Tick size and price efficiency: Further evidence from the Tick Size Pilot Program

• Published date: 01 September 2023
• Author: Kee H. Chung,Chairat Chuwonganant
• Date: 01 September 2023
• DOI: http://doi.org/10.1111/fima.12419

DOI: 10.1111/fima.12419

ORIGINAL ARTICLE

Tick size and price efficiency: Further evidence

from the Tick Size Pilot Program

Kee H.Chung1,2Chairat Chuwonganant3

1School of Management, University at Buffalo,

The State University of New York(SUNY),

Buffalo, New York,USA

2SKK Business School, Sungkyunkwan

University, Seoul, Republic of Korea

3College of Business Administration, Kansas

State University, Manhattan, Kansas, USA

Correspondence

KeeH. Chung, Department of Finance, School

of Management, University at Buffalo, The

State University of New York(SUNY), Buffalo,

NY 14260, USA.

Email: keechung@buffalo.edu

Abstract

This paper examines whether larger tick sizes improve or

hinder price efficiency for small-capitalization stocks using

data from implementing and terminating the Tick Size Pilot

Program (TSPP). We show that the TSPP led to increases

in various liquidity measures, and its termination restored

them to their pre-TSPPlevels. We also find evidence that the

TSPP led to trader migration from the pilot to control stocks.

The TSPP implementation (termination) is associated with

decreases (increases) in price efficiency,indicating that price

efficiency decreases with tick sizes. Liquidity and informed

trading are two channels through which the TSPP changes

price efficiency.

KEYWORDS

informed trading, liquidity, pilot program, price efficiency, tick size,

trader migration

JEL CLASSIFICATION

G10, G14, G18

1INTRODUCTION

Numerous studies have examined the effect of tick sizes on price efficiency.1Bycontrast, there is limited empirical

evidence on how tick sizes affect price efficiency.Prior research suggests that price efficiency increases with informed

trading and liquidity (Chordiaet al., 2008; Easley et al., 1997;Kyle,1985).The Tick Size Pilot Program (TSPP) provides

1Chordiaet al. (2008), Hu et al. (2018), Albuquerque et al. (2020), and Kerr et al. (2020) show that smaller tick sizes increase market efficiency and price infor-

mativeness. In contrast,Comerton-Forde et al. (2019) and Chung et al. (2020) report that an increase in the quote tick size is associated with an increase in

priceefficiency. Lee and Watts (2021) suggest that an increase in tick sizes deters algorithmic trading and improves price efficiency byincreasing information

acquisitionby fundamental investors.

© 2023 Financial Management Association International.

Financial Management. 2023;52:483–511. wileyonlinelibrary.com/journal/fima 483

484 CHUNG ANDCHUWONGANANT

an excellent opportunity to shed additional light on the channels through which prices become efficient because the

TSPP led to exogenouschanges in liquidity and trading. We use the implementation (i.e., an increase in tick sizes) and

termination (i.e., a decrease in tick sizes) of the TSPP as instrumental variables to examine whether changes in price

efficiency could be explained byconcurrent changes in liquidity and informed trading for small-capitalization stocks.2

Prices become informationally efficient when investors acquire value-relevant information and incorporate the

informationin prices through trading. A large tick size increases the cost of front-running and thus motivates traders to

acquire information, which, in turn, could increase price efficiency (Harris, 1997, 2003).3However,a large tick size also

increases the cost of trading and therefore discourages informed trading, reducing price efficiency.4Consequently,

whether a larger tick size increases or decreases price efficiency will likely depend on the relative strength of these

opposing forces. Tothe extent that the relative strength of these forces varies with the market share of algorithmic

trading,5it is not entirely clear whether larger tick sizes improve or hinder price efficiency in markets dominated by

algorithmic trading.6

Prior studies of the TSPP makeinferences regarding the effects of tick sizes on price efficiency, liquidity,and trading

using data from its implementation.7If larger tick sizes indeed caused changes in these variables, they would return

to their pre-TSPP levels after the programtermination. We assess the robustness of these inferences by looking at

whether its ending has opposite effects on these metrics.

Wefirst confirm the finding of prior research (e.g., Chung et al., 2020; Comerton-Forde et al., 2019; Rindi & Werner,

2019) that the implementation of the TSPP led to significant increases in the quoted, effective, and realized spreads,

quoted depths, and the price impact of trades for the pilot stocks. We complement this finding by providingnew evi-

dence that the termination of the TSPP restored these measures to their pre-TSPPlevels. The TSPP is also associated

with a decrease in liquidity for the control stocks. This indirect effect of the TSPP on the control stocks remains even

after the termination of the TSPP.

We show that the TSPP led to significant decreases invarious trading measures, order imbalance, and the probabil-

ity of informed trading (PIN) for the pilot stocks, while its termination led to increases in these measures. By contrast,

the TSPP is associated with an increase in trading, order imbalance, and PIN for the control stocks, suggesting that

the TSPP led to a migration of traders from the pilot stocks (low-liquidity stocks) to the control stocks (high-liquidity

stocks). We show that the termination of the TSPP did not reverse its effect on trading, order imbalance, and PIN for

the control stocks, suggesting that the trader migrationwas not a short-lived phenomenon.

We show that the TSPP is associated with an increase in variance ratios, while its termination led to a decrease in

varianceratios, suggesting that large tick sizes reduce price efficiency. Toexplore how the TSPP affects price efficiency,

we conduct the two-stage least squares (2SLS) regression analysis using the implementation and termination of the

TSPP as instrumental variables. Using the predicted values of various measures of liquidity and informed tradingfrom

2Coxet al. (2022) explore whether the effects of stock splits on retail trading vary with the tick size using stocks in the TSPP. The authors show that the tick

sizedoes not influence the relation between the two variables.

3Butiet al. (2022) find that the dark pool market share for large-capitalization stocks is higher when the book is more competitive using the 2009 data. They

interpret the result as evidencethat the ability of dark pool traders to front-run standing limit orders helps increase their market share, especially when the

relativetick size is large.

4Anshumanand Kalay (1998) suggest that a large tick size imposes large transaction costs on traders, reducing the value of private information. Their model

predicts an increase in information-based trading after decimalization because informed traders have greater incentives to collect and acquire accurate

signalsunder decimal pricing.

5Weller (2018) analyzes the effect of algorithmic trading on price efficiency.The author finds evidence that although algorithmic trading enhances price

efficiency byreflecting public information revealed by other sources, it reduces price efficiency by discouraging the acquisition of new information. Chordia

andMiao(2020)show that low-latency traders trade aggressively at the time of the earnings announcements, resulting in faster incorporation of earnings

surprisesin prices and smaller post-earnings-announcement drifts.

6Algorithmic tradingaccounted for 60%–73% of allU.S.equity trading in 2018 (source: https://www.businesswire.com/news/home/20190205005634/en/

Global-Algorithmic-Trading-Market-to-Surpass-US-21685.53-Million-by-2026#::text=Algorithmic%20trading%20is%20responsible%20for,the%20U.S.

%20gross%20domestic%20product).

7See,for example, Hu et al. (2018), Comerton-Forde et al. (2019), Rindi and Werner (2019), Albuquerque et al. (2020), Chung et al. (2020), and Lee and Watts

(2021).