# project: anomaly-atlas document: Bibliography author: Simon-Pierre Boucher contact: contact@spboucher.ai data_source: hfmarketdata.io created: 2026-08-12 modified: 2026-08-12 status: reviewed

# Bibliography

Every consulted source, with URL and access date. All entries verified against OpenAlex on 2026-08-12 (metadata: title, authors, year, venue, DOI). Where online/print years differ, the print-issue year is used with the online year noted in the theme notes. Theme notes in research/notes/.

# §4.1 — Short-horizon anomalies & lead-lag

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 (accessed 2026-08-12)
Lehmann, B. N. (1990). Fads, Martingales, and Market Efficiency. Quarterly Journal of Economics 105(1), 1–28. https://doi.org/10.2307/2937816 (accessed 2026-08-12)
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 (accessed 2026-08-12)
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 (accessed 2026-08-12)
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 (accessed 2026-08-12)
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 (accessed 2026-08-12)
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 (accessed 2026-08-12)

# §4.1 — Calendar & intraday effects

French, K. R. (1980). Stock returns and the weekend effect. Journal of Financial Economics 8(1), 55–69. https://doi.org/10.1016/0304-405X(80)90021-5 (accessed 2026-08-12)
Ariel, R. A. (1987). A monthly effect in stock returns. Journal of Financial Economics 18(1), 161–174. https://doi.org/10.1016/0304-405X(87)90066-3 (accessed 2026-08-12)
Lakonishok, J. & Smidt, S. (1988). Are Seasonal Anomalies Real? A Ninety-Year Perspective. Review of Financial Studies 1(4), 403–425. https://doi.org/10.1093/rfs/1.4.403 (accessed 2026-08-12)
Wood, R. A., McInish, T. H. & Ord, J. K. (1985). An Investigation of Transactions Data for NYSE Stocks. Journal of Finance 40(3), 723–739. https://doi.org/10.2307/2327796 (accessed 2026-08-12)
Gao, L., Han, Y., Li, S. Z. & Zhou, G. (2018). Market intraday momentum. Journal of Financial Economics 129(2), 394–414. https://doi.org/10.1016/j.jfineco.2018.05.009 (accessed 2026-08-12)
Baltussen, G., Da, Z., Lammers, S. & Martens, M. (2021). Hedging demand and market intraday momentum. Journal of Financial Economics 142(1), 377–403. https://doi.org/10.1016/j.jfineco.2021.04.029 (accessed 2026-08-12)

# §4.1 — Post-publication decay

Schwert, G. W. (2003). Anomalies and Market Efficiency. Handbook of the Economics of Finance, 939–974. https://doi.org/10.1016/S1574-0102(03)01024-0 (accessed 2026-08-12)
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 (accessed 2026-08-12)
Marquering, W., Nisser, J. & Valla, T. (2006). Disappearing anomalies: a dynamic analysis of the persistence of anomalies. Applied Financial Economics 16(4), 291–302. https://doi.org/10.1080/09603100500400361 (accessed 2026-08-12)
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 (accessed 2026-08-12)
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 (accessed 2026-08-12)

# §4.2 — Multiple testing, data snooping, backtest overfitting

White, H. (2000). A Reality Check for Data Snooping. Econometrica 68(5), 1097–1126. https://doi.org/10.1111/1468-0262.00152 (accessed 2026-08-12)
Sullivan, R., Timmermann, A. & White, H. (2001). Dangers of data mining: The case of calendar effects in stock returns. Journal of Econometrics 105(1), 249–286. https://doi.org/10.1016/s0304-4076(01)00077-x (accessed 2026-08-12)
Hansen, P. R. (2005). A Test for Superior Predictive Ability. Journal of Business & Economic Statistics 23(4), 365–380. https://doi.org/10.1198/073500105000000063 (accessed 2026-08-12)
Romano, J. P. & Wolf, M. (2005). Stepwise Multiple Testing as Formalized Data Snooping. Econometrica 73(4), 1237–1282. https://doi.org/10.1111/j.1468-0262.2005.00615.x (accessed 2026-08-12)
Benjamini, Y. & Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society, Series B 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x (accessed 2026-08-12)
Harvey, C. R., Liu, Y. & Zhu, H. (2016). …and the Cross-Section of Expected Returns. Review of Financial Studies 29(1), 5–68. https://doi.org/10.1093/rfs/hhv059 (accessed 2026-08-12)
Harvey, C. R. (2017). Presidential Address: The Scientific Outlook in Financial Economics. Journal of Finance 72(4), 1399–1440. https://doi.org/10.1111/jofi.12530 (accessed 2026-08-12)
Bailey, D. H. & López de Prado, M. (2014). The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting, and Non-Normality. Journal of Portfolio Management 40(5), 94–107. https://doi.org/10.3905/jpm.2014.40.5.094 (accessed 2026-08-12)
Bailey, D. H., Borwein, J. M., López de Prado, M. & Zhu, Q. J. (2014). Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance. Notices of the AMS 61(5), 458–471. https://doi.org/10.1090/noti1105 (accessed 2026-08-12)
Bailey, D. H., Borwein, J. M., López de Prado, M. & Zhu, Q. J. (2016). The probability of backtest overfitting. Journal of Computational Finance 20(4), 39–69. https://doi.org/10.21314/jcf.2016.322 (accessed 2026-08-12)
Hou, K., Xue, C. & Zhang, L. (2020). Replicating Anomalies. Review of Financial Studies 33(5), 2019–2133. https://doi.org/10.1093/rfs/hhy131 (accessed 2026-08-12)
Jensen, T. I., Kelly, B. & Pedersen, L. H. (2023). Is There a Replication Crisis in Finance? Journal of Finance 78(5), 2465–2518. https://doi.org/10.1111/jofi.13249 (accessed 2026-08-12)
Chordia, T., Goyal, A. & Saretto, A. (2020). Anomalies and False Rejections. Review of Financial Studies 33(5), 2134–2179. https://doi.org/10.1093/rfs/hhaa018 (accessed 2026-08-12)
Harvey, C. R. & Liu, Y. (2020). False (and Missed) Discoveries in Financial Economics. Journal of Finance 75(5), 2503–2553. https://doi.org/10.1111/jofi.12951 (accessed 2026-08-12)
Giglio, S., Liao, Y. & Xiu, D. (2021). Thousands of Alpha Tests. Review of Financial Studies 34(7), 3456–3496. https://doi.org/10.1093/rfs/hhaa111 (accessed 2026-08-12)

