标题： 自适应多任务学习用于多行业投资组合优化 显示英文标题

标题： Adaptive Multi-task Learning for Multi-sector Portfolio Optimization

摘要： 在涉及不同类别大量资产的多部门投资组合优化中，跨多个部门准确传递信息以增强模型估计既重要又具有挑战性。 在因子建模的框架下，我们提出了一种新颖的数据自适应多任务学习方法，在研究的多个部门中量化并学习主要时间子空间（由因子张成）之间的相关性。 这种方法不仅提高了多个因子模型的同时估计，还增强了对这些因子模型准确恢复高度依赖的多部门投资组合优化。 此外，开发了一种新颖且易于实现的算法，称为投影惩罚主成分分析，以完成多任务学习过程。 多种模拟设计和对Russell 3000指数每日收益率数据的实际应用展示了多任务学习方法的优势。 显示英文摘要

摘要： Accurate transfer of information across multiple sectors to enhance model estimation is both significant and challenging in multi-sector portfolio optimization involving a large number of assets in different classes. Within the framework of factor modeling, we propose a novel data-adaptive multi-task learning methodology that quantifies and learns the relatedness among the principal temporal subspaces (spanned by factors) across multiple sectors under study. This approach not only improves the simultaneous estimation of multiple factor models but also enhances multi-sector portfolio optimization, which heavily depends on the accurate recovery of these factor models. Additionally, a novel and easy-to-implement algorithm, termed projection-penalized principal component analysis, is developed to accomplish the multi-task learning procedure. Diverse simulation designs and practical application on daily return data from Russell 3000 index demonstrate the advantages of multi-task learning methodology.