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祝由

发布于:2019-09-06 星期五 19:29:52 点击数:3789

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助理教授、硕士生导师。

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助理教授、硕士生导师,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。

 

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

        2019.07~               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 


 

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

运营管理、创业管理(全英文课程)、企业理论(全英文课程)、企业管理实验(全英文课程)、战略管理(ITCIE 全英文课程)

研究成果

1. 论文

[1] Xinyi Wang, Deming Zeng, Haiwen Dai, You Zhu*. Making the right business decision: Forecasting the binary NPD strategy inChinese 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 madebetween 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 externalconditions 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.


[2] 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]

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.


[3] 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]

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.

 

[4] 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]

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.


[5] 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]

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. 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] 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

[2] 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
[3] 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
[4] 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
[5] 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
[6] 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