This paper proposes a machine learning-based framework for asset selection and portfolio construction, termed the Best-Path Algorithm Sparse Graphical Model (BPASGM). The method extends the Best-Path Algorithm (BPA) by mapping linear and non-linear dependencies among a large set of financial assets into a sparse graphical model satisfying a structural Markov property. Based on this representation, BPASGM performs a dependence-driven screening that removes positively or redundantly connected assets, isolating subsets that are conditionally independent or negatively correlated. This step is designed to enhance diversification and reduce estimation error in high-dimensional portfolio settings. Portfolio optimization is then conducted on the selected subset using standard mean-variance techniques. BPASGM does not aim to improve the theoretical mean-variance optimum under known population parameters, but rather to enhance realized performance in finite samples, where sample-based Markowitz portfolios are highly sensitive to estimation error. Monte Carlo simulations show that BPASGM-based portfolios achieve more stable risk-return profiles, lower realized volatility, and superior risk-adjusted performance compared to standard mean-variance portfolios. Empirical results for U.S. equities, global stock indices, and foreign exchange rates over 1990-2025 confirm these findings and demonstrate a substantial reduction in portfolio cardinality. Overall, BPASGM offers a statistically grounded and computationally efficient framework that integrates sparse graphical modeling with portfolio theory for dependence-aware asset selection.

翻译：本文提出了一种基于机器学习的资产选择与投资组合构建框架，称为最佳路径算法稀疏图模型（BPASGM）。该方法扩展了最佳路径算法（BPA），通过将大量金融资产间的线性和非线性依赖关系映射至满足结构马尔可夫性质的稀疏图模型来实现。基于此表示，BPASGM执行依赖关系驱动的筛选，剔除正向或冗余连接的资产，从而分离出条件独立或负相关的资产子集。此步骤旨在增强高维投资组合设置中的分散化并降低估计误差。随后，使用标准均值-方差技术对所选子集进行投资组合优化。BPASGM的目标并非在已知总体参数下改进理论上的均值-方差最优解，而是旨在提升有限样本中的实际表现，因为基于样本的马科维茨投资组合对估计误差高度敏感。蒙特卡洛模拟表明，与标准均值-方差投资组合相比，基于BPASGM的投资组合实现了更稳定的风险-收益特征、更低的已实现波动率以及更优的风险调整后表现。对1990-2025年间美国股票、全球股指及外汇汇率的实证结果证实了这些发现，并显示出投资组合基数的大幅减少。总体而言，BPASGM提供了一个统计基础坚实且计算高效的框架，它将稀疏图建模与投资组合理论相结合，实现了依赖关系感知的资产选择。