Paper: arXiv 2602.17890

Authors: Krishna Neupane

Abstract

This study identifies and quantifies a significant informational friction embedded in the SEC Form 144 disclosure regime, characterized as predictive decoupling. Drawing on a theoretical foundation of welfare economics, the article argues that the current reporting inversion – where trade execution (Form 4) frequently precedes the public notice of intent (Form 144) – violates the conditions for Pareto efficiency by inducing non-symmetric pricing. Utilizing an event-study framework of intent-to-sell windows, the analysis examines cases where insiders file a notice of proposed sale but fail to execute within the statutory 90-day period. The machine learning audit reveals a persistent 52.4 percent opacity rate, where aborted signals remain statistically indistinguishable from routine executions, creating a structural information ceiling that prevents the market from exhausting the signal’s informational content. Contrary to the traditional small-firm effect, the study documents a large-cap significance paradox: while small-cap portfolios yield higher absolute abnormal returns (32.21 bps), statistically significant alpha is concentrated in large-cap firms (14.49 bps, $p = 0.021$). The results suggest that Institutional Salience enables more reliable processing of this negative non-event when reputational costs are maximized. Cross-sectional tests confirm that prior idiosyncratic volatility serves as a signal amplifier, with causal estimators identifying an illiquidity jump of up to 2.63 times. To mitigate this market failure, the study proposes a mandatory execution confirmation (Form 144-A) to transition the regime toward bilateral accountability, converting a predictive blind spot into a verifiable data stream and restoring the informational symmetry requisite for efficient capital allocation.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; no out-of-sample test; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents an empirically rigorous examination of SEC Form 144 non-executions using causal machine learning (Double ML, Causal Forests) and multiple predictive architectures. It introduces novel insights into insider intent, market opacity, and the ’large-cap significance paradox’ with solid theoretical grounding in welfare economics.

Research Flowchart

  flowchart TD
    A[Research Goal: Evaluate Information Friction & Pricing Effects of SEC Form 144/Form 4 Reporting Inversions] --> B[Data Inputs: SEC Form 4 & Form 144 Filings for Intent-to-Sell Windows]
    B --> C[Methodology: Event Study Framework & Machine Learning Audit]
    C --> D[Computational Analysis: Causal Estimators & Idiosyncratic Volatility Cross-Sectional Tests]
    D --> E[Key Finding 1: Persistent 52.4% Opacity Rate in Aborted Sales Signals]
    D --> F[Key Finding 2: Large-Cap Significance Paradox - Significant Alpha of 14.49 bps, p=0.021]
    D --> G[Key Finding 3: Signal Amplification Leading to Illiquidity Jump up to 2.63x]
    E & F & G --> H[Policy Outcome: Proposed Mandatory Execution Confirmation Form 144-A]