Computational Economics最新文献_第4页

Game Analysis of the Behavior of Participants in Green Supply Chain Finance Based on Digital Technology Platforms 基于数字技术平台的绿色供应链金融参与者行为博弈分析

IF 2 4区经济学

Computational Economics Pub Date : 2024-07-17 DOI: 10.1007/s10614-024-10667-5

Yitian Hong, Chuan Qin

{"title":"Game Analysis of the Behavior of Participants in Green Supply Chain Finance Based on Digital Technology Platforms","authors":"Yitian Hong, Chuan Qin","doi":"10.1007/s10614-024-10667-5","DOIUrl":"https://doi.org/10.1007/s10614-024-10667-5","url":null,"abstract":"Similar to traditional supply chain finance (SCF) models, green supply chain finance (GSCF) also faces issues such as information asymmetry and heavy reliance on the creditworthiness of transaction parties. Under the influence of internet ideology, cracking down on traditional GSCF financing issues and transitioning from interpersonal trust to digital trust has become an inevitable trend. Achieving real-time, transparent, correlated, and traceable digital trust, digital technology (DT) platforms provide a solution. Based on the background of \"Green Carbon Chain Pass\" bill discounting financing business in the GSCF model of “Jian Dan Hui (JDH) platform”, game models are constructed involving small and medium-sized enterprises (SMEs), financial institutions (FIs), and core enterprises (CEs) in traditional model and after accessing the platform, based on game theory and considering the uncertainty in the decision-making process. The key factors influencing the strategic choices of the players and the impact mechanism of DT empowering the development of GSCF are explored. MATLAB software is used for simulation experiments. The results show that the cost of business operation, bill maturity values, discount rate, and losses caused by CEs not pay as agreed are important factors affecting the strategic choices of SMEs, FIs, and CEs; Accessing digital platform makes it easier to satisfy the conditions for the tripartite game to evolve into an ideal stable state; Splitting the value of supply chain bills by accessing digital platform can promote business cooperation between FIs and SMEs; The platform, relying on blockchain technology, encourages CEs to pay bills as agreed by increasing default losses; The platform relies on green ratings to motivate SMEs to apply for discounting financing through differentiated financing rates, while promoting their green management; Accessing to digital platform brings efficiency improvements and credit rewards, both of which encourage the three players to choose active financing strategies.","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Should the Occupational Pension Plans’ Investment be Long-Term or Short-Term? Evidence from China 职业年金计划的投资应该是长期还是短期？来自中国的证据

IF 1.9 4区经济学

Computational Economics Pub Date : 2024-07-16 DOI: 10.1007/s10614-024-10677-3

Wenling Liu, Fengmin Xu, Kui Jing, Ziyue Hua

引用次数: 0

Enhancing Long-Term GDP Forecasting with Advanced Hybrid Models: A Comparative Study of ARIMA-LSTM and ARIMA-TCN with Dense Regression 利用先进的混合模型加强长期 GDP 预测：带有密集回归的 ARIMA-LSTM 和 ARIMA-TCN 比较研究

IF 1.9 4区经济学

Computational Economics Pub Date : 2024-07-16 DOI: 10.1007/s10614-024-10683-5

Dalia Atif

引用次数: 0

Stability and Convergence Analysis of a Numerical Method for Solving a $$zeta$$-Caputo Time Fractional Black–Scholes Model via European Options 通过欧式期权求解 $$zeta$$-Caputo 时间分数 Black-Scholes 模型的数值方法的稳定性和收敛性分析

IF 1.9 4区经济学

Computational Economics Pub Date : 2024-07-16 DOI: 10.1007/s10614-024-10678-2

Feten Maddouri

引用次数: 0

Research of Dempster-Shafer’s Theory and Ensemble Classifier Financial Risk Early Warning Model Based on Benford’s Law 基于本福德定律的 Dempster-Shafer 理论与集合分类器金融风险预警模型研究

IF 2 4区经济学

Computational Economics Pub Date : 2024-07-13 DOI: 10.1007/s10614-024-10679-1

Zihao Liu, Di Li

{"title":"Research of Dempster-Shafer’s Theory and Ensemble Classifier Financial Risk Early Warning Model Based on Benford’s Law","authors":"Zihao Liu, Di Li","doi":"10.1007/s10614-024-10679-1","DOIUrl":"https://doi.org/10.1007/s10614-024-10679-1","url":null,"abstract":"Previous research endeavors aimed at enhancing the predictive accuracy of early warning systems for enterprise financial risks have primarily focused on two key areas: optimization of financial risk early warning indicators and development of combination models. However, crucial issues relating to the uncertainty arising from divergent assessment results among multiple classifiers analyzing the same sample data in financial risk early warning, as well as the impact of distorted financial indicator data on the predictive performance of financial early warning models, have remained largely unexplored. This study employs Benford’s law to establish a comprehensive early warning indicator system for financial risks, incorporating its inherent factors. Additionally, the DS-evidence theory is utilized to seamlessly integrate Logistic Regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), and AdaBoost classifiers into an ensemble classifier named the Dempster-Shafer’s theory and Ensemble Classifier (DS-EC) financial risk warning model. The findings demonstrate that: (1) The DS-EC model effectively resolves the issue of uncertainty resulting from diverse evaluation results among multiple classifiers analyzing identical sample data, significantly outperforming LR, NB, SVM, GBDT, and AdaBoost in terms of predictive accuracy. (2) Benford’s law proves to be a robust technique for detecting fraudulent risks within financial data, and its amalgamation with the DC-EC financial risk warning model enhances the model’s predictive accuracy.","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141610270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Is Default Risk Contagious? Evidence from Global Energy Leaders and Environmentally Conscious Energy Firms 违约风险会传染吗？来自全球能源龙头企业和具有环保意识的能源企业的证据

