Trader Leverage and Liquidity

• Published date: 01 August 2017
• Author: BIGE KAHRAMAN,HEATHER E. TOOKES
• Date: 01 August 2017
• DOI: http://doi.org/10.1111/jofi.12507

THE JOURNAL OF FINANCE •VOL. LXXII, NO. 4 •AUGUST 2017

Trader Leverage and Liquidity

BIGE KAHRAMAN and HEATHER E. TOOKES∗

ABSTRACT

Does trader leverage drive equity market liquidity? We use the unique features of

the margin trading system in India to identify a causal relationship between traders’

ability to borrow and a stock’s market liquidity. To quantify the impact of trader

leverage, we employ a regression discontinuity design that exploits threshold rules

that determine a stock’s margin trading eligibility. We find that liquidity is higher

when stocks become eligible for margin trading and that this liquidity enhancement is

driven by margin traders’ contrarian strategies. Consistent with downward liquidity

spirals due to deleveraging, we also find that this effect reverses during crises.

HOW DOES TRADER LEVERAGE impact equity market liquidity? The recent finan-

cial crisis has increased interest in the idea that variation in traders’ ability to

use leverage (that is, the ability of traders to borrow in order to invest in risky

assets) can cause sharp changes in market liquidity. In fact, the assumption

that capital constraints drive market liquidity is central to several influen-

tial theoretical models (see, for example, Gromb and Vayanos (2002), Garleanu

and Pedersen (2007), Brunnermeier and Pedersen (2009), Geanakoplos (2010)).

When traders such as hedge funds act as financial intermediaries and supply

∗Bige Kahraman is from the Oxford Said Business School. Heather E. Tookes is from the Yale

School of Management. We would like to thank Viral Acharya; Ken Ahern; Andrew Ang; Ravi

Anshuman; Nick Barberis; Bo Becker; Bruno Biais (the Editor); Ekkehart Boehmer; Marco Cipri-

ani; Yaxin Duan; Greg Duffee; Andrew Ellul; Thierry Foucault; Francesco Franzoni; Mariassunta

Giannetti; Larry Glosten; William Goetzmann; Jungsuk Han; Florian Heider; Wei Jiang; Charles

Jones; Dmitry Livdan; Albert Menkveld; Paolo Pasquariello; Michael Roberts; Ronnie Sadka; Ku-

mar Venkataraman; Avi Wohl; two anonymous referees; as well as participants at the Federal

Reserve Bank of New York,Tel Aviv University Finance Conference, 2014 SFS Finance Cavalcade,

7th Annual Conference of the Paul WoolleyCentre for the Study of Capital Market Dysfunctionality,

European Winter Finance Conference, European Summer Symposium in Gerzensee, 10th Annual

Central Bank Workshop on the Microstructure of Financial Markets, University of Washington

Summer Finance Conference, and NYU-NSE Conference on Indian Capital Markets; and HEC

Paris, Oxford Said Business School, New York University, Northeastern University, University of

Buffalo, Stockholm University,and the National Stock Exchange of India (NSE) for their valuable

comments and suggestions. We also thank Nirmal Mohanty, Ravi Narain, R. Sundararaman, C. N.

Upadhyay, and staff at the National Stock Exchange of India for providing us with institutional

information. Minhua Wan provided excellent research assistance. The authors acknowledge the

financial support of the 2013–2014 NSE-NYU Stern Initiative on the Study of Indian Capital Mar-

kets. The views expressed in this paper are those of the authors and do not necessarily represent

those of NSE or NYU. We have read the Journal of Finance’s disclosure policy and have no conflicts

of interest to disclose.

DOI: 10.1111/jofi.12507

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1568 The Journal of Finance R

liquidity to markets, frictions related to their ability to obtain leverage can

also impact their ability to supply liquidity. While this idea is theoretically

appealing, testing its validity empirically is challenging, as it requires that

one measure the ability of traders to borrow and then isolate the variation in

leverage that is not caused by the same economic forces that drive variation in

market liquidity. Achieving the latter is particularly problematic if, for exam-

ple, investor selling pressures due to a decline in fundamentals simultaneously

cause a decline in market liquidity and forced deleveraging. This paper exploits

the unique margin trading rules in India to provide causal evidence of the im-

pact of trader leverage on liquidity. Importantly, the analysis sheds light on

the question of when (that is, under what market conditions) trader leverage

is beneficial to market quality and when it is costly.

Indian equity markets provide a particularly useful laboratory for examining

the role of shocks in traders’ ability to borrow. In 2004, Indian regulators

introduced a formal margin trading system that allows traders to borrow in

order to finance their purchases of securities.1As in the United States, under

margin trading in India, investors can borrow up to 50% of the purchase price

of an eligible stock. Thus, the ability to use margin financing relieves capital

constraints and can be considered a positive shock to traders’ ability to borrow.

