Option trading and the cross‐listed stock returns: Evidence from Chinese A–H shares

• Author: Qi Xu,Xiaoli Yu,Xingguo Luo,Shihua Qin
• Date: 01 November 2020
• DOI: http://doi.org/10.1002/fut.22108
• Published date: 01 November 2020

J Futures Markets. 2020;40:1665–1690. wileyonlinelibrary.com/journal/fut © 2020 Wiley Periodicals, Inc.

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1665

Received: 5 February 2020

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Accepted: 8 February 2020

DOI: 10.1002/fut.22108

RESEARCH ARTICLE

Option trading and the cross‐listed stock returns: Evidence

from Chinese A–H shares

Xingguo Luo

1

|Xiaoli Yu

2

|Shihua Qin

3

|Qi Xu

1

1

School of Economics and Academy of

Financial Research, Zhejiang University,

Hangzhou, China

2

School of Economics, Zhejiang

University, Hangzhou, China

3

Faculty of Business and Economics, The

University of Hong Kong, Hong Kong

SAR, China

Correspondence

Qi Xu, School of Economics and Academy

of Financial Research, Zhejiang

University, 310027 Hangzhou, China.

Email: qixu@zju.edu.cn.

Funding information

National Natural Science Foundation of

China, Grant/Award Number: 71771199;

Zhejiang University; Fundamental

Research Funds for the Central

Universities; Academy of Financial

Research, Zhejiang University

Abstract

We empirically investigate the effects of option trading on the cross‐listed stock

returns. Using dual‐listed stocks in mainland China (A) and Hong Kong (H)

stock exchanges, we show that option order imbalance (OI) positively and sig-

nificantly predicts daily stock returns for both markets, controlling for risk

factors and firm characteristics. Informed trading rather than price pressure

better explain thepredictability. High OI stocks have higher tradingvolume and

present lottery‐like properties. Three important events significantly affect the

predictive power of OI, consistent with the improved market quality and the

episode of speculative trading. Robustness checks support the main findings.

KEYWORDS

cross‐listing, option trading, order flow

JEL CLASSIFICATION

G12; G13; G14

1|INTRODUCTION

Options contain forward‐looking information. As suggested by Ross (1976), options are nonredundant securities in

incomplete markets and can improve the allocation and information efficiency. With the rapid growth of derivatives

markets and the increasing use of options around the world over the past four decades, a number of studies explore the

information content of options. One strand of literature examines the information content of option prices.

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Another

stream of studies, however, focuses on the role of option quantities or trading activities.

The theoretical foundation about the importance of option trading information can be dated back to Black (1975). He

suggests that informed traders may prefer to use the option due to its higher leverage. Easley, O'Hara and Srinivas (1998)

further show theoretically that option order flow can be informative about the underlying stock fundamentals, if informed

traders indeed trade in the option market. Ni, Pearson, and Poteshman (2005), Pan and Poteshman (2006), Roll, Schwartz,

and Subrahmanyam (2010), Johnson and So (2012), Hu (2014), and Ge, Lin, and Pearson (2016) among others empirically

show that various forms of option trading information can predict the cross‐section of the underlying stock returns.

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These studies focus almost exclusively on using option trading information to predict its underlying stock returns. However,

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These studies include but not limit to the use of information about option implied volatility/volatility smile/volatility spread (Bali & Hovakimian, 2009; Cremers & Weinbaum, 2010; Xing, Zhang, &

Zhao, 2010), volatility term structure (Bakshi, Panayotov, & Skoulakis, 2011; Johnson, 2017), option price quotes (Muravyev, Pearson, & Broussard, 2013), as well as variance risk premium (Bollerslev,

Tauchen, & Zhou, 2009), and a number of extension studies.

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Another related strand of literature focus on the effects of option trading on corporate activities. These studies include Roll, Schwartz, and Subrahmanyam (2009), Blanco and Wehrheim (2017), and

Cao, Hertzel, Xu, and Zhan (2019). Instead, we still focus on the return predictive powers of option trading.

it is less clear whether the information embedded in option trading may also spill over to other markets beyond the

underlying stock.

