---
project: anomaly-atlas
document: Phase 1 notes — transaction-cost realism
author: Simon-Pierre Boucher
contact: contact@spboucher.ai
data_source: hfmarketdata.io
created: 2026-08-12
status: reviewed
---

# Transaction-cost realism (§4.5 — Q5, the cost frontier)

*Phase 1 literature notes. Citations verified via OpenAlex, accessed
2026-08-12. Constraint: our data has NO quotes — every spread must be
estimated from OHLCV bars. That makes the low-frequency-estimator literature
load-bearing.*

## 1. Spread estimation from bar data (our only instruments)

- Roll (1984) — see microstructure notes; autocovariance-based, undefined when autocov ≥ 0 (expB: mega-caps).
- Hasbrouck, J. (2009). Trading Costs and Returns for U.S. Equities: Estimating Effective Costs from Daily Data. *Journal of Finance* 64(3), 1445–1477. https://doi.org/10.1111/j.1540-6261.2009.01469.x — Gibbs-sampled Roll model; corr 0.965 with TAQ benchmarks.
- Corwin, S. A. & Schultz, P. (2012). A Simple Way to Estimate Bid-Ask Spreads from Daily High and Low Prices. *Journal of Finance* 67(2), 719–760. https://doi.org/10.1111/j.1540-6261.2012.01729.x — high-low estimator; we have highs/lows at every timeframe.
- Abdi, F. & Ranaldo, A. (2017). A Simple Estimation of Bid-Ask Spreads from Daily Close, High, and Low Prices. *Review of Financial Studies* 30(12), 4437–4480. https://doi.org/10.1093/rfs/hhx084 — CHL estimator; best for illiquid names.
- Ardia, D., Guidotti, E. & Kroencke, T. A. (2024). Efficient estimation of bid–ask spreads from open, high, low, and close prices. *Journal of Financial Economics* 161, 103916. https://doi.org/10.1016/j.jfineco.2024.103916 — state-of-the-art OHLC estimator (EDGE), asymptotically unbiased, minimal variance. **Primary estimator for expG**; Roll/CS/CHL as cross-checks.
- Fong, K. Y. L., Holden, C. W. & Trzcinka, C. A. (2017). What Are the Best Liquidity Proxies for Global Research? *Review of Finance* 21(4), 1355–1401. https://doi.org/10.1093/rof/rfx003 — horse race of low-frequency proxies vs intraday benchmarks; validates picking 2–3 complementary proxies.
- Lesmond, D. A., Ogden, J. P. & Trzcinka, C. A. (1999). A New Estimate of Transaction Costs. *Review of Financial Studies* 12(5), 1113–1141. https://doi.org/10.1093/rfs/12.5.1113 — LOT: costs from the incidence of zero returns (1.2 %–10.3 % across deciles). Our no-trade minutes are the intraday analogue.
- Bessembinder, H. (2003). Issues in Assessing Trade Execution Costs. *Journal of Financial Markets* 6(3), 233–257. https://doi.org/10.1016/s1386-4181(02)00064-2 — measurement pitfalls (trade signing, timing conventions) — reminder that even "measured" costs carry convention risk.

## 2. Do anomalies survive costs?

- Novy-Marx, R. & Velikov, M. (2016). A Taxonomy of Anomalies and Their Trading Costs. *Review of Financial Studies* 29(1), 104–147. https://doi.org/10.1093/rfs/hhv063 — low-turnover anomalies mostly survive (with mitigation); **high-turnover ones mostly do not**. Short-horizon effects (ours) are the highest-turnover class → strong prior that Q1/Q2 effects die at the cost frontier.
- Frazzini, A., Israel, R. & Moskowitz, T. J. (2012). Trading Costs of Asset Pricing Anomalies. SSRN. https://doi.org/10.2139/ssrn.2294498 — real institutional executions: realized costs are far below TAQ-implied for patient flow. Gives the *lower* bound of the cost sweep.
- Chen, A. Y. & Velikov, M. (2022). Zeroing In on the Expected Returns of Anomalies. *Journal of Financial and Quantitative Analysis* 58(3), 968–1004. https://doi.org/10.1017/s0022109022000874 — net of spreads + post-publication decay + data mining, the average anomaly earns **~4 bp/month**. The sobering calibration for our whole atlas.
- Detzel, A., Novy-Marx, R. & Velikov, M. (2023). Model Comparison with Transaction Costs. *Journal of Finance* 78(3), 1743–1775. https://doi.org/10.1111/jofi.13225 — ignoring costs biases even *model comparisons*; cost-awareness is not optional at any stage.

## 3. Design of expG (cost frontier)

1. **Spread panel**: EDGE (Ardia et al.) per ticker-month from daily OHLC +
   Corwin–Schultz and CHL cross-checks + Roll where defined; validate the
   three against each other (Fong et al. protocol).
2. **Options as auxiliary evidence**: our options chains carry real bid/ask —
   the only quoted spreads in the dataset; usable as a sanity anchor for the
   underlying's cost regime (with care).
3. **Sweep, don't point-estimate**: report each surviving effect's net value
   across cost multipliers 0.25×–2× the estimated half-spread (Frazzini
   lower bound ↔ retail-taker upper bound), and the crossing point where net
   effect = 0.
4. **Capacity is out of scope** (no volume-at-quote data) — state it as a
   limitation rather than pretend.