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祝由

发布于:2019-09-06 星期五 19:29:52 点击数:12646

祝由,管理学博士,湖南大学博士后,现任湖南大学工商管理学院副教授、硕士生导师,岳麓学者。

E-mail:zy19@hnu.edu.cn

基本信息

       祝由,管理学博士,湖南大学博士后,现任湖南大学工商管理学院副教授、硕士生导师,岳麓学者,主要从事企业理财与资本运营、金融企业及其风险管理、供应链金融、互联网金融、金融科技等领域研究。目前在International Journal of Production Economics,Technological Forecasting & Social Change, Neural Computing & Applications,Entropy等SSCI/SCI收录期刊上发表相关学术论文,主持湖南省“十四五”农业农村现代化规划前期重大研究课题1项、博士后科学基金1项,参与国家自然科学基金、省部级项目若干项。此外,担任International Journal of Production Economics, International Journal of Production Research, Research in International Business and Finance, Neural Computing and Applications等多个国际权威学术期刊审稿人。


教育背景

       2017.06 湖南大学 工商管理 博士

       2010.11 英国格林尼治大学 物流项目管理 硕士

       2009.06 英国格林尼治大学 商业研究 学士


职业经历

        2022.01~至今        湖南大学工商管理学院 副教授

        2019.07~2021.12  湖南大学工商管理学院 助理教授

        2017.07~2019.07  湖南大学工商管理学院 博士后

        2011.01~2013.08  英国格林尼治大学供应链管理研究中心 研究人员


招收学术型研究生的基本要求

        诚实守信、认真勤勉、积极进取、热爱研究;欢迎金融学、管理学、统计学、信息科学等学科背景的学生报考。


研究领域

企业理财与资本运营、金融企业及其风险管理、供应链金融、互联网金融、金融科技

讲授课程

中文课程:运营管理

英文课程:创业管理、企业理论、企业管理实验、新兴技术与动态战略 、创新创业项目、数字货币与区块链金融研究、数据挖掘


研究成果

1. 期刊论文

[1] Tiep Nguyen, Quang Huy Duong, Truong Van Nguyen, You Zhu*, Li Zhou. Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review. International Journal of Production Economics, 2022, 244: 108381. [SCI/SSCI,ABS三星级刊物,通讯作者]

Abstract: Physical Internet (PI) is an open global logistics system of which components are hyperconnected for increased efficiency and sustainability. Digital twin (DT), referring to the virtual representation of a physical object, is wellperceivedas a key driver in the development of PI-based Supply Chain Management (SCM). Due to the capabilities of real-time monitoring and evaluation of large-scale complex systems, significant research efforts havebeen made to exploit values of PI/DT in SCM. Despite this, the current literature remained largely unstructuredand scattered due to a lack of systematic literature reviews to synergise research findings, analyse the evolutionof research fronts and extract emerging trends in the field. To address this issue, the paper deploys a bibliometric knowledge mapping approach to provide a bird’s eye view of the current research status in the PI/DT-SCM area.Using CiteSpace’s keyword co-occurrence network, 518 journal articles are clustered into 10 key researchstreams on PI/DT applications in: job shop scheduling, smart manufacturing design, PI-based SCM,manufacturing virtualisation, information management, sustainability development, data analytics,manufacturing operations management, simulation and optimisation, and assembly process planning. Based oncitation burst rate, keywords representing research frontiers of the PI/DT are detected and their temporal evolutionsare discussed. Likewise, some identified emerging research trends are production process and system,robotics, computer architecture, and cost. Finally, seven future research directions are suggested, whichemphasise on several PI/DT-related issues, including business ecosystem, sustainability development, SCdownstream management, cognitive thinking in Industry 5.0, citizen twin in digital society, and SC resilience.


[2] Gang-Jin Wang*, Yusen Feng, Yufeng Xiao, You Zhu, Chi Xie. Connectedness and systemic risk of the banking industry along the Belt and Road. Journal of Management Science and Engineering (JMSE), 2021, DOI: 10.1016/j.jmse.2021.12.002.[《管理科学学报(英文版)》]

This paper adopts the tail-event driven network (TENET) framework to explore the connectedness and systemic risk of the banking industry along the Belt and Road (B&R) based on weekly returns of 377 publicly-listed banks from 2014 to 2019. We conduct the connectedness analysis from four levels (i.e., system, region, country and institution) and identify the systemic risk contribution of banks. We find that the dynamic total connectedness reached its peak during the outbreak of the “2015–2016 Chinese stock market crash” and its trough during the Brexit vote, and subsequently experienced several periodic fluctuations at a relatively high position. In the B&R banking system, the intra-regional tail risk spillovers are remarkably stronger than the inter-regional tail risk spillovers during the post-crisis period. In addition, the panel regressions estimated by the least squares dummy variable model show that the cross-border merger and acquisitions (M&As) and the merchandise trade export are important drivers for the tail-connectedness across the B&R countries. Our study provides regulators with insightful implications on the systemic risk supervision of the B&R banking industry.


