#quantitative-finance2 articles

2 articles

AIBullisharXiv – CS AI · 4h ago3

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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 ago3

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TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure

Researchers introduced TradeFM, a 524M-parameter generative AI model that learns from billions of trade events across 9,000+ equities to understand market microstructure. The model can generate synthetic market data and generalizes across different markets without asset-specific calibration, potentially enabling new applications in trading and market simulation.

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