财务造假的早期识别是监管层与业界的共同挑战,“单点深入调查”由于成本高昂并不具备普遍的可行性。基于大量历史数据和人工智能(AI)、机器学习(ML)等前沿技术,穆迪分析构建了一套甄别财务造假、评估财报质量的评分标准和预警工具,可以在事发前通过“批量扫描”快速识别需要特别关注的公司。目前,我们已测试了中国股票和债券市场的8,000多家公司,该工具能平均提前1~2年识别出80~95%(根据不同统计口径)的问题公司(依据监管机构的公开调查及处罚决定)。
在本次网络研讨会中,赵博士将介绍研发背景、方法论、数据、回溯验证等细节,并与大家分享此工具在一些现实的市场案例中的应用。


网络研讨会可以利用手机、电脑等智能终端在线参与,或者事后观看回放(仅限已经注册的账号)。欢迎大家积极报名,期待您的加入!
主要议题包括:
• 背景介绍:我们为什么要研发这个工具
• 细节分享:工具背后的方法论、数据、回溯验证等
• 案例分析:此工具在一些现实的市场案例中的应用

讲者:

Janet Zhao 赵吟清 量化研究部 资深总监
Min Wu 吴旻 量化研究部 副总监

Following the commencement of TRIM in 2016, there are has been a multi phased approach to the ECB’s TRIM exercise. As we are gearing up for a year of further TRIM exercises and transitioning to review of wholesale and low default portfolios, Moody’s would like to host this webinar to provide:

  1. Further insight to the challenges in the market thus far
  2. The common themes across Europe
  3. Remediation and best practice approaches

Following the commencement of TRIM in 2016, there are has been a multi phased approach to the ECB’s TRIM exercise. As we are gearing up for a year of further TRIM exercises and transitioning to review of wholesale and low default portfolios, Moody’s would like to host this webinar to provide:

  1. Further insight to the challenges in the market thus far
  2. The common themes across Europe
  3. Remediation and best practice approaches