The results demonstrate that the proposed framework successfully translates known signal quality into a robust, multi-period, and cost-aware allocation mechanism with strictly controlled volatility and turnover.
Abstract
This paper introduces a dynamic portfolio optimization framework for large institutional investors using Scientific Physics-Informed Reinforcement Learning (SciPhyRL). Formulated in continuous time over an extended state space that includes explicit cumulative costs, the approach leverages offline historical data to learn optimal, distribution-aware strategies. A core innovation reduces the optimization challenge to solving an HJB equation by projecting it onto observed trajectories as a pathwise Hamilton-Jacobi equation. This is solved directly from data using PINN in a single offline sweep, eliminating the need for traditional value or policy iteration. To make the method effective at practical short horizons, the control variable is recast from a continuous trading rate to a discrete target holding. This ensures signal-implied positions are reached immediately, while execution costs are evaluated against a microstructure-grounded quadratic price impact model. Evaluated on a $14$-asset ETF universe using an engineered oracle signal, the learned Gibbs policy yields substantial out-of-sample Sharpe ratio improvements over static and myopic baselines. The results demonstrate that the proposed framework successfully translates known signal quality into a robust, multi-period, and cost-aware allocation mechanism with strictly controlled volatility and turnover.
A new Quantum-Inspired Portfolio Optimization (QIPO-RL) model is presented that combines quantum-inspired search techniques, an adaptive RL agent, and asset weights to create a framework for a Reinforcement Learning (RL) based dynamic stock allocation algorithm.
Kishore Kumar Sambangi· International Journal of Com...· 0 citations
This work exploits the auxiliary-threshold representation of CVaR to establish the existence of an optimal strategy and strong duality without requiring market completeness, and proves that the resulting strategies converge to the optimal control as the number of iterations tends to infinity.
Anran Hu, Silvana M. Pesenti, Xiaofei Shi· 0 citations
Modern portfolio management increasingly demands a balance between traditional risk-adjusted returns and strict Environmental, Social, and Governance (ESG) mandates. Current Reinforcement Learning (RL) approaches typically optimize for a single ESG provider, neglecting the significant divergence in rating methodologies across the industry and the unintuitive nature of manually weighting conflicting objectives. This paper addresses these limitations by formulating ESG-aware portfolio optimization as a Multi-Objective Reinforcement Learning (MORL) problem that simultaneously incorporates ratings from three distinct ESG agencies. To bridge the gap between high-dimensional algorithmic trade-offs and human decision-making, we integrate a Preference Elicitation framework using Gaussian Processes. This system enables practitioners to infer their latent utility functions through intuitive pairwise comparisons of candidate portfolios based on their Sharpe ratios and aggregate ESG scores. We systematically evaluate our framework by employing Large Language Model (LLM) personas to simulate Portfolio Managers operating under varied regional contexts. Empirical results using historical market data reveal that regional backgrounds fundamentally shift the derived preference weights. For instance, European-based personas tend to prioritize ESG alignment over financial returns, while Texas-based personas favor risk-adjusted performance. This work offers a highly adaptable framework that successfully aligns multi-objective algorithmic trading with diverse, real-world human sustainability preferences.
Giovanni Dispoto, Marcello Restelli, Carmine Ventre· 0 citations
When economic structures and market dynamics shift, classic portfolio rebalancing algorithms often suffer from unstable and degraded performance. To improve the return and robustness of portfolio management, we explore reinforcement learning (RL) and propose Scenario-Context Rollout (SCR), a macroeconomics-guided feedback mechanism to produce a distribution of next-day joint returns under potential economic shocks. However, doing so faces new challenges, as history will never tell what would have happened differently. As a result, incorporating scenario-based rewards from rollouts introduces a reward-transition mismatch in temporal-difference (TD) learning, destabilizing RL critic training. We theoretically analyze this problem and show that combining scenario-scored rewards with tape-realized transitions induces a hybrid fixed point. Guided by this analysis, we construct a counterfactual next state using the SCR continuations and augment the critic agent's bootstrap target. Doing so stabilizes the learning and provides a viable bias-variance tradeoff. In out-of-sample evaluations across 31 distinct universes of U.S. equity and ETF portfolios, our method improves Sharpe ratio by up to 76% and reduces maximum drawdown by up to 53% compared with classic and RL-based portfolio rebalancing baselines.
Vanya Priscillia Bendatu, Yao Lu· Proceedings of the 32nd ACM...· 0 citations
Portfolio reinforcement learning (RL) commonly represents each action as a complete asset-weight vector, causing the action dimension and exploration difficulty to grow with the investment universe. This study proposes FrontierStep-RL, which replaces the direct N-dimensional action with two bounded variables: a frontier coordinate and a rebalancing step. At each decision date, rolling estimates of expected returns and covariance define a regularized efficient frontier. A cost–risk-aware coordinate organizes the frontier using normalized local changes in predicted volatility and one-way turnover. The coordinate selects a frontier-supported target portfolio, while the step controls how far the pre-trade portfolio moves toward that target. We evaluate FrontierStep-RL on FF49, FF100, and FNSPID-50 against traditional strategies, controlled direct-weight RL policies, and recent portfolio-management methods. FrontierStep-RL achieves net Sharpe ratios of 0.75 on both FF49 and FNSPID-50 while maintaining comparatively low volatility, drawdown, and turnover. In the 100-asset setting, it achieves 0.68, compared with 0.52 for the strongest direct-weight baseline, and completes all runs. At a transaction cost of 50 basis points, it retains net Sharpe ratios of 0.559 and 0.568. The results support fixed-dimensional target selection and controlled execution for scalable, transaction-cost-aware portfolio RL.
Deep learning has entered algorithmic trading largely as data-driven pattern fitting, which markets punish when regimes change. Physics-informed neural networks offer a different discipline: they embed governing equations directly in the training loss, so the model respects the dynamics even where data are scarce. This paper applies that discipline to portfolio optimisation. The reference problem is Merton’s continuous-time allocation between a risky index and a risk-free asset, whose optimal policy solves a Hamilton-Jacobi-Bellman equation with, for constant relative risk aversion, a closed-form solution. We train a from-scratch physics-informed network to solve the reduced equation with no market data at all, only the equation, and validate that it recovers both the analytic value function, to a relative error below two times ten to the minus four, and the closed-form Merton allocation exactly. Two guarantees support the method: the closed-form policy itself, and an a-posteriori bound showing that the value-function error is controlled by the physics residual. We then backtest the allocation on a seeded simulator calibrated to plausible Indian-market parameters, net of Indian transaction costs, against buy- and-hold, a fixed mix, an unrealisable oracle, and a tradable estimation rule. The lesson is deliberately sober: the physics supplies the correct allocation rule, a constant policy on a stable long-run premium is competitive with a sensible fixed mix and far gentler on drawdown than buy-and-hold, and the value that remains is almost entirely in estimating the inputs, where an oracle would reach a Sharpe ratio of 1.10 but a naive adaptive estimator falls to 0.34 after estimation error and turnover costs. Embedding the governing equations makes the model data-efficient and explainable; it does not repeal the estimation problem, and honest system design should place its effort there. The backtest uses a market simulation, not real exchange data.
S. Satyanarayana· International Journal of Com...· 0 citations
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