We exploit two useful features of the system in India: (i) only some exchange-

traded stocks are eligible for margin trading and (ii) the list of eligible stocks

is revised every month and is based on a well-defined eligibility cutoff.

Margin trading eligibility is determined by the average “impact cost,” which

is the estimated price impact of trading a fixed order size. Impact costs are

based on six-month rolling averages of order book snapshots taken at random

intervals in each stock every day. Stocks with measured impact costs of less

than 1% are categorized as Group 1 stocks and are eligible for margin trading.

All remaining stocks are ineligible. The lists of eligible stocks are generated on

a monthly basis, and we are able to observe shocks to the ability of traders to

borrow at the individual stock level.

To identify the causal effect of trader leverage on market liquidity, we employ

a regression discontinuity design (RDD) in which we focus on stocks close to

the eligibility cutoff (see Lee and Lemieux (2010)). At the cutoff of 1%, the

probability of margin trading eligibility jumps from zero to one, which allows

us to employ a “sharp” RDD. We compare the liquidity of stocks that are just

above and just below the cutoff. Because eligibility is revised every month, we

obtain a series of staggered quasi-experiments. This provides important power

for our empirical analysis. We conduct our analysis using two widely used

measures of liquidity: average bid-ask spreads and the price impact of trading.

Our analysis reveals a causal effect of trader leverage on stock market liq-

uidity. In the data, we observe a discontinuous change in both the spread and

the price impact measures at the margin trading eligibility cutoff. Formal tests

confirm that stock market liquidity is significantly higher when stocks become

1The 2004 regulations do not apply to short selling, which has only recently been allowed in

India (for a limited number of stocks). We discuss short selling in more detail in Section I.

Trader Leverage and Liquidity 1569

eligible for margin trading. We conduct placebo analyses in which we repeat

our tests around false cutoffs. Unlike the liquidity patterns at the true cutoff,

we find no evidence of discontinuous jumps in liquidity at the false eligibility

thresholds. This lends further support to the causal interpretation of our find-

ings. Importantly,the finding of liquidity enhancement due to margin trading is

robust to alternative definitions of the local neighborhood around the eligibility

cutoffs as well as to alternative liquidity measures.

Much of the recent literature related to the question of how trader leverage

affects market liquidity focuses on the liquidity dry-ups that are observed dur-

ing crises. Brunnermeier and Pedersen (2009) argue that the deleveraging that

occurs during severe market downturns causes downward price spirals and ex-

acerbates reductions in liquidity. To investigate this idea, we relax the restric-

tion that the effect of Group 1 status is constant across states of the market.

Consistent with the literature (for example, Hameed, Kang, and Viswanathan

(2010)), we find that all stocks experience liquidity declines during severe mar-

ket downturns. Most importantly,we find that this effect is amplified for stocks

that are eligible for margin trading. Thus, there is an important sign change in

the estimated effect of eligibility. While the ability to trade on margin is bene-

ficial to liquidity on average, it becomes harmful during severe downturns. It

is typically very difficult to separate the effects of margin trading from several

other effects taking place in times of market stress (such as panic selling or

increased aggregate uncertainty). Our research design helps to overcome this

empirical obstacle.

Given the evidence of a causal role of leverage on market liquidity, we next

seek to uncover the mechanisms driving the basic results. One unique fea-

ture of our data is that we observe total outstanding margin positions for

each stock at the end of each trading day. We use this information to ana-

lyze patterns in margin traders’ trading strategies at the daily frequency. We

find that margin traders provide liquidity by following contrarian strategies:

changes in margin trading positions are negatively related to stock returns.

This contrarian trading behavior competes away returns to reversal strate-

gies for margin-eligible stocks. We also find that improvements in liquidity are

higher when margin traders are more active. While margin traders are liquidity

providers on average, this role completely reverses and they become liquidity

seekers during severe market downturns. As in the liquidity analysis, the mar-

gin trading results reveal both the benefits and the costs associated with trader

leverage.

Although the intricate relationships between the ability of traders to obtain

funding (“funding constraints”) and asset prices have long been recognized in

the literature (see, for example, Kiyotaki and Moore (1997), Kyle and Xiong

(2001), Gromb and Vayanos (2002), Krishnamurthy (2003)), there is a grow-

ing interest in improving our understanding of these linkages in the after-

math of the recent global financial crisis. Recent theoretical models such as

Garleanu and Pedersen (2007), Brunnermeier and Pedersen (2009), and Fos-

tel and Geanakoplos (2012) provide several new insights into the dynamics

of funding constraints and the feedback mechanisms that they may trigger.