Paper: arXiv 2601.15304

Authors: Sandeep Neela

Abstract

Market integrity monitoring is difficult because suspicious price/volume behavior can arise from many benign mechanisms, while modern detection systems often rely on opaque models that are hard to audit and communicate. We present AIMM-X, an explainable monitoring pipeline that combines market microstructure-style signals derived from OHLCV time series with multi-source public attention signals (e.g., news and online discussion proxies) to surface time windows that merit analyst review. The system detects candidate anomalous windows using transparent thresholding and aggregation, then assigns an interpretable integrity score decomposed into a small set of additive components, allowing practitioners to trace why a window was flagged and which factors drove the score. We provide an end-to-end, reproducible implementation that downloads data, constructs attention features, builds unified panels, detects windows, computes component signals, and generates summary figures/tables. Our goal is not to label manipulation, but to provide a practical, auditable screening tool that supports downstream investigation by compliance teams, exchanges, or researchers.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper relies primarily on basic statistical scoring and thresholding rather than complex mathematical derivations. However, it presents an empirically grounded, reproducible framework using public data and attention proxies for explainable market integrity monitoring.

Research Flowchart

  flowchart TD
    A["Research Goal: Create an explainable,<br/>auditable market integrity screening tool"] --> B["Data Inputs: OHLCV Time Series &<br/>Multi-Source Public Attention (News/Social)"]
    B --> C["Feature Engineering: Compute microstructure<br/>& public attention signals"]
    C --> D["Candidate Window Detection:<br/>Transparent thresholding & aggregation"]
    D --> E["Scoring & Decomposition: Assign AIMM-X<br/>integrity score split into additive components"]
    E --> F["Outcomes: Flagged windows with<br/>interpretable, audit-ready drivers for analysts"]

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0 of 78 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.