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Asset Allocation: A Statistical Learning Approach

资产配置：一种统计学习方法

基本信息

批准号：
1916616
负责人：
Vadim Linetsky
金额：
$ 39.87万
依托单位：
Northwestern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916616&HistoricalAwards=false
关键词：
Asset Allocation Statistical Learning Approach

项目摘要

This award will contribute to the advancement of national prosperity and economic welfare by developing new and improved methods to solve optimal asset allocation problems. Portfolio optimization problems involving allocation of resources across multiple assets are of fundamental importance in financial management and are faced daily by financial institutions, asset managers, pension plans, university endowments, insurance companies, and individual investors. Beyond finance, portfolio optimization problems arise in other industries, such as portfolios of drug development projects in the pharmaceutical industry, portfolios of research and development projects in the technology industry, portfolios of energy generating assets in the energy industry. This award aims to improve out-of-sample performance of asset allocation methods by building on recent advances in machine learning. The award contributes to the education and development of human resources by providing research opportunities to students and enriching student experience at undergraduate and graduate levels by bringing challenging applied problems in asset allocation to the classroom.The pioneering work of Markowitz on mean-variance portfolio optimization laid the theoretical foundations of applying optimization to the asset allocation problem. Notwithstanding the enormous importance and influence of these classical contributions, the fact remains that, from an empirical point of view, Markowitz-style portfolio policies are often outperformed by the naive 1/N equal weights portfolio policy out of sample due because of limitations associated with estimating statistical parameters from historical data. This project modifies the classical Markowitz portfolio optimization by introducing a new portfolio optimization framework inspired by recent advances in machine learning and econometrics. A partially egalitarian least absolute shrinkage and selection operator (PELASSO) building on the celebrated LASSO regression was recently introduced in the econometrics literature in the context of solving the problem of optimally combining different forecasts. Inspired by these recent developments, this project employs the PELASSO portfolio optimization approach by regularizing the portfolio optimization problem to assign portfolio weights of some of the assets to zero and to select and shrink weights of the surviving assets in the portfolio towards equal weights to hedge against parameter estimation risk. The PELASSO portfolio optimization aims to achieve improved out-of-sample performance and is evaluated empirically using rich data from high frequency econometric sources. The project utilizes methods at the intersection of optimization, statistics, machine learning and economics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项将通过开发新的和改进的方法来解决最佳资产配置问题，为促进国家繁荣和经济福利做出贡献。投资组合优化问题涉及在多个资产之间分配资源，在财务管理中具有根本的重要性，金融机构、资产管理者、养老金计划、大学捐赠基金、保险公司和个人投资者每天都要面对这些问题。除了金融之外，其他行业也会出现投资组合优化问题，例如制药行业的药物开发项目投资组合、科技行业的研发项目投资组合、能源行业的发电资产投资组合。该奖项旨在通过利用机器学习的最新进展来提高资产配置方法的样本外性能。该奖项通过为学生提供研究机会，丰富学生在本科和研究生阶段的经验，将资产配置中具有挑战性的应用问题带入课堂，为人力资源的教育和开发做出了贡献。Markowitz在均值-方差投资组合优化方面的开创性工作奠定了将优化应用于资产配置问题的理论基础。尽管这些经典的贡献的巨大的重要性和影响力，事实仍然是，从经验的角度来看，Markowitz风格的投资组合政策往往优于天真的1/N等权重的投资组合政策的样本，由于与估计统计参数的历史数据的局限性。该项目通过引入一个新的投资组合优化框架来修改经典的Markowitz投资组合优化，该框架受到机器学习和计量经济学最新进展的启发。最近在计量经济学文献中引入了一种建立在著名的LASSO回归基础上的部分平均主义最小绝对收缩和选择算子（PELASSO），用于解决不同预测的最优组合问题。受这些最新发展的启发，本项目采用PELASSO投资组合优化方法，通过正则化投资组合优化问题，将一些资产的投资组合权重分配为零，并选择和收缩投资组合中幸存资产的权重，以对冲参数估计风险。PELASSO投资组合优化的目的是实现改进的样本外性能，并使用来自高频计量经济学来源的丰富数据进行经验评估。该项目利用了优化、统计、机器学习和经济学的交叉方法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。