Rosenthal, Dale W.R. (2008): Modeling Trade Direction.
Download (3MB) | Preview
The problem of classifying trades as buys or sells is examined. I propose estimated quotes for midpoint and bid/ask tests and a modeling approach to classification. Prevailing quotes are estimated using flexible approximations to the distribution for delays of quotes relative to trade timestamps. Classification is done by a generalized linear model which includes improved versions of midpoint, tick, and bid/ask tests. The model also considers the relative strengths of these tests, can account for market microstructure peculiarities, and allows for autocorrelations and cross-correlations in trade direction. The correlation modeling corrects for pseudoreplication, yielding more accurate standard errors and fixed effect estimates. Further, the model estimates probabilities of correct classification. The model is compared to various trade classification methods using a sample of 2,836 domestic US stocks from an unexplored, recent, and readily-available dataset. Out of sample, modeled classifications are 1-2% more accurate overall than current methods; this improvement is consistent across dates, sectors, and locations relative to the inside quote. For Nasdaq and NYSE stocks, 1% and 1.3% of the improvement comes from using relative strengths of the various tests; 0.9% and 0.7% of the improvement, respectively, comes from using some form of estimated quotes. For AMEX stocks, a 0.4% improvement is attributed to using a lagged version of the bid/ask test. I also find indications of short- and ultra-short-term alpha.
|Item Type:||MPRA Paper|
|Original Title:||Modeling Trade Direction|
|Keywords:||market microstructure; trade classification; generalized linear mixed model; ultra-high-frequency data analysis|
|Subjects:||G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency; Event Studies
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
|Depositing User:||Dale W.R. Rosenthal|
|Date Deposited:||28. Aug 2008 06:12|
|Last Modified:||13. Feb 2013 23:13|
Archipelago Holdings, Inc. ArcaBook and ArcaTrade Historical: For the Archipelago Exchange and ArcaEdge, Version 1.1, February 2005. Archipelago Holdings, Inc.: Chicago.
Archipelago Holdings, Inc. “ArcaEx Releases December 2004 Transaction Volume Data”, 11 January 2005. Retrieved on 11 June 2008 from http://www.archipelago-exchange.com/inside/news/news_20050111.asp.
Asquith, P.; R. Oman and C. Safaya, “Short Sales and Trade Classification Algorithms” SSRN Working Paper (July 2, 2007). Retrieved on 15 June 2008 from http://ssrn.com/abstract=951420.
Bartlett, M. S. “Approximate Confidence Intervals”, Biometrika, 40:1/2(1953a), 12–19.
Bartlett, M. S. “Approximate Confidence Intervals: II. More Than One Unknown Parameter”, Biometrika, 40:3/4(1953), 306–317.
Bennett, P. and Wei, L. “Market Structure, Fragmentation, and Market Quality”, Journal of Financial Markets, 9:1(2006), 49–78.
Boehmer, E. “Dimensions of Execution Quality: Recent Evidence for US Equity Markets”, Journal of Financial Economics, 78:3(2005), 553-582.
Box, G. E. P. “A General Distribution Theory for a Class of Likelihood Criteria”, Biometrika, 36:3/4(1949), 317–346.
Caudill, S. B.; B. B. Marshall and J. Garner. “Improved Trade Classification Rules: Estimates Using a Logit Model Based on Misclassified Data”, Atlantic Economic Journal, 32:3(2004), 256.
Chacko, G. C.; J. W. Jurek and E. Stafford. “The Price of Immediacy”, Journal of Finance, 63:3(2008), 1253–1290.
Cox, D. R. “Partial Likelihood”, Biometrika, 62:2(1975), 269–276.
Ellis, K.; R. Michaely and M. O’Hara. “The Accuracy of Trade Classification Rules: Evidence from Nasdaq”, Journal of Financial and Quantitative Analysis 35:4(2000), 529–551.
Erlang, A. K. “The Theory of Probabilities and Telephone Conversations”, Nyt Tidsskrift for Matematik, B:20(1909), 33–39.
Financial Industry Regulatory Authority. Notice to Members 07-23: NASD Trade Reporting Requirements, 11 May 2007. Retrieved on 30 December 2007 from http://www.finra.org/RulesRegulation/NoticestoMembers/2007NoticestoMembers/P019150.
Finucane, T. J. “A Direct Test of Methods for Inferring Trade Direction from IntraDay Data”, Journal of Financial and Quantitative Analysis 35:4(2000), 553–576.
Fitzmaurice, G.M.; N. M. Laird and J. H. Ware. Applied Longitudinal Analysis, 2004. Wiley: New York.
Forrester, J. W. “Information Sources for Modeling the National Economy”, Journal of the American Statistical Association, 75:371(1980), 555–566.
Garman, M. B. “Market Microstructure”, Journal of Financial Economics, 3:3(1976), 257–275.
Hasbrouck, J. “Measuring the Information Content of Stock Trades”, Journal of Finance, 46:1(1991), 179–207.
Hasbrouck, J. Empirical Market Microstructure, 2007. Oxford University Press: New York.
Hasbrouck, J. “Using the TORQ Database.” NYSE Working Paper #92-05. NYSE (1992).
Hasbrouck, J. and Schwartz, R. A. “Liquidity and Execution Costs in Equity Markets”, Journal of Portfolio Management, 14:3(1988), 10–16.
