mlsanityData to 14 Oct 2026

When Missing Becomes Structure: Intent-Preserving Policy Completion from Financial KOL Discourse

arXiv 2604.14333 ↗First posted 15 Apr 2026cs.LGcited by 0

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Abstract (arXiv)

Key Opinion Leader (KOL) discourse on social media is widely consumed as investment guidance, yet turning it into executable trading strategies without injecting assumptions about unspecified execution decisions remains an open problem. We observe that the gaps in KOL statements are not random deficiencies but a structured separation: KOLs express directional intent (what to buy or sell and why) while leaving execution decisions (when, how much, how long) systematically unspecified. Building on this observation, we propose an intent-preserving policy completion framework that treats KOL discourse as a partial trading policy and uses offline reinforcement learning to complete the missing execution decisions around the KOL-expressed intent. Experiments on multimodal KOL discourse from YouTube and X (2022-2025) show that KICL achieves the best return and Sharpe ratio on both platforms while maintaining zero unsupported entries and zero directional reversals, and ablations confirm that the full framework yields an 18.9% return improvement over the KOL-aligned baseline.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 34 references that are in the map. First posted on arXiv 15 Apr 2026.

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8 of 34 references.

References · 8

  1. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,809
  2. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,293
  3. 2021A Minimalist Approach to Offline Reinforcement Learningcited by 899
  4. 2020AWAC: Accelerating Online Reinforcement Learning with Offline Datasetscited by 671
  5. 2016Cooperative Inverse Reinforcement Learningcited by 300
  6. 2016Sentiment Analysis of Twitter Data for Predicting Stock Market Movementscited by 34
  7. 2025Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategycited by 15
  8. 2018Trading the Twitter Sentiment with Reinforcement Learningcited by 3

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  1. 2026Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents
  2. 2026Learning to Trade Like an Expert: Cognitive Fine-Tuning for Stable Financial Reasoning in Language Models
  3. 2026From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Markets
  4. 2026Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading
  5. 2026RetailAgent: Structured Adverse Timing in Self-Conditioned Multimodal LLM Trading Agents
  6. 2026When Valid Signals Fail: Regime Boundaries Between LLM Features and RL Trading Policies
  7. 2026Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs
  8. 2026LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs