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Ulsan National Institute of Science and Technology
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Achieving superior portfolio performance through a novel smooth top-$k$ operator that aligns forecasting with decision-making in financial optimization.
Financial advisor personas grounded in fund data can deliver tailored investment insights that far exceed generic recommendations, making manager-specific expertise accessible in LLM systems.
Trajectory planning can boost RL-based driving performance by over 11% while enhancing interpretability and reducing errors.
Stop relying on keyword matching: FinTexTS uses semantic similarity and LLM-driven multi-level classification to pair financial text with time-series data, boosting stock price prediction.
