How and When Are High-Frequency Stock Returns Predictable?

Published Online:https://doi.org/10.1287/mnsc.2022.02435

References

  • Aït-Sahalia Y, Brunetti C (2020) High frequency traders and the price process. J. Econometrics 217(1):20–45.CrossrefGoogle Scholar
  • Aït-Sahalia Y, Sağlam M (2024) High frequency market making: The role of speed. J. Econometrics 239(2):105421.CrossrefGoogle Scholar
  • Alvim LG, dos Santos CN, Milidiu RL (2010) Daily volume forecasting using high frequency predictors. Proc. 10th IASTED Internat. Conf. vol. 674, no. 047 (IASTED, Calgary, AB).Google Scholar
  • Baron M, Brogaard JA, Hagströmer B, Kirilenko A (2019) Risk and return in high frequency trading. J. Financial Quant. Anal. 54(2):993–1024.CrossrefGoogle Scholar
  • Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. J. Finance 74(1):449–492. CrossrefGoogle Scholar
  • Cong LW, Feng G, He J, Wang Y (2024) Mosaics of predictability. Preprint, submitted June 6, http://dx.doi.org/10.2139/ssrn.4853767.Google Scholar
  • Cont R, Stoikov S, Talreja R (2010) A stochastic model for order book dynamics. Oper. Res. 58(3):549–563.LinkGoogle Scholar
  • Dixon M (2018) A high-frequency trade execution model for supervised learning. High Frequency 1(1):35–52.CrossrefGoogle Scholar
  • Easley D, de Prado ML, O’Hara M, Zhang Z (2021) Microstructure in the machine age. Rev. Financial Stud. 34(7):3316–3363.CrossrefGoogle Scholar
  • Ellis K, Michaely R, O’Hara M (2000) The accuracy of trade classification rules: Evidence from Nasdaq. J. Financial Quant. Anal. 35(4):529–551.CrossrefGoogle Scholar
  • Fama EF (1970) Efficient capital markets: A review of theory and empirical work. J. Finance 25(2):383–417.CrossrefGoogle Scholar
  • Fan J, Ke Y, Wang K (2020a) Factor-adjusted regularized model selection. J. Econom. 216(1):71–85.CrossrefGoogle Scholar
  • Fan J, Li R, Zhang CH, Zou H (2020b) Statistical Foundations of Data Science (Chapman and Hall/CRC, Boca Raton, FL).CrossrefGoogle Scholar
  • Glosten LR, Harris LE (1988) Estimating the components of the bid/ask spread. J. Financial Econom. 21(1):123–142.CrossrefGoogle Scholar
  • Hagströmer B (2021) Bias in the effective bid-ask spread. J. Financial Econom. 142(1):314–337.CrossrefGoogle Scholar
  • Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics, 2nd ed. (Springer-Verlag, New York).CrossrefGoogle Scholar
  • Hirschey NH (2021) Do high-frequency traders anticipate buying and selling pressure? Management Sci. 67(6):3321–3345.LinkGoogle Scholar
  • Huang RD, Stoll HR (1994) Market microstructure and stock return predictions. Rev. Financial Stud. 7(1):179–213.CrossrefGoogle Scholar
  • Kercheval AN, Zhang Y (2015) Modelling high-frequency limit order book dynamics with support vector machines. Quant. Finance 15(8):1315–1329.CrossrefGoogle Scholar
  • Knoll J, Stübinger J, Grottke M (2019) Exploiting social media with higher-order factorization machines: Statistical arbitrage on high-frequency data of the S&P 500. Quant. Finance 19(4):571–585.CrossrefGoogle Scholar
  • Kyle AS (1985) Continuous auctions and insider trading. Econometrica 53(6):1315–1335.CrossrefGoogle Scholar
  • Lee CM, Ready MJ (1991) Inferring trade direction from intraday data. J. Finance 46(2):733–746.CrossrefGoogle Scholar
  • Lewis M (2015) Flash Boys: A Wall Street Revolt (W. W. Norton & Company, New York).Google Scholar
  • Lo AW, MacKinlay AC (2002) A Non-Random Walk down Wall Street (Princeton University Press, Princeton, NJ).Google Scholar
  • Malkiel BG (1973) A Random Walk down Wall Street (W. W. Norton & Company, New York).Google Scholar
  • Moallemi CC, Sağlam M (2013) OR forum—The cost of latency in high-frequency trading. Oper. Res. 61(5):1070–1086.LinkGoogle Scholar
  • Ntakaris A, Magris M, Kanniainen J, Gabbouj M, Iosifidis A (2018) Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods. J. Forecasting 37(8):852–866.CrossrefGoogle Scholar
  • NYSE (2023) Rule 7.6 – Minimum Price Variation (MPV). NYSE Regulation Rules, https://www.nyse.com/regulation/rules.Google Scholar
  • O’Hara M, Yao C, Ye M (2014) What’s not there: Odd lots and market data. J. Finance 69(5):2199–2236.CrossrefGoogle Scholar
  • Panayi E, Peters GW, Danielsson J, Zigrand JP (2018) Designating market maker behaviour in limit order book markets. Econom. Statist. 5(C):20–44.Google Scholar
  • Roll R (1984) A simple model of the implicit bid-ask spread in an efficient market. J. Finance 39(4):1127–1139.Google Scholar
  • Roşu I (2009) A dynamic model of the limit order book. Rev. Financial Stud. 22(11):4601–4641.CrossrefGoogle Scholar
  • Sirignano JA (2019) Deep learning for limit order books. Quant. Finance 19(4):549–570.CrossrefGoogle Scholar
  • Timmermann A (2018) Forecasting methods in finance. Annual Rev. Financial Econom. 10(1):449–479.CrossrefGoogle Scholar
  • Tsantekidis A, Passalis N, Tefas A, Kanniainen J, Gabbouj M, Iosifidis A (2017) Forecasting stock prices from the limit order book using convolutional neural networks. 2017 IEEE 19th Conf. Bus. Informatics (CBI), vol. 1 (IEEE, Piscataway, NJ), 7–12.Google Scholar
  • Zheng B, Moulines E, Abergel F (2013) Price jump prediction in a limit order book. J. Math. Finance 3(2):242–255.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.