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Price Clustering: Evidence Using Comprehensive Limit‐Order Data

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  • Chaoshin Chiao
  • Zi‐May Wang

Abstract

Employing comprehensive limit‐order data which identify investor types, this paper examines the clustering pattern of limit‐order prices. First, limit orders, particularly those submitted by individual investors (IIs), tend to cluster at integer and even prices. Second, nonmarketable limit‐order prices cluster more than marketable limit‐order prices, indicating that aggressive limit orders generally embed more information. Third, investors choosing even‐priced limit orders are not penalized by lower execution ratios. Fourth, investors (particularly IIs) strategically exhibit front‐running behavior. Fifth, price clustering indeed creates price barriers. Finally, the degree of price clustering using trade data is significantly underestimated, compared to that using limit‐order data.

Suggested Citation

  • Chaoshin Chiao & Zi‐May Wang, 2009. "Price Clustering: Evidence Using Comprehensive Limit‐Order Data," The Financial Review, Eastern Finance Association, vol. 44(1), pages 1-29, February.
    • Handle: RePEc:bla:finrev:v:44:y:2009:i:1:p:1-29
    DOI: 10.1111/j.1540-6288.2008.00208.x
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    Cited by:

    1. Qin Wang & Jun Zhang, 2016. "Trade Size Clustering In The E-Mini Index Futures Markets," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 39(3), pages 247-262, September.
    2. Vladim'ir Hol'y & Petra Tomanov'a, 2021. "Modeling Price Clustering in High-Frequency Prices," Papers 2102.12112, arXiv.org, revised Mar 2021.
    3. Fraser-Mackenzie, P. & Sung, M. & Johnson, J.E.V., 2015. "The prospect of a perfect ending: Loss aversion and the round-number bias," Organizational Behavior and Human Decision Processes, Elsevier, vol. 131(C), pages 67-80.
    4. Utpal Bhattacharya & Craig W. Holden & Stacey Jacobsen, 2012. "Penny Wise, Dollar Foolish: Buy-Sell Imbalances On and Around Round Numbers," Management Science, INFORMS, vol. 58(2), pages 413-431, February.
    5. Baig , Ahmed & Blau , Ben & Hao, Jie, 2020. "Accounting Information Quality and the Clustering of Stock Prices," American Business Review, Pompea College of Business, University of New Haven, vol. 23(2), pages 182-210, November.
    6. ap Gwilym, Owain & Verousis, Thanos, 2010. "Price clustering and underpricing in the IPO aftermarket," International Review of Financial Analysis, Elsevier, vol. 19(2), pages 89-97, March.
    7. Jen-Chang Liu & Mark Yeats, 2015. "The Anomaly of 28 Days Between the Ex-Dividend and Payment Dates in Taiwanese Stock Markets," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 5(9), pages 1091-1118, September.
    8. Jinyoung Yu & Young‐Chul Kim & Doojin Ryu, 2024. "Left‐digit biases: Individual and institutional investors," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(3), pages 518-532, March.
    9. Ken Chung & Anthony Bellotti, 2021. "Evidence and Behaviour of Support and Resistance Levels in Financial Time Series," Papers 2101.07410, arXiv.org.
    10. Chaudhry, Sajid M. & Bajoori, Elnaz & Nandeibam, Shasi, 2019. "Clustered pricing in the corporate loan market: Theory and empirical evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 275-296.
    11. Das, Sougata & Kadapakkam, Palani-Rajan, 2020. "Machine over Mind? Stock price clustering in the era of algorithmic trading," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    12. Baig, Ahmed S. & Blau, Benjamin M. & Whitby, Ryan J., 2019. "Price clustering and economic freedom: The case of cross-listed securities," Journal of Multinational Financial Management, Elsevier, vol. 50(C), pages 1-12.

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