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Trade-size clustering and price efficiency

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  • Chen, Tao

Abstract

Using a sample of 26 markets, this paper investigates if trade-size clustering affects price efficiency. Our results suggest that more clustering trades are associated with greater resemblance of a random walk, less pricing errors, and shorter price delays. Moreover, we examine three underlying mechanisms to explain how clustering improves efficiency. First, we show that clustering trades are informative, consistent with the idea that stealth traders leverage such tactics to convey private information to prices. Second, we discover that clustering trades are positively related to investor attention (stock liquidity), implying that informed clustering trades happen at the presence of enormous uninformed investors. High attention and liquid markets help reduce the trading friction, thereby prompting quick price adjustments to private information released by the stealth trading.

Suggested Citation

  • Chen, Tao, 2019. "Trade-size clustering and price efficiency," Japan and the World Economy, Elsevier, vol. 49(C), pages 195-203.
    • Handle: RePEc:eee:japwor:v:49:y:2019:i:c:p:195-203
    DOI: 10.1016/j.japwor.2018.12.002
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    1. Wei-Yu Kuo & Tse-Chun Lin & Jing Zhao, 2015. "Cognitive Limitation and Investment Performance: Evidence from Limit Order Clustering," The Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 838-875.
    2. Hasbrouck, Joel, 1991. "Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    3. Ajay Kumar Mishra & Thomas H. McInish & Trilochan Tripathy, 2015. "Price movement and trade size on the National Stock Exchange of India," Applied Economics, Taylor & Francis Journals, vol. 47(45), pages 4847-4854, September.
    4. Kewei Hou & Tobias J. Moskowitz, 2005. "Market Frictions, Price Delay, and the Cross-Section of Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 981-1020.
    5. Dufour, Alfonso & Engle, Robert F, 1999. "Time and the Price Impact of a Trade," University of California at San Diego, Economics Working Paper Series qt62c0h04j, Department of Economics, UC San Diego.
    6. Owain ap Gwilym & Lei Meng, 2010. "Size clustering in the FTSE100 index futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(5), pages 432-443, May.
    7. Verousis, Thanos & ap Gwilym, Owain, 2013. "Trade size clustering and the cost of trading at the London Stock Exchange," International Review of Financial Analysis, Elsevier, vol. 27(C), pages 91-102.
    8. David Neumark & Steven A. Sharpe, 1992. "Market Structure and the Nature of Price Rigidity: Evidence from the Market for Consumer Deposits," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(2), pages 657-680.
    9. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    10. Daniel Andrei & Michael Hasler, 2015. "Investor Attention and Stock Market Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 33-72.
    11. Glosten, Lawrence R. & Milgrom, Paul R., 1985. "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders," Journal of Financial Economics, Elsevier, vol. 14(1), pages 71-100, March.
    12. Ho, Thomas & Stoll, Hans R., 1981. "Optimal dealer pricing under transactions and return uncertainty," Journal of Financial Economics, Elsevier, vol. 9(1), pages 47-73, March.
    13. Chordia, Tarun & Subrahmanyam, Avanidhar, 2004. "Order imbalance and individual stock returns: Theory and evidence," Journal of Financial Economics, Elsevier, vol. 72(3), pages 485-518, June.
    14. Alfonso Dufour & Robert F. Engle, 2000. "Time and the Price Impact of a Trade," Journal of Finance, American Finance Association, vol. 55(6), pages 2467-2498, December.
    15. Joon Chae & Eun Jung Lee, 2011. "An analysis of split orders in an index options market," Applied Economics Letters, Taylor & Francis Journals, vol. 18(5), pages 473-477.
    16. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    17. Ekkehart Boehmer & Juan (Julie) Wu, 2013. "Short Selling and the Price Discovery Process," The Review of Financial Studies, Society for Financial Studies, vol. 26(2), pages 287-322.
    18. Alex Frino & David Johnstone & Hui Zheng, 2010. "Anonymity, Stealth Trading, and the Information Content of Broker Identity," The Financial Review, Eastern Finance Association, vol. 45(3), pages 501-522, August.
