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Speculation and Hedging in Segmented
Markets
Itay Goldstein
Department of Finance, Wharton School, University of Pennsylvania
Yan Li
Department of Finance, Fox School of Business, Temple University
Liyan Yang
Department of Finance, Joseph L. Rotman School of Management,
University of Toronto
We analyze a model in which traders have different trading opportunities and learn
information from prices. The difference in trading opportunities implies that different traders
may have different trading motives when trading in the same market—some trade for
speculation and others for hedging—and thus they may respond to the same information
in opposite directions. This implies that adding more informed traders may reduce price
informativeness and therefore provides a source for learning complementarities leading to
multiple equilibria and price jumps. Our model is relevant to various realistic settings and
helps to understand a variety of modern financial markets. (JEL G14, G12, G11, D82)
1. Introduction
Modern financial markets are populated by different types of traders, who have
different trading opportunities. In this paper, we demonstrate that this market
segmentation feature has unexpected consequences for market efficiency and
other aspects of asset prices. In a nutshell, the difference in trading opportunities
implies that different traders have different motives when trading a given
asset—some trade for speculation, while others trade for hedging—and this
might reduce price efficiency and cause excess volatility.
For helpful comments and discussions, we thank Efstathios Avdis, Henry Cao, Vincent Glode, Jeremy Graveline,
Jungsuk Han, Tom McCurdy, Maureen O’Hara, Marcus Opp, Günter Strobl, James R. Thompson, Yajun
Wang, Masahiro (Masa) Watanabe, and participants at the 2011 China International Conference in Finance
(Wuhan, China), the 2011 European Finance Association Conference (Stockholm, Sweden), the 2011 Northern
Finance Association Conference (Vancouver, Canada), and the 2012 Financial Intermediation Research Society
Conference (Minneapolis, U.S.). We are especially grateful to the editor (David Hirshleifer) and an anonymous
referee for constructive comments that have significantly improved the paper. Yang thanks the Social Sciences and
Humanities Research Council of Canada for financial support. Send correspondence to Itay Goldstein, Department
of Finance, Wharton School, University of Pennsylvania, Philadelphia, PA 19104; telephone: (215)746-0499.
E-mail: itayg@wharton.upenn.edu.
© The Author 2013. Published by Oxford University Press on behalf of The Society for Financial Studies.
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doi:10.1093/rfs/hht059 Advance Access publication September 12, 2013
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The Review of Financial Studies / v 27 n 3 2014
The market segmentation induced by traders with different trading
opportunities is relevant to many real-world examples. We review some of
them in Section 3. Aleading example is the commodities futures markets. In this
market, financial institutions are limited to trade in the futures contracts and use
them for speculation purposes, while commodities producers trade the futures
contracts mostly for hedging, as they fulfill their speculative activities directly in
the production markets. Hence, in the commodity futures market, the different
types of traders trade in different directions in response to information—some
trade for speculation and others for hedging. This can lead to a reduction in
price informativeness and an increase in the futures risk premium.
Other examples involve convertible bonds markets and credit default swaps
(CDS) markets. Typically, some institutions, mostly hedge funds, trade in these
markets while at the same time they also trade in the underlying bond or equity
markets. Other traders, such as retail investors and traditional institutional
investors, are limited to trading in the underlying traditional markets due to
various frictions reviewed in Section 3.3. Hence, the situation highlighted by
our analysis arises, as hedge funds may respond to information in the opposite
direction in their trading in the underlying market than the traditional investors,
leading to negative implications for market efficiency and an increase in the
cost of capital. We discuss additional examples of similar segmentation, such
as across international markets (where some investors are affected by home
bias and others invest across borders), and with human capital markets.
We build a model to formally analyze the pricing and efficiency implications
of the market segmentation featured in these real-world examples. Our model
is based on the classic paper of Grossman and Stiglitz (1980) and extends
it to consider multiple segmented markets. We have two types of (rational)
traders—traders with a relatively small investment opportunity set, S-traders
(e.g., individuals or mutual funds), and traders with a relatively large investment
opportunity set, L-traders (e.g., hedge funds)—and two types of correlated risky
assets—A (e.g., stocks, bonds) and B (e.g., convertible bonds, CDS). Markets
are segmented, such that S-traders can only trade the A-asset, while L-traders
can trade both types of assets.