IF 2 4区经济学

Computational Economics Pub Date : 2024-07-13 DOI: 10.1007/s10614-024-10631-3

Zaheer Anwer, Wajahat Azmi, M. Kabir Hassan, Shamsher Mohamad

引用次数: 0

Dynamic Market Behavior and Price Prediction in Cryptocurrency: An Analysis Based on Asymmetric Herding Effects and LSTM 加密货币的动态市场行为和价格预测：基于非对称羊群效应和 LSTM 的分析

IF 2 4区经济学

Computational Economics Pub Date : 2024-07-13 DOI: 10.1007/s10614-024-10676-4

Guangxi Cao, Meijun Ling, Jingwen Wei, Chen Chen

{"title":"Dynamic Market Behavior and Price Prediction in Cryptocurrency: An Analysis Based on Asymmetric Herding Effects and LSTM","authors":"Guangxi Cao, Meijun Ling, Jingwen Wei, Chen Chen","doi":"10.1007/s10614-024-10676-4","DOIUrl":"https://doi.org/10.1007/s10614-024-10676-4","url":null,"abstract":"This study employs the cross-sectional absolute deviation model and Carhart pricing model to examine the existence and authenticity of various market sizes and liquidity levels within cryptocurrency markets. Additionally, we introduce a herding effect measurement index tailored for the cryptocurrency market and predict cryptocurrency prices by integrating the long short-term memory (LSTM) neural network model. Empirical results reveal the presence of both genuine and pseudo herding phenomena in cryptocurrency markets, with information acquisition asymmetry identified as a significant driver of herding behavior. Specifically, during market downturns in the overall market, only pseudo herding is observed in the upward market, whereas during periods of market prosperity, both genuine and pseudo herding are evident in the downward market. In markets of different sizes, herding is absent in cryptocurrency markets with small market value, while in large market value cryptocurrency markets, pseudo herding is not statistically significant. Genuine herding occurs in both upward and downward markets during non-downturn periods. Regarding cryptocurrency markets with different liquidity levels, herding behavior is not observed in markets with small trading volume. Conversely, in markets with large trading volume, pseudo herding is observed in both upward and downward markets during non-downturn periods, with genuine herding occurring in both markets during boom periods. Additionally, the LSTM model demonstrates superior capability in fitting the price trends of different cryptocurrencies, and considering the herding effect index significantly enhances the accuracy of cryptocurrency price prediction.","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141610368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparing the Mixed Logit Estimates and True Parameters under Informative and Uninformative Heterogeneity: A Simulated Discrete Choice Experiment 比较有信息和无信息异质性下的混合 Logit 估计值和真实参数：模拟离散选择实验

IF 2 4区经济学

Computational Economics Pub Date : 2024-07-12 DOI: 10.1007/s10614-024-10637-x

Maksat Jumamyradov, Benjamin M. Craig, William H. Greene, Murat Munkin

{"title":"Comparing the Mixed Logit Estimates and True Parameters under Informative and Uninformative Heterogeneity: A Simulated Discrete Choice Experiment","authors":"Maksat Jumamyradov, Benjamin M. Craig, William H. Greene, Murat Munkin","doi":"10.1007/s10614-024-10637-x","DOIUrl":"https://doi.org/10.1007/s10614-024-10637-x","url":null,"abstract":"In discrete choice experiments (DCEs), differences between respondents’ preferences may be associated with observable or unobservable factors. Unobservable heterogeneity, related to latent factors associated with the choices of individuals, may be modelled using correlated (i.e. informative heterogeneity) or uncorrelated (i.e. uninformative heterogeneity) individual-specific parameters of a logit model. In this study, we simulated unobservable heterogeneity among DCE respondents and compared the results of the maximum simulated likelihood (MSL) estimation of the mixed logit model when correctly specified and mis-specified. These results show that the MSL estimates are biased and can differ greatly from the true parameters, even when correctly specified. Before estimating a mixed logit model, we highly recommend that choice modellers conduct simulation analyses to assess the potential extent of biases before relying on the MSL estimates, particularly their variances and correlations, and then ultimately determine which model specification produces the least bias.","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141610364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Systemic Financial Risk of Stock Market Based on Multiscale Networks 基于多尺度网络的股票市场系统性金融风险

IF 1.9 4区经济学

Computational Economics Pub Date : 2024-07-09 DOI: 10.1007/s10614-024-10680-8

Youtao Xiang, Sumuya Borjigin

引用次数: 0

Bias Correction in the Least-Squares Monte Carlo Algorithm 最小二乘蒙特卡罗算法中的偏差校正

IF 2 4区经济学

Computational Economics Pub Date : 2024-07-08 DOI: 10.1007/s10614-024-10663-9

François-Michel Boire, R. Mark Reesor, Lars Stentoft

引用次数: 0