Liquidity and traders' behaviors in financial markets

HARVARD UNIVERSITY
Graduate School of Arts and Sciences
DISSERTATION ACCEPTANCE CERTIFICATE
The undersigned, appointed by the Committee for the PhD in Business
Economics
have examined a dissertation entitled
Liquidity and Traders' Behavior in Financial Markets
presented by Laura Elena Serban
candidate for the degree of Doctor of Philosophy and hereby
certify that it is worthy of acceptance.
Signature
-"">
H—_
^Xj~zkA
John
Y.
Campbell, Chair
Signature xX^/
Signature
Shawn Cole
f
Erik Stafford
Date: £ / »*/1 0
Liquidity and Traders' Behavior in Financial Markets
A dissertation presented by
Laura Elena Serban
to
The Committee for the PhD in Business Economics
in partial fulfillment of the requirements
for the degree of

Doctor of Philosophy
in the subject of
Business Economics
Harvard University
Cambridge, Massachusetts
September 2010
UMI Number: 3435462
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Dissertation Advisors: John Y. Campbell Laura Elena Serban
Erik Stafford, Shawn A. Cole

Liquidity and Traders' Behavior in Financial Markets
ABSTRACT
This thesis consists of three essays on the liquidity characteristics and traders'
behavior in the main market for agricultural commodity futures in India, the Na-
tional Commodity and Derivatives Exchange. This electronic trading platform was
launched at the end of 2003 and subsequently became the third largest agricultural
futures market globally.
The first essay estimates the impact of speculators' capital constraints on their
willingness to provide liquidity as measured by trade participation, and on overall
market liquidity as measured by bid-ask spread. To overcome the standard identifi-
cation problem, the study exploits exogenous variation in trading performance in the
form of losses in one asset unrelated to the fundamentals of another asset. The study
finds that a small number of traders accounts for an overwhelming share of trading
activity and participate in the market for a large number of commodities. Consis-
tent with theoretical predictions, a negative shock to these active traders' aggregate
capital causes an increase in future bid-ask spread, but the economic magnitude of
the estimated effect is small. Changes in competition to provide liquidity explain a
considerable fraction of the variation in subsequent market liquidity. The effect is
non-linear: the bid-ask spread is smallest around a natural level of competition, but
increases as competition intensity deviates away from this point.
iii
Using the same dataset, the second essay investigates sources of traders' superior
returns in local commodities. Investors bias their portfolios towards local commodi-
ties,
crops that are differentially grown within lookm of their location, and earn
returns in these commodities that are 3.2% higher than in their non-local commodi-
ties,
even amongst traders who turnover positions frequently. This differential is
greatest in crops that are weather sensitive and for which India has a high percent-
age of world production. The results are consistent with traders possessing superior

domestic supply information on local commodities because their proximity to crop
production causes information acquisition costs to be lower.
The third essay analyzes the trading decisions and performance of all three trader
categories - individuals, brokers, and commercial institutions - participating in agri-
cultural commodity markets in India. In contrast to
U.S.
commodity markets, individ-
uals represent about 80% of participants by number, and contribute between 40-50%
of trading activity and open interest in the market. Client commercial institutions
account for less than 5% of overall trading activity, but for up to 35% of open interest;
although fewest by number, broker proprietary trading desks account for a large por-
tion of trading activity. Brokers are the most active group in spread strategies, while
both brokers and individuals engage frequently in day-trading activities. Broker pro-
prietary accounts are highly diversified across commodities trading 14 commodities
on average, compared to about 4 traded by the other types. In aggregate, brokers
make the largest amount of profits, and they do so consistently over time. The mean
broker account's profits from both intra-day and overnight profits is almost 40 to 60
times larger than the corresponding profits obtained by the mean client institution or
individual. In contrast, individuals lose significant amounts of money. Trading ac-
tivity, open interest and profitability are concentrated within each market participant
iv
group.
This study also analyzes the impact of market-wide characteristics, and beyond that,
the impact of peer actions and outcomes on individuals' decisions to enter into com-
modities futures market. Aggregate entry rates of both individuals and companies in
the commodity futures market are positively serially correlated, and increasing with
trading volume and commodity market returns. The actions and market outcomes of
local peers affect entry decisions. The number of new individual traders in a zip-code
is highly positively serially correlated, and zip-codes with more active participants ex-
perience higher entry rates in the future. Moreover, the recent returns of individual

