--- title: Portfolio & Allocation url: https://www.ml-quant.com/topics/portfolio-allocation/ site: ML-Quant (https://www.ml-quant.com) updated: 2026-09-26 license: Summaries CC BY 4.0; links go to the original sources index: https://www.ml-quant.com/llms.txt --- # Portfolio & Allocation Portfolio construction, allocation, rebalancing and risk budgeting, from Markowitz to deep RL. 609 papers featured; 36 in the last 12 months. Papers featured per quarter: 2023 Q2 34, 2023 Q3 67, 2023 Q4 83, 2024 Q1 66, 2024 Q2 76, 2024 Q3 60, 2024 Q4 42, 2025 Q1 70, 2025 Q2 58, 2025 Q3 17, 2025 Q4 26, 2026 Q1 1, 2026 Q2 2, 2026 Q3 7 ## Most cited - [Missing values handling for machine learning portfolios](https://www.ml-quant.com/papers/arxiv/2207.13071/): 35 citations. The study shows that using cross-sectional means for simple imputation is effective in dealing with missing values in machine learning-constructed portfolios, as complex imputations can cause underperformance due to estimation noise. - [Technical Note - An Unexpected Stochastic Dominance: Pareto Distributions, Dependence, and Diversification](https://www.ml-quant.com/papers/arxiv/2208.08471/): 26 citations. The research suggests that diversifying super-Pareto losses increases portfolio risk, discouraging risk sharing in market equilibrium. - [Dynamic asset allocation with asset-specific regime forecasts](https://www.ml-quant.com/papers/arxiv/2406.09578/): 23 citations. The article introduces a new framework that enhances multi-asset portfolio construction by creating custom regime forecasts for each asset, proven effective through a practical study on a multi-asset portfolio. - [MAD risk parity portfolios](https://www.ml-quant.com/papers/arxiv/2110.12282/): 23 citations. Features & Performance: A study using the Mean Absolute Deviation (MAD) to measure risk in the Risk Parity (RP) model found that RP strategies typically perform between minimum risk and equally weighted strategies. - [Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management](https://www.ml-quant.com/papers/arxiv/2402.00515/): 18 citations. The piece introduces a multi-agent and self-adaptive framework (MASA) for portfolio management, using reinforcement learning to balance returns and risks, providing market trend feedback and outperforming other similar approaches. - [Risk Budgeting Allocation for Dynamic Risk Measures](https://www.ml-quant.com/papers/arxiv/2305.11319/): 17 citations. Risk budgeting allocation approach developed using dynamic risk contributions and deep learning. - [Explainable post hoc portfolio management financial policy of a Deep Reinforcement Learning agent](https://www.ml-quant.com/papers/arxiv/2407.14486/): 16 citations. A new Explainable Deep Reinforcement Learning (XDRL) method for portfolio management has been developed, combining Proximal Policy Optimization with explainable techniques for better transparency in investment predictions. - [Sparse spanning portfolios and under-diversification with second-order stochastic dominance](https://www.ml-quant.com/papers/arxiv/2402.01951/): 15 citations. A new method for estimating sparse second-order stochastic spanning suggests no advantage in expanding a sparse opportunity set beyond 45 assets, with the best sparse portfolio investing in 10 sectors. - [Sparse spanning portfolios and under-diversification with second-order stochastic dominance](https://www.ml-quant.com/papers/ssrn/4713517/): 15 citations. The study explores whether relaxing sparsity constraints on portfolios enhances investment opportunities, finding no benefit from expanding a sparse opportunity set beyond 45 assets. - [AlphaSAGE: Structure-Aware Alpha Mining via GFlowNets for Robust Exploration](https://www.ml-quant.com/papers/arxiv/2509.25055/): 14 citations. Alpha Mining: AlphaSAGE, a new framework for automated alpha mining in quantitative finance, uses a structure-aware encoder and Generative Flow Networks to overcome challenges and outperforms existing methods in creating a diverse and predictive portfolio of alphas. - [Loss-Versus-Rebalancing under Deterministic and Generalized block-times](https://www.ml-quant.com/papers/arxiv/2505.05113/): 13 citations. A study reveals that constant block intervals in blockchain settings provide the best protection against arbitrage for Automated Market Makers' liquidity providers, using random walk theory. - [Data-Driven Merton's Strategies via Policy Randomization](https://www.ml-quant.com/papers/arxiv/2312.11797/): 12 citations. The study applies reinforcement learning to determine optimal portfolio policies in an incomplete market, showing its efficiency and robustness compared to the traditional plug-in method. ## Latest - [Active