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Portfolio & Allocation
Portfolio construction, allocation, rebalancing and risk budgeting, from Markowitz to deep RL.
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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 202327cites
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 202424cites
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
- 3 Oct 202521cites
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
- 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×
- 14 May 202515cites
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
- 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
- 29 Nov 202315cites
On Optimal Tracking Portfolio in Incomplete Markets: The Reinforcement Learning Approach
The paper uses capital injection to solve an optimal tracking portfolio problem in incomplete market models, showcasing the q-learning algorithm's performance.
arXivIn SIAM J. Control. Optim.
Latest
- 9 Oct 20263fanfare
Forecast Accuracy Is Not Trading Profit: Evolving Small Recurrent Networks for Stock Return Prediction
Tiny neuroevolved recurrent networks rank first on both forecast accuracy and daily long-short returns across four portfolios, outperforming larger transformers and proving that forecast accuracy does not guarantee trading profit.
arXiv
- 9 Oct 20263fanfare
Expected Utility Regret Rule: Minimax and Bayes Optimal Portfolio Choice
Proposes a portfolio rule that simultaneously selects class and estimates weights to minimize expected utility regret, attaining both minimax and Bayes lower bounds without requiring prior information.
arXiv
- 9 Oct 20263fanfare
MemTrial: Learning When to Trust Memory in LLM Portfolio Agents
MemTrial uses factorial design to isolate what each memory contributes to portfolio decisions by crediting experiences with their marginal effect rather than shared market moves.
arXiv
- 9 Oct 20263fanfare
Credal Machine Learning for Risk-Averse Decision Making
A method represents epistemic uncertainty with credal sets and combines them with a decision rule that avoids catastrophic predictions under conditional value-at-risk minimization.
arXiv
- 9 Oct 20262fanfare
Conditional value-at-risk under reward-penalty mechanism with applications to robust portfolio management
Derives a closed-form expression for worst-case conditional value-at-risk under reward-penalty mechanisms and distribution uncertainty, enabling optimal portfolio allocations that balance losses with downside risk.
arXiv
- 9 Oct 20262fanfare
Optimal Investment to Reach a Financial Goal: A Stochastic Control Framework
A framework characterizes optimal investment policies for reaching financial goals by deadline, revealing that policies can decrease in asset drift and need not converge to risk-free allocation.
arXiv
- 9 Oct 20262fanfare
Robust enhanced index tracking portfolio selection under distributional uncertainty
Robust optimization models minimize worst-case loss and downside risk for enhanced indexing under distributional uncertainty, beating benchmarks and standard robust approaches in out-of-sample wealth.
arXiv
- 9 Oct 20264fanfare
The Evolution of Quantitative Equity Investing: Half a Century of Alpha Research and Institutional Adaptation
Surveys fifty years of quantitative investing as a history of changing implementation boundaries from diversification through machine learning, emphasizing alpha decay and the gap between research and production systems.
SSRN
- 9 Oct 20264fanfare
Volatility Targeting, Variance Drain, and the Financialization Effect: A Century of Evidence
Analyzing 100 years of daily data, the study finds volatility scaling harmed returns before 1985 but improved them after, a shift attributed to derivatives markets and deregulation, with walk-forward Sharpe ratio of 1.01.
SSRN
- 9 Oct 20264fanfare
A Multi-Strategy Quantitative Portfolio
Eleven systematic strategies across equities, futures, and currencies achieve a 1.91 Sharpe ratio net of costs from January 2012 to June 2026, with 23.6% compound annual return.
SSRN
- 9 Oct 20263fanfare
Beliefs That Predict Returns and Beliefs That Attract Flows: Policy Insights and Sentiment Catering in Mutual Funds
The study shows that mutual fund managers' policy beliefs predict market returns and explain positive alphas, while sentiment beliefs attract flows but lack predictive power.
RePEc
- 9 Oct 20262fanfare
Optimal Currency Strategies Under Deviations From Interest Parity
Extending Campbell et al., the analysis shows empirically measured deviations from interest parity reverse optimal currency demands for emerging-market investors.
RePEc
- 2 Oct 20260cites
Admissible Portfolio Optimization: Information Constraints, Conditional Efficient Frontiers, and the Price of Causal Identification
The research makes conditioning information a decision variable subject to constraints, solving how look-ahead bias and information availability affect portfolio choice and causal identification.
arXiv
- 2 Oct 20260cites
Multiperiod bond portfolio optimization with transaction costs using a Markov Decision process
The study develops a Markov decision process framework for dynamic bond portfolio management that balances yield and interest-rate risk subject to transaction costs.
arXiv
- 2 Oct 20260cites
Taming the Greeks: Option Portfolios with Inductive Biases
A deep learning framework for options trading enforces portfolio-level delta neutrality and other risk constraints during training, improving risk-adjusted returns with lower directional exposure.
arXiv
- 2 Oct 20260cites
When Stress Tests Miss the Risk: Statistical Scenario Analysis for Financial Portfolios
Kernel scenario analysis estimates quantile predictions for portfolio stress-test gains, with online calibration to capture dependence between stressed and unstressed risk factors.
arXiv
- 2 Oct 20263fanfare
The Capacity of Equity Anomalies
Computing capacity for 35 anomalies reveals that high-alpha long legs support less than $2 billion before trading costs eliminate excess returns, while lower-alpha strategies support billions more.
SSRN
- 2 Oct 20262fanfare
Less Is More: Forecast Granularity, Estimation Error, and Portfolio Choice
Investors using machine-learning forecasts can achieve Sharpe ratios of 1.2 by adjusting the number of portfolio groups based on the forecast's information coefficient, beating standard decile sorts.
SSRN
- 2 Oct 20262fanfare
Locking in the rate or staying flexible? Mortgage refinancing around an interest rate shock
The study finds that UK borrowers shifted toward two-year fixed mortgages despite higher pricing after the 2022 rate shock, seeking flexibility and rate protection rather than minimizing immediate costs.
RePEc
- 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 20261cites
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 20261cites
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×