1
All traders observe the prices of both assets. The
two risky assets share a common fundamental component, and L-traders may
use the commonly traded A-asset to hedge their investments in the B-asset (or
vice versa). Before entering the financial market, S-traders can collect private
information about the common fundamental at some cost, while L-traders are
endowed with private information.
We solve the model in closed form and characterize how the prices of the
two assets are determined. We further analyze how the cost of capital and price
informativeness of these two assets depend on interesting model parameters,
such as the number of L-traders and the profitability of speculative positions in
1
The letters “L” and “S” in “L-traders” and “S-traders” mean large and small investment opportunities,
respectively. The letter “A” in the risky “A-asset” means that all traders can trade it.
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Speculation and Hedging in Segmented Markets
the B-asset. The results depend crucially on the trading behavior of L-traders.
More specifically, L-traders trade the risky A-asset for two reasons: speculating
based on superior information about the A-asset’s payoff, and hedging their
investment in the B-asset. Depending on the strength of these two motives,
our model generates very different results regarding the cost of capital and
price informativeness. Of particular interest to us is the case where the hedging
motive in the A-asset is strong. In this case, L-traders trade very differently
from S-traders and tend to reduce the informativeness of the price and increase
the cost of capital.
In Section 5.3, we discuss the implications of these results for policy and
empirical work. First, considering the futures markets, our model sheds new
light on the determinants of the futures risk premium and how it is affected
by the financialization of commodities markets. This can guide policy debate
regarding the desirability of this trend. Second, there is wide debate concerning
the optimal scope of hedge fund activities, and our model speaks to such debate
by showing when the trading activities of hedge funds (L-traders in many of
our examples) are damaging to market efficiency. Third, our model provides a
framework to analyze the effect of trading derivatives, such as CDS markets,
on the efficiency of the primary underlying markets.
We further study the incentive of S-traders to collect information regarding
the fundamental of the commonly traded A-asset. Most of the existing
literature predicts that when more investors are informed, the value of the
information is reduced, and investors have less incentive to gather information,
resulting in strategic substitution in learning.
2
In our model, however, learning
complementarities can naturally arise. That is, as more S-traders become
informed, information becomes more valuable, and uninformed S-traders
have a stronger incentive to collect it, generating strategic complementarity
in information acquisition. The intuition is as follows. Suppose that the
fundamental of the two assets is strong. If L-traders can better explore the
trading opportunities in the B-asset, they will increase their investment in the
B-asset and decrease their investment in the A-asset (due to hedging). When
the price informativeness of the A-asset is determined mainly by the L-traders’
hedging-motivated trading, raising the number of informed S-traders will raise
their speculative demand, making the two offsetting forces—from informed
S-traders and L-traders—more balanced. This, in turn, will make the price
less responsive to changes in information, so that uninformed S-traders have
a more difficult time gleaning information from prices. The resulting learning
complementarities can generate multiplicity of equilibria and excess volatility
in prices.
2
In particular, Grossman and Stiglitz (1980, 394) formulated the following two conjectures about price
informativeness and strategic learning: “Conjecture 1: The more individuals who are informed, the more
informative is the price system. … Conjecture 2: The more individuals who are informed, the lower the ratio of
the expected utility of the informed to the uninformed.”
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We emphasize that the basic premise underlying our results is that markets
are segmented in terms of the ability to move capital across markets and trade
in different markets due to various frictions, but not so much in terms of price
information. In other words, capital is relatively segmented and slow moving
(e.g., Duffie 2010), but information is relatively integrated and fast moving, and
traders actively use this information (e.g., Cespa and Foucault 2012). In fact, we
show in Appendix B.2 that our results hinge on the ability of traders to observe
and understand market prices even in the markets in which they do not trade.
We argue in Section 3.3 that this notion of segmentation/integration is very
relevant for today’s markets given the improvement of information technology
on the one hand and the specialization and delegation of investment on the
other hand, making it easy for information to flow across markets but putting
frictions on the flow of capital.
1.1 Related literature
Our paper is broadly related to five strands of theoretical literature. First, our
paper contributes to the literature that develops different mechanisms that gen-
erate strategic complementarity in information acquisition in financial markets.