Portfolio Management in Concentrated Equity Markets](https://www.ml-quant.com/papers/arxiv/2609.27113/) (2026-09-25): Formulates a stochastic control problem for actively allocating between equal-weighted and market portfolios based on a diversity-dispersion model, outperforming passive strategies during market bubbles. - [Decision-Focused Learning for Mean-Variance Portfolio Optimization via KKT-Based Reformulation](https://www.ml-quant.com/papers/arxiv/2609.21427/) (2026-09-25): Proposes a KKT-based decision-focused learning method that trains mean-variance portfolio models by directly minimizing downstream portfolio loss while preserving all constraints. - [The Critical Line Algorithm and the Constrained LASSO: One Curve, Two Literatures](https://www.ml-quant.com/papers/arxiv/2609.25704/) (2026-09-25): Shows that mean-variance portfolio selection and the constrained LASSO trace identical piecewise-linear solution paths, mapping their parametrizations exactly. - [Optimal Investment and Consumption in Financial Markets with Integrated Variance Clocks](https://www.ml-quant.com/papers/arxiv/2609.26349/) (2026-09-25): Characterizes optimal consumption and investment strategies in markets with stochastic volatility clocks using infinite-horizon backward SDEs, extending to rough and hyper-rough regimes. - [Welcome to the Factor Zoo: Where Mutual Fund Alpha Hides](https://www.ml-quant.com/papers/ssrn/7508299/) (2026-09-25): Using factor selection, the study finds mean active alpha of plus 9 basis points monthly for mutual funds, reversing the no-alpha conclusion when benchmarks are tailored to each fund. - [Data-Driven Minimax-Regret Portfolio Optimization under Tail-Risk Ambiguity](https://www.ml-quant.com/papers/ssrn/7486600/) (2026-09-25): The research proposes a data-driven portfolio method that blends tail-risk models and projects onto valid mixtures, providing bounds on Expected Shortfall regret without Wasserstein assumptions. - [HKC05 - Household Portfolios, Corporate Leverage, and the Supply Side of Monetary Policy](https://www.ml-quant.com/papers/repec/cxv-wpaper-2602/) (2026-09-25): Corporate leverage affects how monetary tightening transmits to the real economy: equity holders lose wealth while safe-asset holders are cushioned, raising the sacrifice ratio. - [Causal PDE-Control Models for Dynamic Portfolio Optimization with Latent Drivers](https://www.ml-quant.com/papers/arxiv/2509.09585/) (2026-04-16): Causal PDE-Control Models (CPCMs) offer a strong and clear framework for portfolio allocation that combines causal factors and complex filtering, outperforming standard econometric and machine-learning techniques. - [From Core to Periphery? Assessing Remote Works Potential to Rebalance EU Regional Development](https://www.ml-quant.com/papers/arxiv/2604.08252/) (2026-04-16): Remote work after the pandemic is causing people to move within cities for better quality of life, rather than relocating to rural areas. - [DeePM: Regime-Robust Deep Learning for Systematic Macro Portfolio Management](https://www.ml-quant.com/papers/arxiv/2601.05975/) (2026-01-16): Deep Learning for Portfolio Management: DeePM uses deep learning to improve macro portfolio management, delivering better risk-adjusted returns than traditional methods across various economic conditions. - [Vaccine Innovation Funding Strategy](https://www.ml-quant.com/papers/ssrn/4480682/) (2025-12-28): A portfolio approach to drug development may improve investment returns and speed up vaccine creation. - [Regulating Cash Holdings: Assessing Lost Returns in Mutual Funds](https://www.ml-quant.com/papers/ssrn/4478272/) (2025-12-28): Israeli mutual funds hold excessive cash, indicating a need for better liquidity management to reduce redemption risks. - [Sustainable Investment in Climate](https://www.ml-quant.com/papers/ssrn/4475732/) (2025-12-28): Global investments in environmental and climate projects are diversifying as investors integrate more green initiatives into their portfolios. - [Sparse Risk Parity Enhanced Index Tracking Portfolio](https://www.ml-quant.com/papers/ssrn/4470609/) (2025-12-28): It tackles a sparse risk parity portfolio problem for index tracking while managing asset risks, with successful results on the SP 500. - [Tail Risk-Managed Portfolio Strategies](https://www.ml-quant.com/papers/ssrn/4463810/) (2025-12-28): It develops real-time Tail Risk-Managed portfolios that minimize tail risks and enhance risk-return profiles compared to standard strategies. - [Smart Data Portfolios: A Governance Framework for AI Training Data](https://www.ml-quant.com/papers/arxiv/2512.16452/) (2025-12-19): The Smart Data Portfolio framework defines data governance in AI as a