traders in a zip-code are positively correlated with the future number of individual
entries in that zip-code; the influence of peer returns is restricted to situations when
neighbors experience negative returns. Our findings suggest that information about
negative peer performance is more likely to spread among individuals than informa-
tion about positive peer performance, or that the individuals in our sample react only
to learning about negative peer returns.
v
Table of Contents
Abstract iii
Acknowledgments viii
i. Active
Traders,
Capital and Liquidity 1
1.1 Introduction 1
1.2 Institutional Context and Data 10
1.3 Identification and Characteristics of Liquidity Providers in an Order
Driven Market 13
1.4 Measuring Liquidity and Capital Availability 24
1.4.1 Liquidity Measures 24
1.4.2 Exogenous Capital Shocks as Trading Revenue in Fundamen-
tally Unrelated Commodities 28
1.4.3 Intra-day Round Trip versus Overnight Inventory Related Rev-
enues 30
1.5 Active Traders' Aggregate Revenues and Individual Security Liquidity 33
1.6 Active Traders' Competition and Individual Security Liquidity 50
1.7 Robustness JJ
1.8 Active Traders' Revenues and Participation Decisions j]
1.9 Conclusion 68
2.
What

are Local Traders
Informed About and When? Evidence from Agricultural Com-
modity Futures 70
2.1 Introduction 70
2.2 Data and Descriptive Statistics yy
2.3 Local Bias 85
2.3.1 Measures of Locality 85
2.3.2 Local Bias 87
2.4 Local Performance 92
2.4.1 Measurement of Returns and Capital 93
2.4.2 Performance for Local Traders 94
2.4.3 Performance and Information on Domestic Supply Shocks 99
VI
2.5 Discussion 108
2.6 Conclusion 110
3.
The Trading Decisions and Performance of Various Investor Types: an Anatomy of a
Large Commodity Futures Market 112
3.1 Introduction 112
3.2 Literature Review 119
3.3 Data and Market Rules 128
3.3.1 NCDEX Market Rules . 130
3.3.2 India's Commodity Markets: World Placement and Brief History 132
3.4 Market Participants' Types and Characteristics 136
3.5 Trading Activity over Time 145
3.6 Share of Trading Activity of Aggregate Trader Type 150
3.7 Performance of Aggregate Trader Types 157
3.8 The Trading Strategies of Aggregate Trader Types 163
3.8.1 Attrition Levels 164
3.8.2 Common Futures Trading Strategies 168

3.9 Trading Activity Concentration by Trader Type 173
3.10 Heterogeneity of Trading Activity and Performance by Trader Type . . 181
3.11 Market-Wide and Neighborhood Determinants of Entry Decisions . . . 190
3.12 Conclusion 207
Appendix 211
A.
Appendix to Chapter 1 2x2
A.i NCDEX Trading Platform 212
A.2 Trading Revenue Decomposition: Intra-Day Round-Trip Trades vs Overnight
Inventory 215
B.
Appendix to Chapter 3 219
Vll
ACKNOWLEDGMENTS
This thesis owes
its
existence
to the
help, support,
and
advice
of
many people.
My
deepest gratitude goes
to the
chair
of my
thesis committee,
Prof.

John
Y.
Campbell,
for
his patient guidance, encouragement
and
excellent advice throughout this study.
His
thoroughness, rigor, efficiency, wealth
and
generosity
of
thought will always
be an
inspiration
for me. I am
forever indebted
to my
long-standing advisor,
Prof.
Shawn
Cole,
for his
constant support
and for his
unflinching confidence
in my
ability
to
succeed!

