Algorithmic Trading, Liquidity, and Price Discovery: An Intraday Analysis of the SPI 200 Futures

• Published date: 01 May 2014
• DOI: http://doi.org/10.1111/fire.12034
• Date: 01 May 2014
• Author: Hui Zheng,Tina Viljoen,P. Joakim Westerholm

The Financial Review 49 (2014) 245–270

Algorithmic Trading, Liquidity, and Price

Discovery: An Intraday Analysis of the SPI

200 Futures

Tina Viljoen

The University of Sydney

P. Joakim Westerholm

The University of Sydney

Hui Zheng∗

The University of Sydney

Abstract

We study the intraday price impact of algorithmic trading (AT) on futures markets. We

find that AT exhibits a strong reverse U-shape intraday pattern, and greater AT activity is

related to lower effective spreads, higher realized spreads and lower adverse selection risk,

which suggests that algorithmic traders strategically enter the market when transaction costs

and information asymmetry are lower. AT is associated with an increase in transaction costs

in the subsequent intraday period mainly through an increase in the adverse selection risk, and

is positively related to both public and private information. Our results strongly suggest that

algorithmic traders are informed and contribute to liquidity and price discovery on the futures

markets.

∗Corresponding author: Discipline of Finance, Business School, The University of Sydney, NSW 2006,

Australia; Phone: (61) 2 9351 3915; Fax: (61) 2 9351 6461; E-mail: hui.zheng@sydney.edu.au.

We would like to thank seminar participants at the Universityof Sydney and The Capital Markets Coop-

erative Research Centre for helpful comments. We specially thank Michael Goldstein (the guest editor)

and two anonymous referees for their constructive advice, which has significantly improved this paper.

All errors are the sole responsibility of the authors.

C2014 The Eastern Finance Association 245

246 T. Viljoen et al./The Financial Review 49 (2014) 245–270

Keywords: algorithmic trading, futures markets, market liquidity, price discovery

JEL Classifications: G10, G13, G14

1. Introduction

This study examines the impact of algorithmic trading (AT) on market liquid-

ity and price discovery in the futures markets. With technological innovation and

the transition to electronic trading platforms, the use of computer algorithms has

increased markedly in the past decade, coinciding with increased trading activity

and lower trade size (see Chordia, Roll and Subrahmanyam, 2011). It has been

well documented that price discovery in futures leads spot markets (see, e.g., Har-

ris, 1989; Stoll and Whaley, 1990; Chan, 1992; Huang and Stoll, 1994).1Hence,

it is particularly important to examine the impact of AT on futures markets as

the results will have a direct implication on the efficiency of underlying equities

markets.

The literature generally supports that AT contributes to the market efficiency

of equities markets by increasing liquidity and speeding up price discovery. For

example, Hendershott and Riordan (2013) find that AT is sensitive to the price

of liquidity, demanding liquidity when it is inexpensive, and supplying liquidity

when costs increase. Brogaard, Hendershott and Riordan (2013) examine the role

of high-frequency traders (HFTs) in price discovery and importantly find that HFTs

overall trade in the direction of reducing transitory pricing errors. Hendershott, Jones

and Menkveld (2011, p. 1) conclude that “AT improves liquidity and enhances the

informativenessof quotes.” Kirilenko, Kyle, Samadi and Tuzun (2011) show that HFT

participation on the futures markets has a significant impact on liquidity.Furthermore,

the importance of futures markets for price discovery is well documented in an

extensive body of literature (e.g., Stoll and Whaley, 1990; Tang, Mak and Choi,

1992; Tse, 1995).

This paper contributes to the literature in the following four ways. First, if AT

contributes to price discovery and pricing occurs first in the futures markets, then it

is important to understand the impact of AT on the futures markets. In this paper,

we connect the literature on AT with the literature on price discovery in the futures

markets. Second, we provide direct empirical evidence as to how AT affects liquidity

and price discovery in the Share Price Index (SPI) 200 Futures contracts traded on the

Australian Securities Exchange (ASX). The SPI contract is particularly interesting for

1For the Australian equities index future that we study, the lead–lag association between future and spot

has also been confirmed by Frino, Walter and West (2000). We verify the association between the future

and the spot index for our sample period (these results are available on request).