https://www.ml-quant.com/topics/portfolio-allocation/Portfolio construction, allocation, rebalancing and risk budgeting, from Markowitz to deep RL. en https://www.ml-quant.com/papers/arxiv/2610.07825/https://www.ml-quant.com/papers/arxiv/2610.07825/Fri, 09 Oct 2026 07:00:00 +0000Tiny 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. https://www.ml-quant.com/papers/arxiv/2610.02290/https://www.ml-quant.com/papers/arxiv/2610.02290/Fri, 09 Oct 2026 07:00:00 +0000Proposes 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. https://www.ml-quant.com/papers/arxiv/2610.11732/https://www.ml-quant.com/papers/arxiv/2610.11732/Fri, 09 Oct 2026 07:00:00 +0000MemTrial uses factorial design to isolate what each memory contributes to portfolio decisions by crediting experiences with their marginal effect rather than shared market moves. https://www.ml-quant.com/papers/arxiv/2610.12115/https://www.ml-quant.com/papers/arxiv/2610.12115/Fri, 09 Oct 2026 07:00:00 +0000A method represents epistemic uncertainty with credal sets and combines them with a decision rule that avoids catastrophic predictions under conditional value-at-risk minimization. https://www.ml-quant.com/papers/arxiv/2610.09246/https://www.ml-quant.com/papers/arxiv/2610.09246/Fri, 09 Oct 2026 07:00:00 +0000Derives 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. https://www.ml-quant.com/papers/arxiv/2610.10046/https://www.ml-quant.com/papers/arxiv/2610.10046/Fri, 09 Oct 2026 07:00:00 +0000A 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. https://www.ml-quant.com/papers/arxiv/2610.04221/https://www.ml-quant.com/papers/arxiv/2610.04221/Fri, 09 Oct 2026 07:00:00 +0000Robust 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. https://www.ml-quant.com/papers/ssrn/7566701/https://www.ml-quant.com/papers/ssrn/7566701/Fri, 09 Oct 2026 07:00:00 +0000Surveys 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. https://www.ml-quant.com/papers/ssrn/7585278/https://www.ml-quant.com/papers/ssrn/7585278/Fri, 09 Oct 2026 07:00:00 +0000Analyzing 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. https://www.ml-quant.com/papers/ssrn/7561158/https://www.ml-quant.com/papers/ssrn/7561158/Fri, 09 Oct 2026 07:00:00 +0000Eleven 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. https://www.ml-quant.com/papers/repec/nbr-nberwo-35528/https://www.ml-quant.com/papers/repec/nbr-nberwo-35528/Fri, 09 Oct 2026 07:00:00 +0000The study shows that mutual fund managers' policy beliefs predict market returns and explain positive alphas, while sentiment beliefs attract flows but lack predictive power. https://www.ml-quant.com/papers/repec/nbr-nberwo-35498/https://www.ml-quant.com/papers/repec/nbr-nberwo-35498/Fri, 09 Oct 2026 07:00:00 +0000Extending Campbell et al., the analysis shows empirically measured deviations from interest parity reverse optimal currency demands for emerging-market investors. https://www.ml-quant.com/papers/arxiv/2610.00147/https://www.ml-quant.com/papers/arxiv/2610.00147/Fri, 02 Oct 2026 07:00:00 +0000The research makes conditioning information a decision variable subject to constraints, solving how look-ahead bias and information availability affect portfolio choice and causal identification. https://www.ml-quant.com/papers/arxiv/2609.38765/https://www.ml-quant.com/papers/arxiv/2609.38765/Fri, 02 Oct 2026 07:00:00 +0000The study develops a Markov decision process framework for dynamic bond portfolio management that balances yield and interest-rate risk subject to transaction costs. https://www.ml-quant.com/papers/arxiv/2609.33767/https://www.ml-quant.com/papers/arxiv/2609.33767/Fri, 02 Oct 2026 07:00:00 +0000A 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. https://www.ml-quant.com/papers/arxiv/2609.37273/https://www.ml-quant.com/papers/arxiv/2609.37273/Fri, 02 Oct 2026 07:00:00 +0000Kernel scenario analysis estimates quantile predictions for portfolio stress-test gains, with online calibration to capture dependence between stressed and unstressed risk factors. https://www.ml-quant.com/papers/ssrn/7524338/https://www.ml-quant.com/papers/ssrn/7524338/Fri, 02 Oct 2026 07:00:00 +0000Computing 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. https://www.ml-quant.com/papers/ssrn/7547959/https://www.ml-quant.com/papers/ssrn/7547959/Fri, 02 Oct 2026 07:00:00 +0000Investors 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. https://www.ml-quant.com/papers/repec/boe-boeewp-023579/https://www.ml-quant.com/papers/repec/boe-boeewp-023579/Fri, 02 Oct 2026 07:00:00 +0000The 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. https://www.ml-quant.com/papers/arxiv/2609.27113/https://www.ml-quant.com/papers/arxiv/2609.27113/Fri, 25 Sep 2026 07:00:00 +0000Formulates 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. https://www.ml-quant.com/papers/arxiv/2609.21427/https://www.ml-quant.com/papers/arxiv/2609.21427/Fri, 25 Sep 2026 07:00:00 +0000Proposes a KKT-based decision-focused learning method that trains mean-variance portfolio models by directly minimizing downstream portfolio loss while preserving all constraints. https://www.ml-quant.com/papers/arxiv/2609.25704/https://www.ml-quant.com/papers/arxiv/2609.25704/Fri, 25 Sep 2026 07:00:00 +0000Shows that mean-variance portfolio selection and the constrained LASSO trace identical piecewise-linear solution paths, mapping their parametrizations exactly. https://www.ml-quant.com/papers/arxiv/2609.26349/https://www.ml-quant.com/papers/arxiv/2609.26349/Fri, 25 Sep 2026 07:00:00 +0000Characterizes optimal consumption and investment strategies in markets with stochastic volatility clocks using infinite-horizon backward SDEs, extending to rough and hyper-rough regimes. https://www.ml-quant.com/papers/ssrn/7508299/https://www.ml-quant.com/papers/ssrn/7508299/Fri, 25 Sep 2026 07:00:00 +0000Using 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. https://www.ml-quant.com/papers/ssrn/7486600/https://www.ml-quant.com/papers/ssrn/7486600/Fri, 25 Sep 2026 07:00:00 +0000The 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. https://www.ml-quant.com/papers/repec/cxv-wpaper-2602/https://www.ml-quant.com/papers/repec/cxv-wpaper-2602/Fri, 25 Sep 2026 07:00:00 +0000Corporate leverage affects how monetary tightening transmits to the real economy: equity holders lose wealth while safe-asset holders are cushioned, raising the sacrifice ratio. https://www.ml-quant.com/papers/arxiv/2509.09585/https://www.ml-quant.com/papers/arxiv/2509.09585/Thu, 16 Apr 2026 07:00:00 +0000Causal 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. https://www.ml-quant.com/papers/arxiv/2604.08252/https://www.ml-quant.com/papers/arxiv/2604.08252/Thu, 16 Apr 2026 07:00:00 +0000Remote work after the pandemic is causing people to move within cities for better quality of life, rather than relocating to rural areas. https://www.ml-quant.com/papers/arxiv/2601.05975/https://www.ml-quant.com/papers/arxiv/2601.05975/Fri, 16 Jan 2026 07:00:00 +0000Deep 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. https://www.ml-quant.com/papers/ssrn/4480682/https://www.ml-quant.com/papers/ssrn/4480682/Sun, 28 Dec 2025 07:00:00 +0000A portfolio approach to drug development may improve investment returns and speed up vaccine creation.