    19. Lee, Charles M C & Ready, Mark J, 1991. "Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
    20. Anat R. Admati, Paul Pfleiderer, 1988. "A Theory of Intraday Patterns: Volume and Price Variability," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 3-40.
    21. Harris, Lawrence, 1991. "Stock Price Clustering and Discreteness," The Review of Financial Studies, Society for Financial Studies, vol. 4(3), pages 389-415.
    22. Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
    23. Laurie Simon Hodrick & Pamela C. Moulton, 2009. "Liquidity: Considerations of a Portfolio Manager," Financial Management, Financial Management Association International, vol. 38(1), pages 59-74, March.
    24. Barclay, Michael J. & Warner, Jerold B., 1993. "Stealth trading and volatility : Which trades move prices?," Journal of Financial Economics, Elsevier, vol. 34(3), pages 281-305, December.
    25. Meng, Lei & Verousis, Thanos & ap Gwilym, Owain, 2013. "A substitution effect between price clustering and size clustering in credit default swaps," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 139-152.
    26. Benjamin M. Blau & Bonnie F. Van Ness & Robert A. Van Ness, 2012. "Trade Size And Price Clustering: The Case Of Short Sales And The Suspension Of Price Tests," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 35(2), pages 159-182, June.
    27. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2005. "Evidence on the speed of convergence to market efficiency," Journal of Financial Economics, Elsevier, vol. 76(2), pages 271-292, May.
    28. Maureen O'Hara & Chen Yao & Mao Ye, 2014. "What's Not There: Odd Lots and Market Data," Journal of Finance, American Finance Association, vol. 69(5), pages 2199-2236, October.
    29. Shane A. Corwin & Paul Schultz, 2012. "A Simple Way to Estimate Bid‐Ask Spreads from Daily High and Low Prices," Journal of Finance, American Finance Association, vol. 67(2), pages 719-760, April.
    30. Comerton-Forde, Carole & Putniņš, Tālis J., 2015. "Dark trading and price discovery," Journal of Financial Economics, Elsevier, vol. 118(1), pages 70-92.
    31. Joel Hasbrouck, 2009. "Trading Costs and Returns for U.S. Equities: Estimating Effective Costs from Daily Data," Journal of Finance, American Finance Association, vol. 64(3), pages 1445-1477, June.
    32. Dechow, Patricia M. & Hutton, Amy P. & Meulbroek, Lisa & Sloan, Richard G., 2001. "Short-sellers, fundamental analysis, and stock returns," Journal of Financial Economics, Elsevier, vol. 61(1), pages 77-106, July.
    33. Alexander, Gordon J. & Peterson, Mark A., 2007. "An analysis of trade-size clustering and its relation to stealth trading," Journal of Financial Economics, Elsevier, vol. 84(2), pages 435-471, May.
    34. Robin K. Chou & Yun‐Yi Wang, 2009. "Strategic order splitting, order choice, and aggressiveness: Evidence from the Taiwan futures exchange," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 29(12), pages 1102-1129, December.
    35. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    36. Chen, Tao, 2018. "Round-number biases and informed trading in global markets," Journal of Business Research, Elsevier, vol. 92(C), pages 105-117.
    37. Barnea, Amir, 1974. "Performance Evaluation of New York Stock Exchange Specialists," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 9(4), pages 511-535, September.
    38. Turan G. Bali & Lin Peng & Yannan Shen & Yi Tang, 2014. "Liquidity Shocks and Stock Market Reactions," The Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1434-1485.
    39. Moulton, Pamela C., 2005. "You can't always get what you want: Trade-size clustering and quantity choice in liquidity," Journal of Financial Economics, Elsevier, vol. 78(1), pages 89-119, October.
    40. Ekkehart Boehmer & Eric K. Kelley, 2009. "Institutional Investors and the Informational Efficiency of Prices," The Review of Financial Studies, Society for Financial Studies, vol. 22(9), pages 3563-3594, September.
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    More about this item

    Keywords

    Size clustering; Price efficiency; Stealth trading;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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