I
have learned
a
great deal from
our
frequent research discussions where
he
pushed
me to be
refine
my
ideas, improve
my
analysis, clarify
my
interpretations,
and
always work harder.
The
generosity
of his
time,
as
well
as the
constructive comments
in
the
final stages
of

this thesis will never
be
forgotten;
his
down-to-earth attitude
and positive energy
are a
lesson
for
life.
I was
also lucky
to
benefit from
Prof.
Erik
Stafford's advice.
His
imagination, high research standards, insightful comments,
and writing advice have helped
me to
become
a
more creative
and
sophisticated
researcher.
I
have also benefited greatly from discussion
and

advice from
Prof.
Jeremy
Stein,
Prof.
Josh Coval,
and Prof.
Robin Greenwood.
I am
grateful
for
their insightful
suggestions
on the
various topics covered
in
this thesis,
as
well
as for
pushing
me to
think
of
relevant, hard
and
out-of-the-box research questions.
My academic life
was
also shaped

by
wonderful teachers
and
mentors.
I was
fortunate
to
have
the
opportunity
to
talk extensively with
my
general examiners
Prof.
Alvin Roth
and Prof.
David Parkes,
as
well
as
with Susan Athey. They
all
taught
me
about
the
relevance
of
models

and
mathematics
in the
real world,
and
have kindled
a
passion
for
market design,
a
field
to
which
I
hope
to
contribute some day.
I
would also
viii
like to acknowledge Gary Chamberlain and Jim Stock from whom I learned statistics
and econometrics. I would have never started a Ph.D. in Economics without the
inspirational advice of
Prof.
David Laibson, who guided my first steps in economics
research.
This thesis would have taken a very different shape without my colleague and co-
authors, Stefan Hunt, who generously introduced me to the dataset on commodity
futures on which the results in this thesis rely, and to the management team at the

National Commodity and Derivatives Exchange in India, which whom he had devel-
oped a prior relationship. I am thankful for our numerous and long discussions that
sharpened my thinking and iorced me to become more organized, as well as for his
help with my presentation skills! Despite some rough times in our working relation-
ship,
our co-authorship has been an invaluable experience for me! My colleagues Erik
Budish, Daniel Carvalho, Paul Niehaus, Thomas Mertens, Soojin Yim, Tarek Hassan,
Justin Ho, Itay Fainmesser, Elias Albagli, Fuhito Kojima, and Mihai Manea have also
provided inspiration and a pleasant work environment.
I am also indebted to the National Commodity and Derivatives Exchange for
their support and assistance in obtaining the data set used in this thesis, and for
sharing their extensive experience and insights with me. Special thanks are due to
the Ramalinga Ramasehsan, Jagdish Choudhry, Anand Iyer, Somesh Vaidya, Ankur
Garg, Raj Benahalkar, Nirmalendu Jajodia, Uma Mohan, Ravinder Sachdev, and Sid-
dharth Surana. I would also like to acknowledge financial support from the South
Asia Initiative Fellowship and the Warburg Research Funds, as well as from the Har-
vard Business School Doctoral Programs Office. Special thanks are due to Janice
McCormick, John Korn, Debra Hoss, LuAnn Langan and Jennifer Mucciarone for
their care, promptness, and efficiency in running the program.
IX
I could not imagine my life in Cambridge and Boston without my Romanian
friends. A few words cannot summarize the impact they have had on my life. I would
like to thank Tatiana Truhanov for her optimism and resourcefulness; Cristina Bucur
for her kind care; Florin Morosan for his tireless advice, insightful discussions, and
wonderful cooking; Alex Salcianu and Emanuel Stoica for always sharing the depth
and wealth of their knowledge, and for perfectly organized MIT events, especially
the Romanian parties; Andreea Balan-Cohen and Charles Cohen for their original
entertainment recommendations; Florin Albeanu for many wonderful coffee breaks;
and Mihaela Enachescu for her long and always supportive friendship. I am most
grateful to Emma Voinescu and Crisii Jitianu for their unquestioning and consistent

