Asset Allocation: A Statistical Learning Approach

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多种资产之间的资源配置，这在财务管理中是至关重要的，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面临着这一问题。除了金融之外，投资组合优化问题还出现在其他行业，例如制药行业的药物开发项目投资组合、技术行业的研发项目投资组合、能源行业的能源生产资产投资组合。该奖项旨在通过建立机器学习的最新进展来改善资产配置方法的样本外性能。该奖项为学生提供研究机会，并通过将资产配置方面具有挑战性的应用问题带入课堂，丰富学生在本科和研究生阶段的经验，从而促进人力资源的教育和发展。马科维茨关于均值-方差投资组合优化的开创性工作为将最优化应用于资产配置问题奠定了理论基础。尽管这些经典贡献具有巨大的重要性和影响力，但事实仍然是，从经验的角度来看，由于从历史数据估计统计参数的局限性，马科维茨式投资组合政策的表现往往优于样本外的朴素1/N等权投资组合政策。该项目通过引入受机器学习和计量经济学最新进展启发的新的投资组合优化框架，修改了经典的马科维茨投资组合优化。在著名的LASSO回归的基础上，最近在计量经济学文献中引入了部分平均主义的最小绝对收缩和选择算子（PELASSO），以解决不同预测的最佳组合问题。受这些最新发展的启发，该项目采用PELASSO投资组合优化方法，通过正则化投资组合优化问题，将部分资产的投资组合权重分配为零，并选择和缩小投资组合中幸存资产的权重，以对冲参数估计风险。PELASSO投资组合优化旨在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、统计学、机器学习和经济学交叉的方法。该奖项反映了美国国家科学基金会的法定使命，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。

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.