 2024, 44(8): 2536-2547. https://doi.org/10.12341/jssms240067
    Abstract & Html ( ) Download PDF ( )   Knowledge map   Save
    The ranked set sampling method is suitable for the situation where the sample measurement is difficult but the ranking is easy, and has been widely applied in clinical medicine, ecological environment, agriculture and forestry, and other fields. The distribution function is an important function in probability statistics. In order to improve the estimation efficiency of the distribution function of an unknown population, this paper adopts the kernel estimation idea and the average rank method to construct a nonparametric estimator of the distribution function based on the ranked set sampling method. The new estimator is shown to have asymptotic unbiasedness, consistency, and uniformly strong consistency. The estimation efficiency is evaluated by the mean integrated square error of the estimator. The research results of asymptotic relative efficiency and simulated relative efficiency show that the estimation efficiency of the new estimator is higher than that of the corresponding estimator under simple random sampling, and as the sample size decreases, the relative advantage of the new estimator becomes more apparent. Finally, the application results of coniferous tree data further verify the correctness of the theoretical research results.