support when I needed it most, and for all the great times we spent together! I am
also grateful to my boyfriend, Dan Iancu, for being next to me throughout my Ph.D.
journey. His perseverance, passion for detail, and kindness have certainly made me
a better person! I will always carry with me his inspired and perfectly timed gifts,
including my favorite Starbucks bears, and cherish the memories we built together
over the last five years.
I would have never been able to complete this Ph.D. without my family. Their
tireless love, unconditional support, and constant encouragement have kept me afloat
through the toughest times of my life. I owe my direction in life to my father, who has
taught me to ask questions, to insist on finding a solution to any problem, to persevere
in spite of any difficulties, and to always believe in
myself!
I have always tried to
emulate his unbounded optimism, as well as his passion for work and research. I
am grateful to my mother for her immense love and care, and for never tiring in
trying to make me a better person. Her kind advice has prevented me from making
many mistakes! My sister has always been there for me for the good as well as the
x
bad, and I could not imagine a better sibling! Her thoughtfulness, reserved manner,
and refined taste have always been great resources for me. I am also grateful to my
brother-in-law for his optimism and humor, and to my cutest baby nephew, who has
truly enlightened our lives ever since he came into this world! This thesis is dedicated
to my beautiful family!
XI
i. Active Traders, Capital and Liquidity
1.1 Introduction
Many asset classes exhibit significant cross-sectional and time-series variation in
liquidity. Understanding the causes of variation in liquidity is important for a num-
ber of reasons. First, according to recent extensions of the capital asset pricing model,
liquidity risk is a systematic determinant of asset prices. Second, liquidity is impor-

tant for our understanding of how traders affect asset prices. Third, efficient trading
requires liquid markets; thus, understanding what determines liquidity is crucial to
the design of financial markets.
While there is significant literature on the cross-sectional determinants of liquid-
ity (Stoll, 2000, 2003), the time-series variation in liquidity received less attention. Yet,
there is considerable daily variation in the liquidity of a security when measured
by bid-ask spread, market depth, price impact, or price reversals. Moreover, recent
studies have suggested that there are common liquidity components within an asset
class (e.g., stocks) as well as across asset classes (e.g., stocks and bonds). What are
the sources of high-frequency variation in liquidity? To what extent are these due
to changes in adverse selection costs faced by liquidity providers, shocks to their ag-
gregate capital, or related variation in the intensity of their competition? Are there
liquidity spillovers due to market-making arbitrageurs trading across multiple secu-
rities?
1
In this paper, I attempt to provide answers to these questions using a unique
dataset and an innovative empirical strategy. The data consist of the entire history of
trader-identified order and transaction activity records along with institutional types
of all participants on the National Commodity and Derivatives Exchange (NCDEX)
of India, globally the third largest exchange for agricultural commodity futures from
inception on the 15th of December 2003 to the end of the first quarter in 2008. This
data-set is particularly suited for my research questions for several reasons. First,
the NCDEX trading platform is a fully electronic limit order book allowing for the
computation of comprehensive liquidity measures. Second, over the sample period,
close to 85% of trading activity in commodity futures in India occurred on NCDEX
providing a centralized, rather than a fragmented picture of liquidity. Third, there are
no designated market makers on NCDEX, a feature common across the majority of
financial exchanges nowadays, allowing me to investigate the questions of interest in
a general setting. Fourth, India is an environment where trading capital resources are
likely scarce on a regular basis: NCDEX is a young trading platform in a developing

