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Portfolio & Allocation
Portfolio construction, allocation, rebalancing and risk budgeting, from Markowitz to deep RL.
- Papers featured
- 609
- Last 12 months
- 36
- Cited 100+
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Featured papers in this topic with the most citations today.
- 29 Nov 202335cites
Missing values handling for machine learning portfolios
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.
arXivIn Journal of Financial EconomicsFeatured 2×
- 12 Oct 202326cites
Technical Note - An Unexpected Stochastic Dominance: Pareto Distributions, Dependence, and Diversification
The research suggests that diversifying super-Pareto losses increases portfolio risk, discouraging risk sharing in market equilibrium.
arXivIn Oper. Res.
- 20 Jun 202423cites
Dynamic asset allocation with asset-specific regime forecasts
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.
arXivIn Annals of Operations Research
- 23 Jan 202423cites
MAD risk parity portfolios
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.
arXivIn Annals of Operations Research
- 7 Feb 202418cites
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management
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.
arXivIn Adaptive Agents and Multi-Agent SystemsFeatured 2×
- 24 May 202317cites
Risk Budgeting Allocation for Dynamic Risk Measures
Risk budgeting allocation approach developed using dynamic risk contributions and deep learning.
arXivIn Oper. Res.
- 24 Jul 202416cites
Explainable post hoc portfolio management financial policy of a Deep Reinforcement Learning agent
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.
arXivIn PLOS ONEFeatured 2×
- 5 Sep 202415cites
Sparse spanning portfolios and under-diversification with second-order stochastic dominance
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.
arXiv
- 7 Feb 202415cites
Sparse spanning portfolios and under-diversification with second-order stochastic dominance
The study explores whether relaxing sparsity constraints on portfolios enhances investment opportunities, finding no benefit from expanding a sparse opportunity set beyond 45 assets.
SSRN
- 3 Oct 202514cites
AlphaSAGE: Structure-Aware Alpha Mining via GFlowNets for Robust Exploration
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.
arXiv
- 14 May 202513cites
Loss-Versus-Rebalancing under Deterministic and Generalized block-times
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.
arXiv
- 20 Dec 202312cites
Data-Driven Merton's Strategies via Policy Randomization
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.
arXivFeatured 3×
Latest
- 25 Sep 20260cites
Active Portfolio Management in Concentrated Equity Markets
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.
arXiv
- 25 Sep 20260cites
Decision-Focused Learning for Mean-Variance Portfolio Optimization via KKT-Based Reformulation
Proposes a KKT-based decision-focused learning method that trains mean-variance portfolio models by directly minimizing downstream portfolio loss while preserving all constraints.
arXiv
- 25 Sep 20260cites
The Critical Line Algorithm and the Constrained LASSO: One Curve, Two Literatures
Shows that mean-variance portfolio selection and the constrained LASSO trace identical piecewise-linear solution paths, mapping their parametrizations exactly.
arXiv
- 25 Sep 20260cites
Optimal Investment and Consumption in Financial Markets with Integrated Variance Clocks
Characterizes optimal consumption and investment strategies in markets with stochastic volatility clocks using infinite-horizon backward SDEs, extending to rough and hyper-rough regimes.
arXiv
- 25 Sep 20263fanfare
Welcome to the Factor Zoo: Where Mutual Fund Alpha Hides
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.
SSRN
- 25 Sep 20262fanfare
Data-Driven Minimax-Regret Portfolio Optimization under Tail-Risk Ambiguity
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.
SSRN
- 25 Sep 20263fanfare
HKC05 - Household Portfolios, Corporate Leverage, and the Supply Side of Monetary Policy
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.
RePEc
- 16 Apr 20260cites
Causal PDE-Control Models for Dynamic Portfolio Optimization with Latent Drivers
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.
arXiv
- 16 Apr 20260cites
From Core to Periphery? Assessing Remote Works Potential to Rebalance EU Regional Development
Remote work after the pandemic is causing people to move within cities for better quality of life, rather than relocating to rural areas.
arXiv
- 16 Jan 20261cites
DeePM: Regime-Robust Deep Learning for Systematic Macro Portfolio Management
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.
arXiv
- 28 Dec 2025108shares
Vaccine Innovation Funding Strategy
A portfolio approach to drug development may improve investment returns and speed up vaccine creation.
SSRNFeatured 2×
- 28 Dec 20253cites
Regulating Cash Holdings: Assessing Lost Returns in Mutual Funds
Israeli mutual funds hold excessive cash, indicating a need for better liquidity management to reduce redemption risks.
SSRNFeatured 2×
- 28 Dec 202566shares
Sustainable Investment in Climate
Global investments in environmental and climate projects are diversifying as investors integrate more green initiatives into their portfolios.
SSRNFeatured 2×
- 28 Dec 20250cites
Sparse Risk Parity Enhanced Index Tracking Portfolio
It tackles a sparse risk parity portfolio problem for index tracking while managing asset risks, with successful results on the SP 500.
SSRNFeatured 2×
- 28 Dec 2025440shares
Tail Risk-Managed Portfolio Strategies
It develops real-time Tail Risk-Managed portfolios that minimize tail risks and enhance risk-return profiles compared to standard strategies.
SSRNFeatured 2×
- 19 Dec 20250cites
Smart Data Portfolios: A Governance Framework for AI Training Data
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.
arXiv
- 14 Dec 20250cites
Exploratory Mean-Variance with Jumps: An Equilibrium Approach
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.
arXiv
- 1 Dec 2025154shares
Mutual Fund Decline in 401(k)s
This research highlights the rise of collective investment trusts in 401k plans due to their lower costs and tailored options for investors.
SSRNFeatured 2×
- 1 Dec 2025128shares
Behavioral Biases in Fund Management
The study looks at how mutual fund performance is influenced by internal biases when large amounts of capital are invested.
SSRNFeatured 2×
- 1 Dec 20250cites
Portfolio Optimization via Transfer Learning
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.
arXiv
- 1 Dec 20250cites
Black-Litterman and ESG Portfolio Optimization
A unique portfolio optimization method that incorporates ESG scores into the Black-Litterman framework shows significant returns with daily updates.
arXiv
- 12 Nov 20253cites
Effective and Scalable Programs to Facilitate Labor Market Transitions for Women in Technology
In Poland, cheap online portfolio challenges and one‑on‑one mentoring sharply increased women’s tech employment, and data-driven targeting improved admissions.
arXiv
- 4 Nov 20251cites
A mathematical study of the excess growth rate
- 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.
arXiv
- 27 Oct 20250cites
An Empirical study on Mutual fund factor-risk-shifting and its intensity on Indian Equity Mutual funds
Finds Indian mutual funds often change investment styles, which can materially alter their risk‑adjusted returns.
arXiv
- 27 Oct 20250cites
Managing Portfolios Across the Return Distribution
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.
arXiv
- 27 Oct 20250cites
Optimal allocations with distortion risk measures and mixed risk attitudes
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.
arXiv
- 24 Oct 202516shares
Brazilian ML Portfolios
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.
RePEc
- 9 Oct 20250cites
FR-LUX: Friction-Aware, Regime-Conditioned Policy Optimization for Implementable Portfolio Management
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.
arXiv
- 9 Oct 20255cites
Signed network models for portfolio optimization
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.
arXiv
- 9 Oct 20250cites
Inverse Portfolio Optimization with Synthetic Investor Data: Recovering Risk Preferences under Uncertainty
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.
arXivFeatured 2×