Modeling Trade Direction

Rosenthal, Dale W.R. (2008): Modeling Trade Direction. Forthcoming in: Journal of Financial Econometrics

Abstract

I propose a modeling approach to classifying trades as buys or sells. Modeled classifications consider information strengths, microstructure effects, and classification correlations. I also propose estimators for quotes prevailing at trade time. Comparisons using 2,800 US stocks show modeled classifications are 1-2% more accurate than current methods across dates, sectors, and the spread. For Nasdaq and NYSE stocks, 1% and 1.3% of improvement comes from using information strengths; 0.9% and 0.7% of improvement comes from estimating quotes. I find evidence past studies used unclean data and indications of short-term price predictability. The method may help detect destabilizing order flow.

• Item Type: MPRA Paper
• Language: English
• Keywords: trade classification; delay models; trade publishing delays; prevailing quotes; trade direction; flash crash detection
• Subjects: G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D82 - Asymmetric and Private Information ; Mechanism Design

References

Aigner, D. J. (1973)., “Regression with a Binary Independent Variable Subject to Errors of Observation.” Journal of Econometrics, 1, 49–60.

Archipelago Holdings, Inc. (2005a)., ArcaBook and ArcaTrade Historical: For the Archipelago Exchange and ArcaEdge, Version 1.1. Chicago.

Archipelago Holdings, Inc. (2005b)., “ArcaEx Releases December 2004 Transaction Volume Data.” Retrieved on 11 June 2008 from http://www. archipelago-exchange.com/inside/news/news_20050111.asp.

Asquith, P., R. Oman, and C. Safaya. (2010)., “Short Sales and Trade Classification Algorithms.” Journal of Financial Markets, 13, 157–173.

Benveniste, L. M., S. M. Erdal, and J. William J. Wilhelm. (1998)., “Who Benefits From Secondary Market Price Stabilization of IPOs?” Journal of Banking and Finance, 22, 741–767.

Bessembinder, H. (2003)., “Issues in Assessing Trade Execution Costs.” Journal of Financial Markets, 233–257.

Boehmer, E., J. Grammig, and E. Theissen. (2007)., “Estimating the Probability of Informed Trading: Does Trade Misclassification Matter?” Journal of Financial Markets, 10, 26–47.

Boone, J. P. and K. K. Raman. (2001)., “Off-Balance Sheet R&D Assets and Market Liquidity.” Journal of Accounting and Public Policy, 20, 97–128.

Chakrabarty, B., B. Li, V. Nguyen, and R. A. V. Ness. (2007)., “Trade Classification Algorithms for Electronic Communications Network Trades.” Journal of Banking and Finance, 31, 3806–3821.

Cox, D. R. (1975)., “Partial Likelihood.” Biometrika, 62, 269–276.

Danielsen, B. R., R. A. V. Ness, and R. S. Warr. (2007)., “Auditor Fees, Market Microstructure, and Firm Transparency.” Journal of Business Finance and Accounting, 34, 202–221.

Ellis, K., R. Michaely, and M. O’Hara. (2000)., “The Accuracy of Trade Classification Rules: Evidence from Nasdaq.” Journal of Financial and Quantitative Analysis, 35, 529–551.

Erlang, A. K. (1909)., “The Theory of Probabilities and Telephone Conversations.” Nyt Tidsskrift for Matematik, B, 33–39.

Finucane, T. J. (2000)., “A Direct Test of Methods for Inferring Trade Direction from Intra-Day Data.” Journal of Financial and Quantitative Analysis, 35, 553–576.

Hansen, P. R., A. Lunde, and J. M. Nason. (2011)., “The Model Confidence Set.” Econometrica, 79, 453–497.

Hasbrouck, J. (1992)., “Using the TORQ Database.” Working Paper 92-05, NYSE.

Heagerty, P. J. and S. L. Zeger. (2000)., “Marginalized Multilevel Models and Likelihood Inference.” Statistical Science, 15, 1–19.

Henker, T. and J. Wang. (2006)., “On the Importance of Timing Specifications in Market Microstructure Research.” Journal of Financial Markets, 9, 162–179.

Kauermann, G. and R. J. Carroll. (2001)., “A Note on the Efficiency of Sandwich Covariance Matrix Estimation.” Journal of the American Statistical Association, 96, 1387–1396.

Lee, C. M. C. and M. J. Ready. (1991)., “Inferring Trade Direction From Intraday Data.” Journal of Finance, 46, 733–746.

McCullagh, P. (1987)., Tensor Methods in Statistics. London: Chapman and Hall.

McCullagh, P. and J. A. Nelder (1989)., Generalized Linear Models. 2nd edn., London: Chapman and Hall.

Nocedal, J. and S. J. Wright (2006)., Numerical Optimization. 2nd edn., New York: Springer.

Odders-White, E. R. (2000)., “On the Occurrence and Consequences of Inaccurate Trade Classification.” Journal of Financial Markets, 3, 259–286.

Peterson, M. and E. Sirri. (2003)., “Evaluation of the Biases in Execution Cost Estimation Using Trade and Quote Data.” Journal of Financial Markets, 6, 259–280.

Roll, R. (1984)., “A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market.” Journal of Finance, 39, 1127–1139.

Rosenthal, D. W. R. (2008)., “Trade Classification and Nearly-Gamma Random Variables.” PhD dissertation, University of Chicago, Department of Statistics.

Schultz, P. H. and M. A. Zaman. (1994)., “Aftermarket Support and Underpricing of Initial Public Offerings.” Journal of Financial Economics, 35, 199–219.

Stoll, H. R. (2006)., “Electronic Trading in Stock Markets.” Journal of Economic Perspectives, 20, 153–174.

Stoll, H. R. and C. Schenzler. (2006)., “Trades Outside the Quotes: Reporting Delay, Trading Option, or Trade Size?” Journal of Financial Economics, 79, 615–653.

Tanggaard, C. (2004)., “Errors in Trade Classification: Consequences and Remedies.” Working Paper 420680, SSRN.

Vergote, O. (2005)., “How to Match Trades and Quotes for NYSE Stocks?” Working paper, Katholieke Universiteit Leuven.

Wong, W. H. (1986)., “Theory of Partial Likelihood.” Annals of Statistics, 14, 88–123.