Data Driven Optimally Outperforming Without Dynamic Programming

时间:2023-05-25         阅读:


主 题Data Driven Optimally Outperforming Without Dynamic Programming

主讲人加拿大滑铁卢大学 李玉英教授

主持人统计学院 兰伟教授

时间:5月26日 10:00-11:00


主办单位:统计研究中心 统计学院 科研处


Yuying Li is a professor at the Cheriton School of Computer Science at the University of Waterloo in Canada. Prior to joining UW, she was a senior research associate at Cornell University 1988-2005. She is also the recipient of the 1993 Leslie Fox first Prize in numerical analysis competition held at Oxford England. Her research interests include financial data science, machine learning, computational finance, and computational optimization. Li is currently an associate editor for Journal of Computational Finance, as well as Journal of Finance and Data Science.

李玉英,加拿大滑铁卢大学Cheriton计算机科学学院的教授。在加入滑铁卢大学之前,她于1988年至2005年在康奈尔大学担任高级研究员。她也是1993年在英国牛津大学举行的Leslie Fox数值分析比赛一等奖的获得者。她的研究兴趣包括金融数据科学、机器学习、计算金融和计算优化。她目前是Journal of Computational Finance和Journal of Finance and Data Science的副主编。


We propose a data driven learning framework to learn stochastic optimal asset allocation strategies without dynamic programming (DP). Our proposed neural network (NN) Policy Function Approximation (PFA) approach learns the optimal dynamic policies directly from data samples, which can either come from simulating a parametric model or resampling market observations directly.

Resampling non-parametrically approximates the sampling distribution of the least prejudiced empirical distribution. We use block resample market data to generate training and testing data sets. We formally establish additionally that using block resampling, for typical data lengths and expected block sizes in finance, the probability of repeating a sample path, even with 1,000,000 random path draws, is negligible.

For outperforming a benchmark, we propose suitable objective functions, which are consistent with asset allocation performance evaluation metrics in financial industry. Specifically, we propose to use information ratio (IO) and tracking differences as objective functions. Using the proposed data driven approach, objective functions, and block resampled market data, we discover robust and higher performance strategies over a benchmark by allocating over equity and bond market indices, as well as factor investing assets. We contrast and assess testing outperformance based on terminal wealth distributions.


重采样非参数地近似于最小偏见经验分布的抽样分布。主讲人使用区块重采样市场数据来生成训练和测试数据集。我们还正式确定,使用区块重采样,对于金融中的典型数据长度和预期区块大小,即使有 1000000 个随机路径绘制,重复样本路径的概率也可以忽略不计。