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

TL;DR

This paper introduces a framework that converts social media KOL investment advice into executable trading policies by completing missing execution details using offline reinforcement learning, improving trading performance.

Contribution

It presents a novel intent-preserving policy completion method that transforms partial KOL discourse into full trading strategies without assumptions about execution timing or size.

Findings

KICL achieves the best return and Sharpe ratio on YouTube and X data.

The framework maintains zero unsupported entries and reversals.

Ablation shows an 18.9% return improvement over baseline.

Abstract

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.