arXiv:2608. 04305v1 Announce Type: new Abstract: Risk-aware Q-learning (RaQL) provides a model-free, two-timescale estimator for dynamic risk objectives, but its finite-budget behavior remains fragile: fixed inner-loop hyperparameters can produce unstable value estimates, persistent Bellman residuals, and inefficient sample reuse.
By Yifan Wu, Junjie Lei, Wenjie Huang
arXiv:2606. 04574v1 Announce Type: new Abstract: This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurrency markets.
By Damian Lebied\'z, Robert \'Slepaczuk
arXiv:2606. 03704v1 Announce Type: new Abstract: Financial decision-making tasks such as stock recommendation and portfolio allocation typically estimate future return and risk and then select trades or allocations for an investor, and the chosen optimization objective often determines realized performance.
By Keigo Sakurai, Takahiro Ogawa, Miki Haseyama, Anjyu Anan, Kei Nakagawa
PPO‑HRAP introduces a hybrid regime‑aware policy that blends Proximal Policy Optimization with a volatility‑conditioned regime prior to balance upside participation and drawdown control in trading. The agent uses market and portfolio features, rewards that combine log return, VIX‑conditioned drawdown penalty, exposure deviation, and turnover cost, and outputs a blended action between the PPO actor and the regime‑derived target exposure. In backtests on SPY (2020‑2022) it achieved a 27.62% total return, 8.48% annualized return, and reduced maximum drawdown from 34.10% to 18.47%, while maintaining stable performance across multiple seeds and ranking first on total return and Sharpe ratio in single‑run cross‑asset tests on QQQ and DIA.
By Duong Hien Chi Kien, Thanh Trung Huynh
arXiv:2608. 12251v1 Announce Type: cross Abstract: Financial volatility is regime dependent, yet incorporating regime information into neural networks can also destabilize training.
By Junyi Ye, Gargi Vijay Borde
arXiv:2607. 23370v1 Announce Type: new Abstract: Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance.
By Muhammad Abdullah Haroon