FinPILOT is a plugin inference‑time policy optimization framework that enhances pretrained reinforcement‑learning trading agents by leveraging a multi‑step price forecaster to generate imagined market trajectories. At each decision point, it optimizes the pretrained policy using the forecaster’s predicted price path before executing a trade, without altering the original training pipeline. Across five pretrained policies and multiple market settings, FinPILOT consistently improves total return and Sharpe ratio, with gains increasing as forecast accuracy improves.
By Eun Go, Rohan Deb, Arindam Banerjee
arXiv:2606. 30997v1 Announce Type: new Abstract: We present a three-phase deep reinforcement learning system for personalized portfolio management that addresses three limitations shared by all prior financial RL work: 1) ticker lock-in, 2) monolithic objectives , and 3) static user models.
By Ramin Pishehvar
arXiv:2608. 09880v1 Announce Type: cross Abstract: Financial prediction typically relies on task-specific regression, ranking, or policy heads, separating the language model from the numerical object ultimately evaluated.
By Xu Ouyang, Moontae Lee
arXiv:2608. 15841v1 Announce Type: new Abstract: Reinforcement learning has gained increasing attention as a data-driven approach for stock trading.
By Arishi Orra, Himanshu Choudhary, Manoj Thakur
The paper introduces Future‑Aligned Soft Contrastive Learning (FASCL), a representation learning framework that uses pairwise future return correlations as continuous supervision to improve asset retrieval. FASCL’s soft contrastive loss aligns retrieved assets with correlated future returns, and the authors propose a new evaluation protocol to directly assess future trajectory similarity. Experiments on 5,631 US‑listed securities outperform 14 baselines in future return correlation, rank information coefficient, trend consistency, and gross Sharpe ratio across various retrieval depths and basket sizes.
By Hyeongmin Lee, Chanyeol Choi, Jihoon Kwon, Yoon Kim, Alejandro Lopez-Lira, Wonbin Ahn, Justin Xu, Srijan Sood, Qingsong Wen, Chun-Li Yang, Yongjae Lee
DIVINE is a cross‑market pretraining framework for financial time‑series that reconstructs 77 targets derived from 16 standard technical indicators using raw OHLCV data. Trained jointly on six equity markets, its lightweight 0.05M‑parameter encoder outperforms larger pretraining baselines and matches or exceeds larger financial foundation models in downstream stock ranking tasks. The study demonstrates that diverse indicator supervision and cross‑market training yield complementary gains, highlighting that supervision design and market diversity, rather than model size, drive strong transferable financial representations.
By Kuan-Yu Chen, Shu-Cheng Zheng, Yu-Chen Den, Wei-Cheng Liao, Tien-Hao Chang