Hasbrouck, J.; G. Sofianos and D. Sosebee. “New York Stock Exchange Systems and Trading Procedures.” NYSE Working Paper #93-01. NYSE (1993).
Heagerty, P. J. and Zeger, S. L. “Marginalized Multilevel Models and Likelihood Inference”, Statistical Science 15:1(2000), 1–19.
Henker, T. and Wang, J. “On the Importance of Timing Specifications in Market Microstructure Research”, Journal of Financial Markets 9(2006), 162–179.
Johnson, T. C. “Volume, Liquidity, and Liquidity Risk”, Journal of Financial Economics, 87:2(2008), 388–417.
Kauermann, G. and Carroll, R. J. “A Note on the Efficiency of Sandwich Covariance Matrix Estimation”, Journal of the American Statistical Association 96:456(2001), 1387–1396.
Keim, D. B. and Madhavan, A. “The Upstairs Market for Large-Block Transactions: Analysis and Measurement of Price Effects”, Review of Financial Studies 9:1(1996), 1–36.
Lee, C. M. C. and Ready, M. J. “Inferring Trade Direction From Intraday Data”, Journal of Finance 46:2(1991), 733–746.
Lefèvre, E. Reminiscences of a Stock Operator, 1923. Doubleday, Doran & Company: New York.
McCullagh, P. and Nelder, J. A. Generalized Linear Models, 2nd Edition, 1989. Chapman and Hall: London.
McCullagh, P. Tensor Methods in Statistics, 1987. Chapman and Hall: London.
McCulloch, C. E. and Searle, S. R. Generalized, Linear, and Mixed Models, 2001. Wiley: New York.
Mead, R. The Design of Experiments, 1988. Cambridge University Press.
National Association of Securities Dealers. Notice to Members 99-66: NASD Trade Reporting Requirements, 10 August 1999. Retrieved on 31 December 2007 from http://www.finra.org/RulesRegulation/NoticestoMembers/1999NoticestoMembers/P004186.
The Nasdaq Stock Market LLC. The Nasdaq Closing Cross Fact Sheet, November 2006. Retrieved on 3 January 2007 from http://www.nasdaqtrader.com/trader/openclose/ccfactsheet.pdf.
The Nasdaq Stock Market LLC. The Nasdaq Opening Cross Fact Sheet, November 2006. Retrieved on 3 January 2007 from http://www.nasdaqtrader.com/trader/openclose/openfactsheet.pdf.
New York Stock Exchange, Inc. TAQ 3 User’s Guide, Version 1.0, 3 January 2005. New York Stock Exchange, Inc.: New York.
New York Stock Exchange, Inc. New Product: NYSE Liquidity Replenishment Points, 7 September 2006. Retrieved on 7 December 2006 from http://www.nysedata.com/nysedata/default.aspx?tabid=155&id=114.
Niederhoffer, V. and Osborne, M. F. M. “Market Making and Reversal on the Stock Exchange”, Journal of the American Statistical Association 61:316(1966), 897–916.
Nocedal, J. and Wright, S. J. Numerical Optimization, 2nd Edition, 2006. Springer: New York.
O’Hara, M. Market Microstructure Theory, 1997. Blackwell: London.
Odders-White, E. R. “On the Occurrence and Consequences of Inaccurate Trade Classification”, Journal of Financial Markets 3(2000), 259–286.
Osborne, M. F. M. “The Dynamics of Stock Trading”, Econometrica 33:1(1965), 88–113.
Pesaran, M. H. “Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure”, Econometrica 74:4(2006), 967–1012.
Peterson, M. and Sirri, E. “Evaluation of the Biases in Execution Cost Estimation Using Trade and Quote Data”, Journal of Financial Markets 6(2003), 259–280.
Rosenthal, D. W. R. “Trade Classification and Nearly-Gamma Random Variables” (Ph.D. dissertation, University of Chicago, 2008).
Searle, Shayle R.; G. Casella and C. E. McCulloch. Variance Components, 1992. Wiley: New York.
Securities Training Corporation. Series 55: The Equity Trader Examination Study Manual, 6 November 2006. Securities Training Corporation: New York.
Stoll, H. R. “Electronic Trading in Stock Markets”, Journal of Economic Perspectives 20:1(2006), 153–174.
Stoll, H.R. and Schenzler, C. “Trades Outside the Quotes: Reporting Delay, Trading Option, or Trade Size?”, Journal of Financial Economics 79(2006), 615–653.
U.S. Securities and Exchange Commission. Final Rule: Disclosure of Order Execution and Routing Practices, 17 November 2000. Retrieved 20 December 2007 from http://www.sec.gov/rules/final/34- 43590.pdf.
U.S. Securities and Exchange Commission. Regulation NMS, 9 June 2005. Retrieved 20 December 2007 from http://www.sec.gov/rules/final/3451808.pdf.
van Belle, G. Statistical Rules of Thumb, 2002. Wiley: New York.
Vergote, O. “How to Match Trades and Quotes for NYSE Stocks?” KU Working Paper. Katholieke Universiteit Leuven (2005).
Wong, W. H. “Theory of Partial Likelihood”, Annals of Statistics, 14:1(1986), 88–123.
Zellner, A. “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias”, Journal of the American Statistical Association 57:298(1962), 348–368.
Available Versions of this Item
- Modeling Trade Direction. (deposited 28. Aug 2008 06:12) [Currently Displayed]