Adding a Bayesian source memory for market-feedback adaptive retrieval to a frozen LLM improves macro-F1 from 0.438 to 0.471 and portfolio Sharpe from 0.52 to 0.84 in point-in-time financial event-impact prediction.
FNSPID: A comprehensive financial news dataset in time series.arXiv preprint arXiv:2402.06698
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A survey proposing a taxonomy of Injective, Bridging, and Internal Alignment paradigms to evolve TSA into user-driven Time Series Question Answering with LLMs.
SSAI maps news into four factors (sentiment, risk, confidence, volatility) for trading, but factor portfolios, ridge models, and RL agents show no reliable edge over baselines after coverage controls and costs.
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Point-in-Time Financial RAG with Frozen LLMs and Market-Feedback Adaptive Retrieval
Adding a Bayesian source memory for market-feedback adaptive retrieval to a frozen LLM improves macro-F1 from 0.438 to 0.471 and portfolio Sharpe from 0.52 to 0.84 in point-in-time financial event-impact prediction.
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From Time Series Analysis to Question Answering: A Survey in the LLM Era
A survey proposing a taxonomy of Injective, Bridging, and Internal Alignment paradigms to evolve TSA into user-driven Time Series Question Answering with LLMs.
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Semantic State Abstraction Interfaces for LLM-Augmented Portfolio Decisions: Multi-Axis News Decomposition and RL Diagnostics
SSAI maps news into four factors (sentiment, risk, confidence, volatility) for trading, but factor portfolios, ridge models, and RL agents show no reliable edge over baselines after coverage controls and costs.