On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

Trend · papers per month

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← all fields·7 papers on “reinforcement learning” in Quant Finance · 1 month

AlphaZeroBeta: Deep Reinforcement Learning for Market-Neutral Portfoliosq-fin.PM

AlphaZeroBeta uses deep reinforcement learning for market-neutral portfolios, outperforming traditional methods.

problem Traditional portfolio management methods often fail during market regime shifts or when assumptions break down.

method Combines a composite reward function and CNN-GRU policy trained end-to-end via Recurrent PPO.

result Achieves higher Sharpe ratios than baselines while maintaining near-zero benchmark correlations.

deep reinforcement learning market-neutral portfolios reinforcement learning portfolio management deep learning

2026-07-20abs ↗pdf ↗

SciPhy Reinforcement Learning for Portfolio Optimizationq-fin.PM

Paper uses SciPhyRL for optimizing large institutional portfolios.

problem Optimizing large institutional portfolios with cumulative costs and practical short horizons.

method Formulates a continuous-time optimization problem, reduces it to solving an HJB equation, and uses PINN for direct solution.

result Learned Gibbs policy yields substantial out-of-sample Sharpe ratio improvements.

portfolio optimization reinforcement learning institutional investing cumulative costs short horizons

2026-07-16abs ↗pdf ↗

A Noise-Robust Elicit-to-Optimize Framework for Distortion Riskmetrics via Inverse Reinforcement Learningcs.LG

Framework uses IRL and RL to elicit and optimize risk preferences robustly to noise.

problem Eliciting and optimizing risk preferences in noisy environments.

method Adaptive Bayesian IRL for elicitation, model-free RL for optimization, using quantile networks.

result Framework achieves convergence rate of

O(\exp(-cm+O(\sqrt{m\log m})))

Inverse Reinforcement Learning Reinforcement Learning Risk Metrics Distortion Risk Elicitation

2026-07-15abs ↗pdf ↗

NeuralChaos: Optimal Adapted Approximation of Square Integrable Predictable Processesmath.PR

NeuralChaos efficiently approximates complex stochastic processes.

problem Representing and computing square-integrable predictable processes over time.

method Introduces NeuralChaos, a neural operator architecture for

\mathbb{R}^{d}

-valued predictable processes.

result NeuralChaos achieves best

N

-term chaoslet approximation rates and is dense in

\mathcal{H}^2_T(\mathbb{R}^{d})

NeuralChaos predictable processes square-integrable stochastic control reinforcement learning

2026-07-15abs ↗pdf ↗

Is Deep Hedging Reinforcement Learning?q-fin.CP

Deep hedging uses RL to minimize risk in financial markets.

problem Minimizing risk in financial markets using reinforcement learning.

method Trains a neural network policy via Monte Carlo simulation and stochastic gradient descent.

result Deep hedging algorithm falls within the RL category.

reinforcement learning deep hedging financial markets risk minimization Monte Carlo simulation

2026-07-15abs ↗pdf ↗

Reinforcement Learning for Execution under Dynamic Fees in a Closed-Loop DEX Simulatorcs.LG

DQN outperforms static policies in a dynamic fee environment for automated market makers.

problem How automated market makers (AMMs) perform under dynamic fees is unknown.

method Constructed a closed-loop simulator with dynamic fees, noise flow, and arbitrage.

result A small DQN policy outperforms static policies in a dynamic fee environment.

Reinforcement Learning Automated Market Makers Dynamic Fees Closed-Loop Simulator Order Flow

2026-07-12abs ↗pdf ↗

Can Reinforcement Learning Efficiently Discover Price Manipulation?q-fin.TR

RL agent outperforms model-based approach in detecting price manipulation.

problem Detecting and exploiting price manipulation opportunities.

method Compared model-free RL with model-based approach in a market with Almgren-Chriss framework.

result RL consistently outperforms model-based approach, especially with noisy parameter estimates.

Reinforcement Learning Price Manipulation Financial Markets RL vs Model-Based Sequential Least Squares Quadratic Programming

2026-07-07abs ↗pdf ↗