#algorithmic-trading2 articles

2 articles

AIBullisharXiv – CS AI · 4h ago3

🧠

Portfolio Reinforcement Learning with Scenario-Context Rollout

Researchers developed a new portfolio reinforcement learning method called macro-conditioned scenario-context rollout (SCR) that addresses market regime shifts and distribution changes. The approach generates plausible return scenarios under stress events and improves portfolio performance by up to 76% in Sharpe ratio and reduces maximum drawdown by 53%.

AIBullisharXiv – CS AI · 4h ago4

🧠

FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data

Researchers have developed FinBloom 7B, a specialized large language model trained on 14 million financial news articles and SEC filings, designed to handle real-time financial queries. The model introduces a Financial Agent system that can access up-to-date market data and financial information to support decision-making and algorithmic trading applications.