Stock price discreteness and clustering: decimals and ordered probit model
| DOI | https://doi.org/10.1108/JFRC-07-2012-0025 |
| Date | 04 February 2014 |
| Published date | 04 February 2014 |
| Pages | 49-60 |
| Author | Haksoon Kim |
| Subject Matter | Accounting & Finance,Financial risk/company failure,Financial compliance/regulation |
Stock price discreteness
and clustering: decimals
and ordered probit model
Haksoon Kim
Department of Accounting, Finance and Law, Sorrell College of Business,
Troy University, Montgomery, Alabama, USA
Abstract
Purpose – The purpose of this paper is to revisit the ordered probit model of Hausman et al. after the
NYSE decimalization.
Design/methodology/approach – The changed ordered probit model.
Findings – The model can somewhat capture the different impact of trading-related “explanatory”
variables on price changes among three different decimals but does not explain much about price
discreteness and irregular transaction intervals among the existing models of stock price discreteness.
Overall 1/16th and 1/24th range of the dependent variable is better explained by trading-related
explanatory variables than 1/8th range of the dependent variable for small firms and there is not much
difference in large firms among three decimals. The results imply that finer specification in
decimalization and smaller firm size matters in trading after the decimalization project.
Originality/value – First paper to revisit the ordered probit model of Hausman et al. after the NYSE
decimalization.
Keywords Decimals, Orderedprobit model, Stock price discretenessand clustering
Paper type Research paper
1. Introduction
In August 2000, NYSE announced new decimalization project in their order processing
system, expecting the better understanding of price mechanism and create opportunity
for tighter spreads. The basic idea is replacing fractions with dollars an d cents.
US Securities and Exchange Commission (SEC hereafter) ordered stock markets to
convert from fractions to decimals by April 9, 2001. It was a major regulatory change
in the order processing system, and it was possible from the combined effort of NYSE
and SEC. So, it is interesting to see the relationship between trading-related factors and
stock price discreteness and clustering after the introduction of new decimalization
project. In that way, we better understand the price mechanism under the new
decimalization rule. There are several papers which analyzed the data after the
decimalization of the NYSE order processing system. Graham et al. (2003) analyzed the
daily stock price data after the decimalization and compared them with the one before
the decimalization using ex-dividend pricing to see whether price discreteness and
transactions costs affect stock returns. Cloyd et al. (2004) analyzed the similar
ex-dividend day stock price behavior as Graham et al. (2003) but also included the effect
of equalizing the federal income tax rates on dividend and long-term capital gain income
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1358-1988.htm
The author would like to thank investment seminar participants at the Louisiana State
University in 2005 for helpful comments. The initial project started when the author was at the
Louisiana State University. The author is responsible for all the errors.
Journal of Financial Regulation and
Compliance
Vol. 22 No. 1, 2014
pp. 49-60
qEmerald Group Publishing Limited
1358-1988
DOI 10.1108/JFRC-07-2012-0025
Stock price
discreteness and
clustering
49
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