country where banks, and institutional and foreign traders are restricted from trad-
ing. Most importantly, the combination of the large variety of commodities traded
on NCDEX with the existence of multi-commodity high-frequency traders allows for
an innovative empirical design that isolates relatively exogenous shocks to trading
capital.
Traditional models useful for the characterization of liquidity point to two main
sources. The first relates to asymmetric information and suggests that liquidity
providers should rationally shade quoted prices to account for the probability of
trading with a better informed trader (Kyle, 1985; Glosten and Milgrom, 1985). The
second relates to either inventory holding costs and risks in dealer markets (Ho and
2
Stoll, 1983) or to waiting costs and risks in the execution of limit orders (Rosu, 2005)
in order-driven markets. These models assume that market makers' trading budgets
are infinite. Recent work by Brunnermeier and Pedersen (2007) focuses on specula-
tors'
capital availability and shows that when capital constraints are tight - because of
higher margins or trading losses - speculators reduce positions and market liquidity
declines. Orthogonally, a number of models emphasize that the level of competi-
tion among liquidity providers is causally related to market liquidity. Grossman and
Miller (1988) show that because market makers face fixed costs of monitoring and
maintaining a presence in the market and aggregate profits from liquidity provision
are bounded, there is an optimal number of such traders. Their model implies that
deviations away this point may cause a decrease in liquidity. Chacko, Jurek, and
Stafford (2008) find that a simple imperfect-competition (and full-information) model
of market-making is able to fit a wide range of features of real-word transaction costs.
Transaction costs are modeled as the rents that a quasi-monopolistic market maker
extracts from impatient investors who trade via limit orders. Importantly, the magni-
tude of these rents depends on the competition from opposing order flow.
In this paper, I use a novel empirical strategy to investigate the extent to which
speculators' capital constraints and the intensity of their competition cause time-series

variation in liquidity. First, although, in limit order markets, no trader has an affirma-
tive commitment to provide an option to trade at all times, the natural market-maker
candidates are participants who trade frequently and persistently - I refer to these
traders as active. If market-making arbitrageurs arise naturally, they almost surely
fall within this group.
I find that a small fraction - close to 4% (about 7,400 traders) - of all traders on
NCDEX account for slightly more than 60% of the average daily traded volume. I
3
characterize the portfolios, institutional features, trading behavior and performance
of active traders. Broadly, I find that the median active trader participates in the mar-
ket for about 15 commodities and at least two different commodity types. The mean
active trader is classified as such in at least 2 commodities with some being active in
as many as 33 distinct commodities. About 54% of broker proprietary accounts and
11%
of institutional clients are active traders, while only 4% of individual traders are
so.
Active traders perform particularly well in commodities in which they are classi-
fied as such, while losing especially on open positions in other commodities held in
their portfolio.
I proxy for changes in speculators' wealth by trading revenues on the NCDEX
trading platform; specifically, I am able to precisely reconstruct the history of profit
and losses of each trader in all commodities in which he participates. However, using
changes in capital from trading in a certain commodity to identify the link between
capital and the liquidity of that commodity is problematic. For example, suppose I
observe that a wheat trader who suffered a major loss in wheat futures becomes sub-
stantially less willing to provide liquidity in wheat. Is this because he suffered a neg-
ative capital shock, because he updated his view on whether others possessed better
information than he did, or because his long-term view on the expected price evolu-
tion of wheat has changed? Even with precise trading data, it is nearly impossible
to discern between these motives. The ideal way to address such issues is to identify