trade-off between information risk and fairness, creating a Governance-Efficient Frontier for optimal data allocation in AI services. - [Exploratory Mean-Variance with Jumps: An Equilibrium Approach](https://www.ml-quant.com/papers/arxiv/2512.09224/) (2025-12-14): This study uses Reinforcement Learning to solve the Mean-Variance Portfolio Optimization problem, creating a profitable investment strategy that adapts to changing preferences over time. - [Mutual Fund Decline in 401(k)s](https://www.ml-quant.com/papers/ssrn/4960502/) (2025-12-01): This research highlights the rise of collective investment trusts in 401k plans due to their lower costs and tailored options for investors. - [Behavioral Biases in Fund Management](https://www.ml-quant.com/papers/ssrn/4961553/) (2025-12-01): The study looks at how mutual fund performance is influenced by internal biases when large amounts of capital are invested. - [Portfolio Optimization via Transfer Learning](https://www.ml-quant.com/papers/arxiv/2511.21221/) (2025-12-01): A portfolio strategy leveraging transfer learning improves investment results by filtering useful information from noise, leading to better performance as indicated by a higher Sharpe ratio. - [Black-Litterman and ESG Portfolio Optimization](https://www.ml-quant.com/papers/arxiv/2511.21850/) (2025-12-01): A unique portfolio optimization method that incorporates ESG scores into the Black-Litterman framework shows significant returns with daily updates. - [Effective and Scalable Programs to Facilitate Labor Market Transitions for Women in Technology](https://www.ml-quant.com/papers/arxiv/2211.09968/) (2025-11-12): In Poland, cheap online portfolio challenges and one‑on‑one mentoring sharply increased women’s tech employment, and data-driven targeting improved admissions. - [A mathematical study of the excess growth rate](https://www.ml-quant.com/papers/arxiv/2510.25740/) (2025-11-04): - Excess Growth - Excess Rate - Growth Excess - Surplus Growth - Overgrowth - Growth Surplus Recommended: Excess Growth (keeps meaning but is more concise).: The paper proves that a central portfolio metric—the excess growth rate—can be exactly described using basic information‑theory ideas and a few natural axioms. In short, it shows that the extra growth a portfolio achieves is essentially an information quantity, so portfolio performance can be understood like information gain. - [An Empirical study on Mutual fund factor-risk-shifting and its intensity on Indian Equity Mutual funds](https://www.ml-quant.com/papers/arxiv/2510.19619/) (2025-10-27): Finds Indian mutual funds often change investment styles, which can materially alter their risk‑adjusted returns. - [Managing Portfolios Across the Return Distribution](https://www.ml-quant.com/papers/arxiv/2510.19271/) (2025-10-27): Finds that investors targeting specific outcome quantiles change volatility exposure (cutting risk to protect downside or seeking dispersion for upside) and introduces a distributional actor‑critic to learn such strategies. - [Optimal allocations with distortion risk measures and mixed risk attitudes](https://www.ml-quant.com/papers/arxiv/2510.18236/) (2025-10-27): Groups people with similar risk attitudes, reducing the n‑agent risk‑sharing problem to a two‑agent (risk‑averse vs risk‑seeking) model with clear existence conditions. - [Brazilian ML Portfolios](https://www.ml-quant.com/papers/repec/eee-ememar-v-51-y-2022-i-pb-s1566014122000085/) (2025-10-24): The research investigates the use of machine learning to predict stock returns in Brazil, showing that an Equal Risk Contribution approach greatly enhances risk-adjusted returns. - [FR-LUX: Friction-Aware, Regime-Conditioned Policy Optimization for Implementable Portfolio Management](https://www.ml-quant.com/papers/arxiv/2510.02986/) (2025-10-09): FR-LUX is a new reinforcement learning framework that learns trading policies and remains stable across different market conditions, offering high average Sharpe ratio and excellent risk-return efficiency. - [Signed network models for portfolio optimization](https://www.ml-quant.com/papers/arxiv/2510.05377/) (2025-10-09): The study shows that using negative edges in weighted signed network representations of financial markets can help reduce portfolio risk, performing on par with traditional models. - [Inverse Portfolio Optimization with Synthetic Investor Data: Recovering Risk Preferences under Uncertainty](https://www.ml-quant.com/papers/arxiv/2510.06986/) (2025-10-09): The research introduces an inverse portfolio optimization framework that can deduce latent investor preferences from observed portfolio allocations, offering a robust tool for preference inference and portfolio design.