# AI Disclosure & S&P 500 Returns — Empirical Study

Links the semantic AI-disclosure factors (EDGAR Research Database) to realized stock
performance (Yahoo Finance) and tests whether AI language in 10-K filings explains
the cross-section of subsequent returns. Full 40-page paper in `report/`.

## Pipeline (run in order)
| Script | Purpose | Key output |
|--------|---------|-----------|
| `01_download_prices.py` | Monthly prices (500 tickers), SPY, market caps | `data/prices_monthly.parquet` |
| `02_build_panel.py` | Look-ahead-free forward returns (July formation) + controls; merge | `data/merged_panel.parquet` |
| `03_analysis.py` | Sorts, FF6 alphas, two-way FE panel, Fama-MacBeth, post-ChatGPT break | `tables/results.json` |
| `06_extended.py` | Placebo, sector-neutral/double sorts, Mag-7 exclusion, alt signals/horizons, PCA, DiD, fwd-vol | `tables/results2.json` |
| `07_event_study.py` | ChatGPT (Nov 2022) market-model event study, daily CARs | `tables/results_event.json` |
| `04_figures.py`, `08_figures_ext.py` | 13 publication figures | `figures/fig1..13_*.png` |
| `05_make_tables.py`, `09_make_tables_ext.py` | 12 LaTeX table fragments | `report/tab_*.tex` |

FF5 + momentum factors fetched into `data/ff5_monthly.parquet`, `data/mom_monthly.parquet`.

## Report
**`report/ai_disclosure_returns_full.tex` → `ai_disclosure_returns_full.pdf` (40 pp).**
Compile: `cd report && tectonic ai_disclosure_returns_full.tex`.
(`ai_disclosure_returns.tex` is the earlier short 6-page version.)

## Headline finding
AI-disclosure intensity barely predicted returns for a decade, then was sharply
rewarded after ChatGPT (Nov 2022). High−low (Q5−Q1) forward-return spread:
**+2.4 pp/yr pre-2022 → +14.6 pp/yr post-2022**; long-short Sharpe 0.15 → 1.33.
Identified by: (1) a **placebo** — the jump is AI-specific (ESG/climate go *negative*);
(2) an **event study** — high-AI firms earned +10.1% higher abnormal return in the
year after ChatGPT (t=1.97); (3) it survives **Mag-7 exclusion** (+14.1pp). It is a
**cross-sectional, regime-dependent re-rating** (absorbed by firm FE, halved by sector
FE), not within-firm news timing.

## Design notes
- **Signals**: section-weighted cosine similarity to AI anchors (`*_wmean`) for
  adoption/infrastructure/disruption-risk; standardized cross-sectionally each year.
- **Timing**: 10-K(FY=Y) → form June Y+1 → hold July Y+1..June Y+2 (no look-ahead).
- **Inference**: Newey-West (sorts, FM, monthly LS), double-clustered SE (panel/DiD),
  Welch t (event study).
- **Caveats**: 3 post-period cohorts (concentrated in 2024); current-membership
  survivorship; shares-held-constant size proxy; AI signals collinear (ρ=0.75–0.86);
  effect operates substantially through a sector (tech) channel.