主要简介 |
姓名: 王迪 性别: 女 出生年月:1993 年 03 月 籍贯:黑龙江 民族:汉族 研究方向:公司财务与公司治理 |
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担任课程 |
本科:中级财务会计、会计管理信息系统 |
教育背景 |
2019年09月–2023年06月 暨南大学,财务管理,管理学博士 2015年08月–2018年03月 哈尔滨理工大学,会计学,管理学硕士 2011年09月–2015年07月 哈尔滨理工大学,会计学,管理学学士 |
工作经历 |
2023年12月–至今 betway必威 |
科研成果 |
论文: [1] Di Wang, Zhanchi Wu, Junjie You, et al. Does hometown connection between chairmen and CEOs improve compensation–performance sensitivity in China?, Humanities and Social Sciences Communications, 2023, 10(1): 1-12.(SSCI Q1) [2] Di Wang, Zhanchi Wu, Bangzhu Zhu. Controlling Shareholder Characteristics and Corporate Debt Default Risk: Evidence Based on Machine Learning, Emerging Markets Finance and Trade, 2022, 58(12): 3324-3339.(SSCI Q1) [3] 王玉冬, 王迪, 王珊珊. 高新技术企业创新资金配置风险预警的FOA-SVM模型及实证, 系统工程理论与实践, 2018, 38(11): 2852-2862.(CSSCI) [4] 王玉冬, 王迪(通讯作者), 王珊珊. 基于PSO-BP和FOA-BP神经网络的财务危机预警模型比较, 统计与决策, 2018, 38(11): 177-179.(CSSCI) 项目: 2022年01月-2025年12月,参与,国家自然科学基金面上项目:“终极控制人特征与公司债务违约:作用机理与预警监控”(项目批准号 72173057) 2021年01月-2023年12月,参与,广东省自然科学基金面上项目:“终极控制人多元化投资:动因识别、风险传导与预警监控”(项目批准2021A1515011536) |
联系方式 |
邮箱:willowdee2020@sina.com |
Dr. Di Wang
Personal Information |
Gender: Female Birth: 1993.03 Nationality: Chinese Research Interests: Corporate Finance; Corporate Governance |
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Courses Taught |
Bachelor: Intermediate Financial Accounting; Accounting Management Information System |
Educational History |
Jinan University Guangzhou,China Ph.D. in Management Sep.2019-Jun.2023 Harbin University of Science and Technology Harbin,Heilongjiang,China M.D. in Management Aug.2015-Mar.2018 B.D. in Management Sep.2011-Jul.2015 |
Work Experience |
Guangdong University of Foreign Studies Guangzhou, China Dec.2023 - Present |
Research Outputs |
[1]Di Wang, Zhanchi Wu, Junjie You, et al. Does hometown connection between chairmen and CEOs improve compensation–performance sensitivity in China?, Humanities and Social Sciences Communications, 2023, 10(1): 1-12.(SSCI Q1) [2]Di Wang, Zhanchi Wu, Bangzhu Zhu. Controlling Shareholder Characteristics and Corporate Debt Default Risk: Evidence Based on Machine Learning, Emerging Markets Finance and Trade, 2022, 58(12): 3324-3339.(SSCI Q1) [3]Wang Y D, Wang D, Wang S S. FOA-SVM model and empirical study on risk early warning of innovation fund allocation of high-tech enterprises[J]. Systems Engineering-Theory & Practice, 2018, 38(11): 2852-2862.(In Chinese, CSSCI) [4]Wang Y D, Wang D, Wang S S.Comparison of financial crisis early warning models based on PSO-BP and FOA-BP neural networks, Statistics&Decision, 2018, 38(11): 177-179.(In Chinese, CSSCI) |
Contact |
E-mail:willowdee2020@sina.com |
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