exogenous shocks to speculators' capital and competition intensity and to examine
market liquidity around such events. My novel empirical strategy draws on this in-
sight and uses three features of the data. I use the fact that active traders participate
in the market for multiple, diverse commodities. For a commodity, such as wheat, I
can separate the remaining contracts traded into fundamentally related futures, e.g.,
4
other cereals, and fundamentally unrelated futures such as metals or spices. I then
proxy for an exogenous shock to a liquidity provider's capital with a gain or loss in
one market unrelated to the fundamentals of another market. Commodities are the
appropriate asset class for my methodology. In contrast to equities and bonds, which
co-move with the market portfolio in commodity space, I can track pairs of securities
that are fundamentally unrelated by measuring their price correlation and selecting
low correlation pairs. Finally, I decompose a trader's daily revenue into a component
due to intra-day round-trip trades within a contract and a component due to holding
a position overnight for at least one day. As trading revenue from intra-day, round-
trip trades may be mechanically correlated with future liquidity measures
1
, I argue
that an active trader's second type of revenue, derived from fundamentally unre-
lated commodities, serves as a source of plausibly exogenous variation in his capital,
which allows me to directly identify the relationship between capital and willingness
to trade and provide liquidity. The identification restriction is that changes in prices
of unrelated commodities have no effect on a trader's desire to trade in a commodity,
except through affecting that trader's capital.
I quantify the effect of plausibly exogenous variation in active traders' aggregate
capital on overall individual security liquidity as measured by average daily bid-
ask spread. While my estimates are consistent with theory - an exogenous negative
shock to active traders' aggregate capital causes an increase in next-day liquidity - the
economic magnitudes are small. To my knowledge, this paper is the first to provide
credible causal estimates of the capital channel for liquidity in a competitive market

1
For example, if liquidity providers trade with limit orders, their intra-day trading revenue should
be larger on days when the bid-ask spread is large. If liquidity measures are persistent over time, then
intra-day revenues may be mechanically positively associated with short-run liquidity. The concern
extends to intra-day, round-trip revenue from uncorrelated commodities, if for any reason, liquidity
across price uncorrelated commodities has common components due to causes other than constraints
or competition among multi-commodity trading arbitrageurs.
5
environment. In order to further investigate the impact of capital shocks on liquidity
provision, I turn to active traders' participation decisions. Although I do find that
a decrease in capital due to trading in an unrelated security, lowers participation as
measured by a participation indicator, number of trades or traded value and that this
effect is non-linear, the magnitude of the estimated coefficients is again economically
small.
Interestingly, I also find that the effects of intra-day round-trip trading losses on
liquidity as well as on participation levels in a particular commodity are considerably
larger than those due to revenues from overnight positions even when the loss comes
from trading in unrelated commodities. However, as pointed out earlier, the former
effect could potentially be due to a mechanical correlation.
I next ask whether changes in competition for liquidity provision cause variation
in subsequent market liquidity. Intuitively, profitable active traders are natural com-
petitors for liquidity provision. As such, for each commodity, I measure competition
intensity as the ratio of profitable active traders to the total number of active traders.
Following a similar empirical design as above, I find that plausibly exogenous shocks
in competition for liquidity provision due changes to in the number of winning ac-
tive traders in uncorrelated commodities explain a large fraction of the variation -
between 20-30% - in subsequent bid-ask spreads. The effect is non-linear: the bid-
ask spread is smallest around a naturally optimal level of competition, but increases
as competition intensity deviates away from this threshold. An increase in the ratio
of winning active traders in uncorrelated commodities to the total number of active

traders from o to 0.4 corresponds a decrease in next day bid spread of 20 basis points
(50%
of a standard deviation). However, an identically sized increase in this ratio
beyond the 0.5 cutoff corresponds to an increase in next day bid-ask spread of 18 to
6
20 basis points.
In general, the magnitudes and statistical significance of the estimated effects of
interest are larger when predicting liquidity in the non-nearby contracts than in the
nearby contract
2
. This is encouraging because the degree of non-synchronicity in
natural customer order flow is higher in non-nearby contracts and hence shocks to
liquidity supply from market making intermediaries are expected to have a more
pronounced impact on the aggregate liquidity of these contracts.
My results suggest that although on the NCDEX market, intermediary-speculators'
capital constraints do generate spill-overs in participation and liquidity from one se-
curity to another, such spill-overs are economically small. In contrast, competition
spill-overs defined as competition among active traders who made profits in other,
unrelated commodities appears to be an economically important channel.
My paper is most closely related to a study by Camerton-Forde, Hendershott,
Jones,
Moulton, and Seasholes (2008) who show that aggregate market and specialist-
firm level effective bid-ask spreads widen following periods when the NYSE special-
ists have large positions or lose money, suggesting that market makers' financing
constraints are associated with lower future liquidity. Several earlier studies have
also suggested that this channel is important for the time-series variation of liquidity.
Hatch and Shane (2002) show that specialist firm acquisitions are followed by a lower
bid-ask spread in stocks assigned to the acquired firm potentially due to an easing
of financing constraints. Coughenour and Saad (1998) compare the liquidity charac-
teristics of assigned stocks across specialist firms with different organizational forms