[3] Xinyi Wang, Deming Zeng, Haiwen Dai, You Zhu*. Making the right business decision: Forecasting the binary NPD strategy in Chinese automotive industry with machine learning methods. Technological Forecasting & Social Change, 2020,155:120032. [SSCI,ABS三星级刊物,通讯作者]

Abstract: The new product development (NPD) is crucial to firms’ survival and success. Tough decisions must be made between the binary NPD strategy (i.e. incremental NPD strategy and radical NPD strategy) to ensure that scarceresources are allocated efficiently. The inappropriate NPD strategy that does not meet the internal and external conditions may lead to resources waste and performance decline. The binary NPD strategy forecasting is helpfulto guide the firms when to improve existing products and when to develop ‘really new’ products. Therefore, theprimary purposes of this study are to construct an evaluating indicator system and to find the appropriatemethod for the binary NPD strategy forecasting. Here we obtain 1088 valid sample datasets from Chinese automotiveindustry, covering the period 2001–2014. The empirical results indicate that RS-MultiBoosting as akind of hybrid ensemble machine learning (HEML) method demonstrate an outstanding forecasting performancein dealing with the small datasets by comparison with the other four ensemble machine learning (EML) methodsand three individual machine learning (IML) methods. The findings can help firms to make the right businessdecision between incremental and radical NPD strategies so that they can avoid resources waste and improve theoverall NPD performance.


[4] You Zhu, Li Zhou, Chi Xie*, Gang-Jin Wang, Truong V Nguyen. Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. International Journal of Production Economics, 2019, 211: 22-33. [SCI/SSCI,ABS三星级刊物,第一作者]

Abstract: In recent years, financial institutions (FIs) have tentatively utilized supply chain finance (SCF) as a means of solving the financing issues of small and medium-sized enterprises (SMEs). Thus, forecasting SMEs' credit risk in SCF has become one of the most critical issues in financing decision-making. Nevertheless, traditional credit risk forecasting models cannot meet the needs of such forecasting. Many researchers argue that machine learning (ML) approaches are good tools. Here we propose an enhanced hybrid ensemble ML approach called RSMultiBoosting by incorporating two classic ensemble ML approaches, random subspace (RS) and MultiBoosting, to improve the accuracy of forecasting SMEs' credit risk. The experimental samples, originating from data on forty-six quoted SMEs and seven quoted core enterprises (CEs) in the Chinese securities market between 31 March 2014 and 31 December 2015, are collected to test the feasibility and effectiveness of the RS-MultiBoosting approach. The forecasting result shows that RS-MultiBoosting has good performance in dealing with a small sample size. From the SCF perspective, the results suggest that to enhance SMEs' financing ability, ‘traditional’ factors, such as the current and quick ratio of SMEs, remain critical. Other SCF specific factors, for instance, the features of trade goods and the CE's profit margin, play a significant role.


[5] You Zhu, Chi Xie*, Gang-Jin Wang, Xin-Guo Yan. Comparison of individual, ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance. Neural Computing & Applications, 2017, 28(S1): 41-50. [SCI, JCRQ1,第一作者]

Abstract: Supply chain finance (SCF) becomes more important for small- and medium-sized enterprises (SMEs) due to global credit crunch, supply chain financing woes and tightening credit criteria for corporate lending. Currently, predicting SME credit risk is significant for guaranteeing SCF in smooth operation. In this paper, we apply six methods, i.e., one individual machine learning (IML, i.e., decision tree) method, three ensemble machine learning methods [EML, i.e., bagging, boosting, and random subspace (RS)], and two integrated ensemble machine learning methods (IEML, i.e., RS–boosting and multiboosting), to predict SMEs credit risk in SCF and compare the effectiveness and feasibility of six methods. In the experiment, we choose the quarterly financial and non-financial data of 48 listed SMEs from Small and Medium Enterprise Board of Shenzhen Stock Exchange, six listed core enterprises (CEs) from Shanghai Stock Exchange and three listed CEs from Shenzhen Stock Exchange during the period of 2012–2013 as the empirical samples. Experimental results reveal that the IEML methods acquire better performance than IML and EML method. In particular, RS–boosting is the best method to predict SMEs credit risk among six methods.

 

[6] You Zhu, Chi Xie*, Gang-Jin Wang, Xin-Guo Yan. Predicting China’s SME credit risk in supply chain finance based on machine learning methods. Entropy, 2016, 18(5): 195-202. [SCI,JCRQ2,第一作者]

Abstract: We propose a new integrated ensemble machine learning (ML) method, i.e., RS-RAB (Random Subspace-Real AdaBoost), for predicting the credit risk of China’s small and medium-sized enterprise (SME) in supply chain finance (SCF). The sample of empirical analysis is comprised of two data sets on a quarterly basis during the period of 2012–2013: one includes 48 listed SMEs obtained from the SME Board of Shenzhen Stock Exchange; the other one consists of three listed core enterprises (CEs) and six listed CEs that are respectively collected from the Main Board of Shenzhen Stock Exchange and Shanghai Stock Exchange. The experimental results show that RS-RAB possesses an outstanding prediction performance and is very suitable for forecasting the credit risk of China’s SME in SCF by comparison with the other three ML methods.