Froot, Scharfstein, and Stein (1992) show that if traders have short horizons,
they may herd on the same type of information and learn what other informed
traders also know. Hirshleifer, Subrahmanyam, and Titman (1994) demonstrate
the possibility of strategic complementarity in collecting information when
some traders receive private information before others. Veldkamp (2006a,
2006b) relies on fixed costs in information production to generate strategic
complementarities and explain large jumps and comovement in asset prices.
Garcia and Strobl (2011) study how relative wealth concerns affect investors’
incentives to acquire information. Barlevy and Veronesi (2000, 2008) and
Breon-Drish (2011) generate strategic complementarities with non-normally
distributed asset payoff structures. Our paper proposes a different mechanism
for strategic complementarities in financial markets—namely that traders, who
have related pieces of information but have different investment opportunity
sets, may wish to trade an asset in different directions, thereby reducing price
informativeness.As we argue in Section 3, our mechanism is relevant to various
realistic settings and captures a key feature of modern financial markets.
Second, our paper is related to the literature on derivative markets. In
particular, as we show in Section 3.1, our model can be viewed as a setting of
the commodity futures market, and our analysis provides a new information
channel for commodity hedgers to affect futures prices. By contrast, the
literature has largely ignored this channel because most models are conducted
in a setup without asymmetric information (see, e.g., Hirshleifer 1988a, 1988b;
Gorton, Hayashi, and Rouwenhorst 2013). The only exceptions that we are
aware of are Stein (1987) and Sockin and Xiong (2013). Our paper differs from
and complements both papers in terms of research questions and mechanisms.
Stein (1987) studies how speculation affects price volatility and welfare, and
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Speculation and Hedging in Segmented Markets
in his model, the entry of informed speculators brings into the price the
noise in their signals, which lowers price informativeness and can lead to
price destabilization and welfare reduction. Sockin and Xiong (2013) develop
a model to study an information channel for commodity futures prices to
feed back to commodity demand and spot prices, and provide implications
for transparency and econometric implementations. In contrast, our model
examines information transmission occurring in the futures market, and the
negative informativeness effect is caused by behaviors of those traders who are
informed of the same information but respond to this information in opposite
directions. In addition, our analysis focuses on futures premiums and explores
implications for learning.
The applications of our analysis to other derivatives also link our paper
to the theoretical and empirical studies on options, CDS, etc. For example,
Biais and Hillion (1994) develop a model to show that introducing options
can alleviate the market breakdown problem by completing the markets, but
can also complicate the information inference problem of market makers by
complexifying the strategies of informed insiders. Chakravarty, Gulen, and
Mayhew (2004) find evidence that informed traders trade in both stock and
option markets and affect price discovery. Recently, Boehmer, Chava, and
Tookes (2012) provide evidence that the trading in different derivative markets
affects the equity market in different ways. Our paper complements those
studies by highlighting a new channel (segmentation) through which the effect
of informed trading on efficiency might be negative.
The third line of research related to our paper is the study of multiple
assets in (noisy) rational expectations equilibrium settings. Admati (1985)
is the first to analyze the properties of noisy rational expectations equilibria
for a class of economies with many risky assets. Watanabe (2008) and
Biais, Bossaerts, and Spatt (2010) extend Admati’s model to an overlapping
generation setting to study the effect of asymmetric information and supply
shocks on portfolio choice, return volatility, and trading volume. Yuan (2005)
introduces borrowing constraints into a two-asset model and shows how
trading can cause contagion across two fundamentally independent markets.
Van Nieuwerburgh and Veldkamp (2009, 2010) show that the interactions
between the multi-asset portfolio problem and the information acquisition
problem help to explain the home-bias puzzle and the underdiversification
puzzle. All the above-mentioned papers assume that all investors have equal
access to the same investment vehicles, unlike the market-segmentation
scenarios that are the focus of our paper. We demonstrate in Appendix B.1
that this segmentation is key to our results.
Fourth, a number of papers feature hedging-motivated trading in financial
assets. Glosten (1989), Spiegel and Subrahmanyam (1992), Dow and Rahi
(2003), Goldstein and Guembel (2008) and Kyle, Ou-Yang, and Wei (2011),
among others, study Kyle (1985)–type models with endogenous noise trading
generated from risk-averse uninformed hedgers who hedge their endowment
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