电子邮箱:yaoxiao18@cufe.edu.cn

教育背景

工作经历

研究方向

银行风险管理🏰，绿色金融🩰，企业管理

教学经历

本科生课程👳‍♂️👨🏿‍🚀：管理研究方法🏋🏻‍♀️🧑🏼‍🔬，应用统计，投资学，商务统计

MBA课程：数据、模型与决策，投资学

研究生课程🏞：SAS入门与应用

研究项目

[1]基于贝叶斯广义线性混合模型的公司债券信用风险测度研究(2020.01 – 2022.12)☄️，国家自然科学基金青年项目,主持人(项目编号：71901230)

[2]课程思政和OBE理念相融合的教学方法改革探索研究——以《管理研究方法》为例,北京K8凯发平台娱乐登录官方网站2022年度教学方法研究项目(课程思政专项项目),主持人

论文发表

[1] Z. Li, A. Li, A. Bellotti,X. Yao*. (2023). The profitability of online loans: A competing risks analysis on default and prepayment.European Journal of Operational Research, 306, 968-985.

[2] 姚潇🚌，李可🤸🏿‍♀️，余乐安. (2022).非平衡样本下基于生成对抗网络过抽样技术的公司债券违约风险预测研究[J].系统工程理论与实践, 42(10), 2618-2634.

[3] B. Huang,X. Yao *, Y. Luo & J. Li. (2022). Improving Financial Distress Prediction Using Textual Sentiment of Annual Reports.Annuals of Operations Research, 1-28.

https://doi.org/10.1007/s10479-022-04633-3

[4] X. Zhang, Y. Zhao &X. Yao *(2022). Forecasting corporate default risk in China.International Journal of Forecasting, 38, 1054-1070.

[5] 窦超，姚潇*,陈晓(2021).政府背景大客户与债券发行利差—基于供应链风险传递视角[J].管理科学学报, 24(9), 59-78.

[6] Z. Li, J. Zhang,X. Yao* & G. Kou (2021). How to identify early defaults in online lending: A cost-sensitive multi-layer learning framework.Knowledge-Based Systems, 221, 106963.

[7] K. Li, F. Zhou, Z. Li,X. Yao* & Y. Zhang (2021). Predicting loss given default using post-default information.Knowledge-Based Systems, 224, 107068.

[8] D. Wu,X. Yao* & J. Guo (2021). Is Textual Tone Informative or Inflated for Firm’s Future Value? Evidence from Chinese Listed Firms.Economic Modelling, 94, 513-525.

[9] L. Yu,X. Yao, X. Zhang, H. Yin & J. Liu (2020). A Novel Dual-Weighted Fuzzy Proximal Support Vector Machine with Application to Credit Risk Analysis.International Review of Financial Analysis, 71, 101577.

[10] 姚潇,吴冬晓,庞守林(2020).基于文本挖掘的管理层语调对公司债券信用利差的影响[J].经济理论与经济管理, 3, 99-112.

[11] Z. Li, K. Li,X. Yao& Q. Wen (2019). Predicting Prepayment and Default Risks of Unsecured Consumer Loans in Online Learning.Emerging Markets of Finance and Trade, 55(1), 118-132.

[12] X. Yao *, J. Crook and G. Andreeva (2017). Enhancing Two-Stage Modelling Methodology for Loss Given Default with Support Vector Machines.European Journal of Operational Research, 263(2), 679-689.

[13] X. Yao *, J. Crook and G. Andreeva (2017). Is It Obligor or Instrument That Explains Recovery Rate: Evidence from US Corporate Bond.Journal of Financial Stability, 28, 1-15.

[14] X. Yao *, J. Crook and G. Andreeva (2015). Support vector regression for loss given default modelling. European Journal of Operational Research, 240(2), 528-538.

[15] L. Yu,X. Yao(2013). A total least squares proximal support vector classifier for credit risk evaluation.Soft Computing,17(4), 643-650.

[16] L. Yu,X. Yao, Shouyang Wang, K.K. Lai (2011). Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection,Expert Systems with Applications, 38(12), 15392-15399.

[17] 姚潇,余乐安(2012).模糊近似支持向量机模型及其在信用风险评估中的应用[J].系统工程理论与实践, 3, 549-554.

其它专著

杨晟，赵阳，姚潇.“基于深度强化学习算法的股指期货交易系统与实证”,《量化实证分析在金融风险管理中的应用》，中国金融出版社🚶‍♂️👨🏼‍✈️，2021.

招生条件

优先考虑具有经济管理背景或统计和计算机编程基础较好的同学🛍，同时具备优秀的英文水平和表达能力