Library/Portfolio Optimization & Construction

A curated list of papers on portfolio optimization and construction

This post maintains a curated list of papers on portfolio optimization and construction.

The link on the title of the paper is a direct download link when available. Otherwise, it takes you to the journal page for the paper.

Click on the image to see a 15-slide deck with an overview of the paper.

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Yang, L. (2026). Harvesting factor premia across regimes: An anchor-stabilized hidden Markov framework for multifactor portfolios. Working paper, Columbia University.

This paper develops a regime-aware multifactor allocation framework that turns latent market states into implementable portfolio weights. The paper starts from a U.S. equity factor panel, estimates persistent market regimes with a Gaussian Hidden Markov Model, projects filtered regime probabilities one step ahead, and maps those probabilities into regime-conditioned mean-variance portfolios with Ledoit-Wolf covariance shrinkage, turnover penalties, and VIX or CAPE anchors. In the 2013-2023 walk-forward test, the framework does not beat passive equity benchmarks on raw bull-market return, but it substantially improves downside control: the long-only protective strategy achieves a Sharpe ratio of 1.33 and maximum drawdown of -3.64%, while the S&P 500 has a Sharpe ratio of 0.90 and maximum drawdown of -23.97%. The main message is that regime information is economically useful only when it is translated into disciplined, forward-looking, turnover-aware allocation decisions rather than treated as a standalone timing signal.

Owen, S. R. (2023). An analysis of conditional mean-variance portfolio performance using hierarchical clustering. The Journal of Finance and Data Science, 9, 100112.

This paper studies whether hierarchical clustering can improve conditional mean-variance portfolio construction by producing better ex-ante covariance estimates than a traditional Markowitz optimizer. Using CRSP monthly stock returns from 1965 to 2017, filtered for investability, the paper forms long-only, three-month buy-and-hold portfolios across 12-, 60-, and 120-month covariance look-back windows and compares cluster-optimized portfolios with Markowitz and market benchmarks. The central finding is that clustering the covariance matrix by stock-return correlations improves out-of-sample risk-adjusted performance: cluster-optimized Sharpe ratios exceed the benchmarks across look-back windows, the approach outperforms Markowitz by Sharpe ratio 54%, 68%, and 60% of the time, and it delivers smoother, lower portfolio weight changes than Markowitz. The practical message is that this is not black-box machine learning for its own sake; it is an interpretable way to condition the covariance matrix, diversify across correlated groups, and reduce the instability that often makes unconstrained mean-variance optimization hard to implement.