What’s Not There: The Odd-Lot Bias in TAQ Data

47 Pages Posted: 23 Jul 2011 Last revised: 26 Dec 2019

See all articles by Maureen O'Hara

Maureen O'Hara

Cornell University - Samuel Curtis Johnson Graduate School of Management

Chen Yao

The Chinese University of Hong Kong (CUHK) - CUHK Business School

Mao Ye

University of Illinois at Urbana-Champaign; National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: July 22, 2011

Abstract

We investigate the systematic bias that arises from the exclusion of trades for less than 100 shares from TAQ data. In our sample, we find that the median number of missing trades per stock is 19%, but for some stocks missing trades are as high as 66% of total transactions. Missing trades are more pervasive for stocks with higher prices, lower liquidity, higher levels of information asymmetry and when volatility is low. We show that odd lot trades contribute 30 % of price discovery and trades of 100 shares contribute another 50%, consistent with informed traders splitting orders into odd-lots and smaller trade sizes. The truncation of odd-lot trades leads to a significant bias for empirical measures such as order imbalance, challenges the literature using trade size to proxy individual trades, and biases measures of individual sentiment. Because odd-lot trades are more likely to arise from high frequency traders, we argue their exclusion from TAQ and the consolidated tape raises important regulatory issues.

Keywords: TAQ data, Odd-lots, Price Discovery, Transparency, Order Imbalance, Retail Trading

JEL Classification: G10, G14

Suggested Citation

O'Hara, Maureen and Yao, Chen and Ye, Mao, What’s Not There: The Odd-Lot Bias in TAQ Data (July 22, 2011). Johnson School Research Paper Series No. 31-2011, Midwest Finance Association 2012 Annual Meetings Paper, WBS Finance Group Research Paper No. 167, Available at SSRN: https://ssrn.com/abstract=1892972 or http://dx.doi.org/10.2139/ssrn.1892972

Maureen O'Hara (Contact Author)

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

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Chen Yao

The Chinese University of Hong Kong (CUHK) - CUHK Business School ( email )

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Hong Kong
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HOME PAGE: http://sites.google.com/site/chenyaosite/research

Mao Ye

University of Illinois at Urbana-Champaign ( email )

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National Bureau of Economic Research (NBER) ( email )

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