[7] You Zhu, Chi Xie*, Bo Sun, Gang-Jin Wang, Xin-Guo Yan. Predicting China’s SME credit risk in supply chain financing by logistic regression, artificial neural network and hybrid models. Sustainability, 2016, 8(5): 433-449. [SCI/SSCI,JCRQ2,第一作者]

Abstract: Based on logistic regression (LR) and artificial neural network (ANN) methods, we construct an LR model, an ANN model and three types of a two-stage hybrid model. The two-stage hybrid model is integrated by the LR and ANN approaches. We predict the credit risk of China’s small and medium-sized enterprises (SMEs) for financial institutions (FIs) in the supply chain financing (SCF) by applying the above models. In the empirical analysis, the quarterly financial and non-financial data of 77 listed SMEs and 11 listed core enterprises (CEs) in the period of 2012–2013 are chosen as the samples. The empirical results show that: (i) the “negative signal” prediction accuracy ratio of the ANN model is better than that of LR model; (ii) the two-stage hybrid model type I has a better performance of predicting “positive signals” than that of the ANN model; (iii) the two-stage hybrid model type II has a stronger ability both in aspects of predicting “positive signals” and “negative signals” than that of the two-stage hybrid model type I; and(iv) “negative signal” predictive power of the two-stage hybrid model type III is stronger than that of the two-stage hybrid model type II. In summary, the two-stage hybrid model III has the best classification capability to forecast SMEs credit risk in SCF, which can be a useful prediction tool for China’s FIs.

 

2. 智库成果

[1] 资政建议在全国哲学社会科学工作办公室《成果要报》刊发;2021;第一完成人

[1] 研究成果纳入湖南省人民政府办公厅印发的《湖南省“十四五”农业农村现代化规划》;2021;第一完成人

[2] 资政建议在湖南大学《湖南大学资政专报》编发;获得省委省政府领导肯定性批示;2021;第一作者

 

3. 会议论文

[1] Li Zhou, Truong V Nguyen, You Zhu, “Recycling in a dual-channel supply chain: a system dynamic perspective”, Proceedings of the 21st International Working Seminar on Production Economics, Innsbruck, Austria, February 24-28, 2020.

[2] Li Zhou, You Zhu, Yong Lin, “Improving the supply chain design for senior citizen apartment in small cities of China”, IEEE2011 The 2nd International Conference on Business Management and Electronic Information, WuHan, China, May 11-13, 2011.

[3] Li Zhou, You Zhu, Yong Lin, Yong Mei Bentley, “Cloud supply chain: A conceptual model”, Proceedings of the 17th International Working Seminar on Production Economics, Innsbruck, Austria, February 20-24, 2012.


4. 专利

[1] 专利名称:网络课堂答题辅助即时反馈装置, 发明人:周莉/祝由/周宏坤,申请号:202020502306.8,申请日:2020.04.08


5. 研究项目

主持

[1] 湖南省“十四五”农业农村现代化规划前期重大研究课题:区块链技术在农业农村现代化中的应用课题研究(No. SKZ2020008),2019-2020,15万元

[2] 中国博士后科学基金面上项目:基于AI技术的P2P互联网金融平台个人信用风险研究(No. 2018M632960),2018-2019,5万元


参与

[1] 研究阐释党的十九届五中全会精神国家社会科学基金重大项目:新兴数字技术驱动下金融安全风险防控体系构建与能力建设研究(No. 21ZDA114), 首席专家:谢赤,2021-2022

[2] 国家自然科学基金面上项目:大数据环境下基于动态耦合网络的投资决策交互过程与证券市场稳定性研究(No. 71971079),主持人:谢赤,2020-2023

[3] 国家自然科学基金面上项目:复杂金融网络动态演化行为与危机传染及其控制研究(No. 71373072),主持人:谢赤,2014-2017
[4] 高等学校博士学科点专项科研基金:藕合实体经济的金融市场风险评估与协同监管研究(No. 20130161110031),主持人:谢赤,2014-2016
[5] 国家自然科学基金项目面上项目:基于多层信息溢出网络的金融机构关联性与系统性风险贡献研究(No. 71871088),主持人:王纲金,2019-2022
[6] 国家自然科学基金项目青年项目:金融市场尾部相关性网络的建模及其演化与稳定性研究(No. 71501066),主持人:王纲金,2016-2018
[7] 湖南省自然科学基金项目青年项目:金融市场间信息溢出网络的构建及其演化机制研究(No. 2017JJ3024),主持人:王纲金,2017-2019