Our main research question is whether option trading information predicts the cross‐listed stock returns. Cross‐

listed stocks are stocks with the same underlying company but are traded in different markets. Therefore, their prices

are expected to reflect the value of the same claim for the firm's future cash flow. A natural question is why cross‐listed

stocks are particularly relevant when examining the predictive information of option trading. Cross‐listing offers us an

ideal venue to understand the potential informed trading in option markets. If option traders indeed obtain important

private information about the firm's fundamentals and try to leverage up their profits using options, their trading

information may also predict the cross‐listed stock returns, given the same underlying firm fundamentals. Instead, if

the effects of option trading on stock returns are mainly due to temporary price pressures, then option trading may not

necessarily affect cross‐listed stocks in a different market. The idea is consistent with Gagnon and Karolyi (2009). They

suggest that cross‐listed stocks allow to separate firm‐specific from aggregate price changes and hence enable more

precise measurement of information asymmetry and international spillover.

Different from existing cross‐listing studies (e.g., Fernandes & Ferreira, 2008; Muscarella & Vetsuypens, 1996),

which mainly use American Depositary Receipt (ADR), our empirical analysis focuses on stocks dual‐listed in

Shanghai/Shenzhen (A‐share) and Hong Kong (H‐share) stock exchanges. From 1993, Chinese companies are able to

cross‐list A‐shares in mainland China (on the Shanghai Stock Exchange (SSE) or the Shenzhen Stock Exchange (SZSE))

and H‐shares on the Hong Kong Exchanges and Clearing Limited (HKEX) simultaneously or with some lags. This

cross‐listing setting is unique in that before 2014, mainland Chinese investors have full access to A‐shares but no access

to H‐shares, while Hong Kong investors and foreign investors can only trade H‐shares but not A‐shares. On November

17, 2014, the Chinese government officially permitted the Shanghai–Hong Kong Connect, making the mutual trading of

a list of leading stocks (including all the AH cross‐listed shares) in the two markets available.

The focus on A–Hcross‐listed stocks provides us a unique empirical setting to understand our main research question

from at least two aspects. Initially, individual stock options do not exist for stocks listed in Shanghai and Shenzhen stock

exchanges, but options on stocks listed in Hong Kong stock exchanges are available. Therefore, we can test whether option

trading in one market (Hong Kong) can predict stock returns in another market (mainland China), where individual stock

options are not available. This is important not only because we provide the first set of evidence on whether option trading

can predict individual stock returns in China, but may also contribute to better understand potential mechanisms. Hu (2014)

suggests that option trading may affect stock returns due to the delta hedging activities by option market makers. The

potential predictive power of H‐share options on A‐share returns may cast doubt on this channel in our case. The absence of

individual equity options in mainland China and the segmentation of the A–H stock markets before the 2014 Shanghai–

Hong Kong Connect imply that potential option market makers in Hong Kong can only delta hedge with H‐shares but not

A‐shares due to the segmentation, hence can hardly affect cross‐listed stocks directly through trading A‐shares.

Second, the launch of the 2014 connect links the two markets. Therefore, we expect to observe changes in the

potential predictive power of option trading information for stock returns in these two markets, given the changes of

clienteles, risk preferences, and even market structures. We can, therefore, understand better the potential drivers of the

predictability. Previous studies like Sohn and Jiang (2016) and Aitken, Ji, Mollica, and Wang (2017) mainly focus on the

price discovery and market liquidity by exploring AH cross‐listed stocks and the Shanghai–Hong Kong Connect. We

instead highlight the informed trading and the role of option trading on this issue.

We use the information about option order flow to measure option market activities. Specifically, we focus on the option

order flow imbalance (order imbalance or OI hereafter).

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Our empirical findings lend strong support to our main prediction:

that is option OI predicts future stock returns. By sorting stocks into five portfolios according to their OIs, we show that

stocks in the highest OI portfolios significantly outperform stocks in the lowest OI portfolios. A daily rebalanced long–short

strategy earns an excess return around 10 basis points per day (or about 25% annually) for both A and H stocks.

4

Results hold

for both equal‐weighted and value‐weighted portfolios. Controlling for different systematic risk factors does not affect our

main findings qualitatively. We also control for a set of firm, stock, and option level characteristics in panel regressions. Our

findings confirm that OI positively predicts A and H stock returns even account for these alternative predictors.

Why option OI may predict stock returns? We consider two potential explanations: that is informed trading and

price pressure. Initially, following the theoretical literature such as Easley et al. (1998), informed traders may prefer to

3

Hu (2014) considers a different version of option OI based on the component of stock OI due to option trading component. Instead, we consider a direct and commonly used measure of OI to measure

option trading activities. We also consider alternative option trading activity measures such as option to stock volume ratio (OS), however, results are unclear.

4

The annualized return looks large, but is comparable to the long–short return of 8.76 bp per day (or 22% annually) for OI‐sorted portfolios with US data documented by Hu (2014).

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LUO ET AL.