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LLMs fail to identify shadow trading targets in SEC enforcement theory

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the potential of large language models (LLMs) to identify "shadow trading" targets, a novel theory of insider trading liability involving trading in a peer firm's securities based on material nonpublic information about an economically linked company. The study applied a two-stage LLM pipeline to SEC 10-K filings to assess semantic similarity between firms and correlate it with stock returns. While the pipeline successfully identified Incyte as a close peer in the SEC v. Panuwat case, broader analysis across 30 M&A events found no significant association between semantic similarity and abnormal stock returns, questioning the empirical basis of shadow trading enforcement. AI

IMPACT This research suggests current NLP models may not be sufficient for identifying complex financial relationships relevant to insider trading enforcement.

RANK_REASON Academic paper on applying NLP/LLMs to a legal/regulatory theory. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs fail to identify shadow trading targets in SEC enforcement theory

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Academic paper on applying NLP/LLMs to a legal/regulatory theory. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sarah Wilson, Michael MacKay, Anthony Marello, Trinav Bhattacharyya ·

    Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory

    arXiv:2608.01322v1 Announce Type: new Abstract: Shadow trading -- trading in a peer firm's securities on the basis of material nonpublic information (MNPI) about an "economically linked" company -- is a novel and contested theory of insider trading liability, first prosecuted in …

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