# §4.3 — Microstructure & artifacts

Roll, R. (1984). A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market. Journal of Finance 39(4), 1127–1139. https://doi.org/10.1111/j.1540-6261.1984.tb03897.x (accessed 2026-08-12)
Blume, M. E. & Stambaugh, R. F. (1983). Biases in computed returns: An application to the size effect. Journal of Financial Economics 12(3), 387–404. https://doi.org/10.1016/0304-405x(83)90056-9 (accessed 2026-08-12)
Fisher, L. (1966). Some New Stock-Market Indexes. Journal of Business 39(S1), 191–225. https://doi.org/10.1086/294848 (accessed 2026-08-12)
Zhang, L., Mykland, P. A. & Aït-Sahalia, Y. (2005). A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High-Frequency Data. JASA 100(472), 1394–1411. https://doi.org/10.1198/016214505000000169 (accessed 2026-08-12)
Aït-Sahalia, Y., Mykland, P. A. & Zhang, L. (2005). How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise. Review of Financial Studies 18(2), 351–416. https://doi.org/10.1093/rfs/hhi016 (accessed 2026-08-12)
Hansen, P. R. & Lunde, A. (2006). Realized Variance and Market Microstructure Noise. Journal of Business & Economic Statistics 24(2), 127–161. https://doi.org/10.1198/073500106000000071 (accessed 2026-08-12)
Bandi, F. M. & Russell, J. R. (2006). Separating microstructure noise from volatility. Journal of Financial Economics 79(3), 655–692. https://doi.org/10.1016/j.jfineco.2005.01.005 (accessed 2026-08-12)
Bandi, F. M. & Russell, J. R. (2008). Microstructure Noise, Realized Variance, and Optimal Sampling. Review of Economic Studies 75(2), 339–369. https://doi.org/10.1111/j.1467-937x.2008.00474.x (accessed 2026-08-12)

# §4.4 — Time-series methodology

Lo, A. W. (1991). Long-Term Memory in Stock Market Prices. Econometrica 59(5), 1279–1313. https://doi.org/10.2307/2938368 (accessed 2026-08-12)
Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica 37(3), 424–438. https://doi.org/10.2307/1912791 (accessed 2026-08-12)
Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics 17(3), 1217–1241. https://doi.org/10.1214/aos/1176347265 (accessed 2026-08-12)
Politis, D. N. & Romano, J. P. (1994). The Stationary Bootstrap. JASA 89(428), 1303–1313. https://doi.org/10.1080/01621459.1994.10476870 (accessed 2026-08-12)
Bai, J. & Perron, P. (1998). Estimating and Testing Linear Models with Multiple Structural Changes. Econometrica 66(1), 47–78. https://doi.org/10.2307/2998540 (accessed 2026-08-12)

# §4.5 — Transaction costs

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 (accessed 2026-08-12)
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 (accessed 2026-08-12)
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 (accessed 2026-08-12)
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 (accessed 2026-08-12)
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 (accessed 2026-08-12)
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 (accessed 2026-08-12)
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 (accessed 2026-08-12)
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 (accessed 2026-08-12)
Frazzini, A., Israel, R. & Moskowitz, T. J. (2012). Trading Costs of Asset Pricing Anomalies. SSRN working paper. https://doi.org/10.2139/ssrn.2294498 (accessed 2026-08-12)
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 (accessed 2026-08-12)
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 (accessed 2026-08-12)

# §4.6 — This dataset

HF Market Data API documentation & OpenAPI schema. https://www.hfmarketdata.io/docs (accessed 2026-08-12) — capabilities established empirically in research/data_source_profile.md (Experiment A).