showing that the stocks assigned to partnerships whose access to capital is tighter
relative to firms capitalized by larger parent companies exhibit worse liquidity fea-
2
The nearby contract is the contract with the closest expiration date, but does not expire in the current
month. On a given day, the contract records the highest traded volume.
7
tures.
There are at least three drawbacks to these studies. First, they all focus on
NYSE specialist firms and hence are relevant only to this specific market structure.
Second, the sample period examined ends before 2007 when the NYSE adopted its
electronic hybrid market structure, significantly decreasing the importance of spe-
cialists for liquidity provision. Most importantly, none of these estimates can be
interpreted causally. My main contribution to this literature is to provide plausible
causal estimates of the capital-channel for liquidity variation. In addition, my setting
is that of an electronic, limit order market with no designated market makers, the
market structure most common nowadays.
My study also relates to an extensive theoretical (Amihud and Mendelson, 1980;
Ho and Stoll, 1981, 1983) and empirical literature (Hasbrouck, 1998a; Hasbrouck and
Sofianos, 1993; Hasbrouck, 1998b; Naik and Yadav,
2003;
Wahal, 1998) that character-
izes the trading behavior and market quality impact of designated dealers focusing
on specialists on the NYSE and dealers on NASDAQ. For example, the empirical
study of Hasbrouck and Sofianos (1993) investigates the trading activity of specialist
on the NYSE, finding that they make profits largely from short-term market making
activity rather than from long-term value positions. Wahal (1998) finds that the entry
and exit of dealers on NASDAQ has a significant effect on spreads in addition to
other known determinants. However, he does not have access to dealers' positions
and performance and hence cannot directly estimate the link between capital and
liquidity.

My work indirectly contributes to the recent literature that exhibits common fac-
tors in the liquidity of stocks (Chordia and Subrahmanyam, 2005a; Huberman and
Halka, 2001; Hasbrouck and Seppi, 2001) and across different asset classes such as
stocks and bonds (Chordia and Subrahmanyam, 2005b). While studies such as that
8
of Chordia and Subrahmanyam (2005b) and Hameed, Kang, and Viswanathan (2008)
point to macro-economic factors as driving such commonality, others suggest that it
could be due informational spill-overs or capital constraints of speculators trading
across different asset classes. For example, Coughenour and Saad (1998) find that
stocks traded by the same specialist firm share a common liquidity component and
suggest that this might be due the specialist firm's capital constraints. I contribute
to this debate through my empirical design, which strives to distinguish between the
informational and capital constraints channel.
Through the use of data from commodity futures markets, my study also relates
to an older article that examines the activities of floor traders in CBOT's futures pits
(Manaster and Mann, 1996). The authors focus on cross-sectional relationships be-
tween market makers' inventory positions and document that they control inventory
throughout the trading day. However, they also show that floor traders' behavior con-
tradicts typical inventory control models as their inventories and reservation prices
are positively correlated consistent with active position taking. My work examines
naturally arising rather than designated market makers and focuses on the effect of
capital constraints and competition intensity on liquidity.
The paper proceeds as follows. Section 1.2 briefly describes the institutional con-
text of commodity futures markets and the National Commodity and Derivatives
Exchange in India, the data and my sample selection criteria. Section 1.3 describes
my procedure for the selection of active traders as well as these traders' portfolios,
trading and performance characteristics. Section 1.4 describes the main features of my
methodology, which isolates exogenous shocks to active traders' capital. Sections 1.5
and 1.6 focus on the impact of active trades' capital and competition intensity on
the liquidity of the securities in which they trade. Section 1.8 evaluates the effect of

