---
project: anomaly-atlas
document: Phase 1 notes — short-horizon mean-reversion & lead-lag
author: Simon-Pierre Boucher
contact: contact@spboucher.ai
data_source: hfmarketdata.io
created: 2026-08-12
status: reviewed
---

# Short-horizon mean-reversion & lead-lag (Q1, Q2)

*Phase 1 literature notes. All citations verified via OpenAlex, accessed
2026-08-12. Epistemic status flags: **robust** / **decayed** / **disputed** /
**likely-artifact**.*

## 1. The founding results

- Lo, A. W. & MacKinlay, A. C. (1988). Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test. *Review of Financial Studies* 1(1), 41–66. https://doi.org/10.1093/rfs/1.1.41 — The variance-ratio test rejects the random walk for weekly index returns 1962–1985 (VR > 1: *momentum* at the index level), explicitly not fully explained by infrequent trading. **Status: robust as a historical fact; the effect itself decayed.**
- Lehmann, B. N. (1990). Fads, Martingales, and Market Efficiency. *Quarterly Journal of Economics* 105(1), 1–28. https://doi.org/10.2307/2937816 — Weekly winner/loser reversals in individual stocks survive his spread corrections. **Status: disputed** — later work attributes much of it to bid-ask bounce and liquidity provision compensation.
- Jegadeesh, N. (1990). Evidence of Predictable Behavior of Security Returns. *Journal of Finance* 45(3), 881–898. https://doi.org/10.1111/j.1540-6261.1990.tb05110.x — Strong negative first-order *monthly* serial correlation in individual stocks. **Status: decayed** post-publication (see McLean–Pontiff below).

Key structural insight for Q1: at the *index/portfolio* level early evidence
showed VR > 1 (positive autocorrelation), while *individual* stocks showed
reversal. The difference is exactly the cross-autocorrelation structure —
which is where the artifacts live.

## 2. Lead-lag: the anomaly and its artifact twin

- Lo, A. W. & MacKinlay, A. C. (1990). When Are Contrarian Profits Due to Stock Market Overreaction? *Review of Financial Studies* 3(2), 175–205. https://doi.org/10.1093/rfs/3.2.175 — Contrarian profits come largely from **lead-lag cross-autocorrelations (large leads small)**, not overreaction. The canonical Q2 result.
- Scholes, M. & Williams, J. (1977). Estimating betas from nonsynchronous data. *Journal of Financial Economics* 5(3), 309–327. https://doi.org/10.1016/0304-405X(77)90041-1 — Non-synchronous observation biases correlations/betas and manufactures spurious lead-lag. **The artifact null for all of Q2** — our expB measured exactly this on hfmarketdata.io (SPY "leads" stale tickers +0.047 at 1min; SPX lags SPY +0.065).
- Epps, T. W. (1979). Comovements in Stock Prices in the Very Short Run. *Journal of the American Statistical Association* 74(366a), 291–298. https://doi.org/10.1080/01621459.1979.10482508 — Cross-correlations shrink as sampling gets finer (the **Epps effect**). At our 1min floor, contemporaneous correlations are mechanically attenuated and the "missing" correlation shows up at leads/lags. Any 1min lead-lag claim must model this.
- Chordia, T., Roll, R. & Subrahmanyam, A. (2005). Evidence on the speed of convergence to market efficiency. *Journal of Financial Economics* 76(2), 271–292. https://doi.org/10.1016/j.jfineco.2004.06.004 — Predictability from order flow is arbitraged away within **5–60 minutes** (already by 2005). Sets the prior: minute-scale inefficiencies in liquid names should be tiny-to-absent in our 2020s data.

## 3. What decayed

- McLean, R. D. & Pontiff, J. (2016). Does Academic Research Destroy Stock Return Predictability? *Journal of Finance* 71(1), 5–32. https://doi.org/10.1111/jofi.12365 — Across 97 published predictors: −26 % out-of-sample, −58 % post-publication. **The base rate for our whole project.**
- Chordia, T., Subrahmanyam, A. & Tong, Q. (2014). Have capital market anomalies attenuated in the recent era of high liquidity and trading activity? *Journal of Accounting and Economics* 58(1), 41–58. https://doi.org/10.1016/j.jacceco.2014.06.001 — Anomaly profits attenuate sharply post-decimalization as liquidity/arbitrage grows. Directly relevant: our data (2000→2026) spans this attenuation; sub-period stability checks (expH) are mandatory.
- Jacobs, H. & Müller, S. (2020). Anomalies across the globe: Once public, no longer existent? *Journal of Financial Economics* 135(1), 213–230. https://doi.org/10.1016/j.jfineco.2019.06.004 — Decay is largely a U.S. phenomenon. Our data is U.S.-centric: expect the *fastest* decay regime.

## 4. Implications for our experiments (expC, expD)

1. Reversion at 1min in liquid names should be ≈ 0 (bounce-free mega-caps
   showed AC1 CI covering 0 in expB) — a *negative finding* here is the
   expected, publishable outcome.
2. Any reversion in mid/low liquidity must beat the measured bounce null
   (expB: AC1 −0.05 to −0.23 with zero economics).
3. Any lead-lag must beat the staleness-predicted cross-correlation AND
   survive on both-fresh subsamples (Scholes–Williams; Epps).
4. Test sub-periods: pre/post 2010 and pre/post publication of the classic
   papers; expect attenuation à la Chordia et al.
5. Horizon matters: 1min → daily aggregation sweeps let us find where (if
   anywhere) reversion exceeds its artifact floor.