9
capital availability on active traders' participation decisions. In section 1.7 I perform
robustness checks. Section 3.12 concludes and outlines future research directions.
1.2 Institutional Context and Data
NCDEX is one of the 3 national exchanges recognized by the Indian commodity
regulator, the Forward Markets Commission, and the Government of India, after the
policy decision taken early 2003 to fully lift the ban on commodity futures trading
3
. NCDEX functions as an electronic limit order book providing commodity futures
in 98 commodities during my sample period broadly in 4 categories - agricultural
produce, precious metals, non-ferrous metals and energy. Additional details on the
the rules governing the NCDEX trading platform are available in Appendix A.i. At
the end of 2008, NCDEX accounted for 25% of the total Indian commodity futures
market, but more than 85% of the agricultural commodities market. MCX, its main
competitor, dominates trading in metals and energy.
Table 1.1 presents the evolution of the exchange by year and commodity type
over the entire sample period between 2004Q1-2008Q1. During this period, the av-
erage daily one sided open interest value was $711 million and average daily traded
value $583 million; 98 commodities were traded on the exchange, of which 75 were
agricultural commodities. In total, 160,045 traders and 835 members participated in
the market. Members of the exchange act as brokers and may trade on proprietary
accounts. Approximately 85% of the traders are classified as individuals, with the
3
The first national commodity futures exchange, National Multi-Commodity Exchange (NMCE) was
launched in Ahmedabad in November 2002, followed by two exchanges in Mumbai, the Multi Commod-
ity Exchange (MCX) launched in October, 2003 and the National Commodity and Derivatives Exchange
(NCDEX) launched on December 15,
2003.
During my sample period, commodity futures traded on
the 3 national exchanges and 22 local exchanges. However, trading volume concentrated on national

exchanges with 99% of traded value occurring on NCDEX and MCX by 2006.
10
remaining accounted for by domestically registered commercial companies, financial
securities firms and brokers. In contrast to markets in developed economies, partici-
pation in commodity futures markets in India was severely restricted over my sample
period
4
with banks, mutual funds, hedge funds and foreign investors forbidden from
trading.
My study uses proprietary data including the entire trading, holding, order, and
best bid-ask records of the exchange since its inception on December 15, 2003 to
March 31, 2008. The data come from the Surveillance Files of the trading platform,
and thus they are complete to the best extent possible as well as highly reliable In the
following, I describe my data and sample selection.
Each trading record consists of a timestamp (seconds granularity), contract sym-
bol,
buy/sell indicator, quantity traded, and transaction price. For each trade, I can
identify both parties to the trade as well as the unique order sequence number that
generated the trade. These data allow me to precisely compute the open position
value and trading revenue for each market participant at high frequency.
Bid-ask data by contract are available for the period July 14, 2004 and March 31,
2008.
I employ all the bid-ask observations to compute average daily percentage and
dollar bid-ask spreads by commodity and contract. Liquidity measures are computed
separately for the nearby contract and other active contracts traded concomitantly.
For commodities that began trading after the first date of the bid-ask series, I remove
the first 20 days of trading in order to exclude potential outliers due to thin markets at
contract launch. In addition, I remove observations on trading days when the bid-ask
spread is available, but no trades took place in the nearby contract.
Information on the institutional types of approximately 90% of the accounts is

4
Restrictions apply to the current date, although a pending government bill proposes removing such
barriers.
11