Public information and uninformed trading: Implications for market liquidity and price efficiency

doi:10.1016/j.jet.2016.02.012

Journal of Economic Theory

Volume 163, May 2016, Pages 604-643

https://doi.org/10.1016/j.jet.2016.02.012 Get rights and content

Abstract

We develop a rational expectations equilibrium model in which noise trading comes from discretionary liquidity traders. The equilibrium quantity of aggregate noise trading is endogenously determined by the population size of liquidity traders active in the financial market. By improving market liquidity, public information reduces the expected trading loss of liquidity traders and thus attracts more such traders to the market, which negatively affects information aggregation. Analyzing an alternative setting that models noise trading as coming from hedgers yields similar insights. In a setting with endogenous information, public information can harm information aggregation both through crowding out private information and through attracting noise trading.

Introduction

Rational expectations equilibrium (REE) models have been the workbench for analyzing financial markets by providing a machinery of Hayek's (1945) idea that prices aggregate information dispersed among market participants. These models typically introduce “noise trading” or “liquidity trading” to prevent the market price from fully revealing private information and to circumvent the “no trade” problem (Milgrom and Stokey, 1982). The essential feature of noise trading is that it has no informational content; that is, in a statistical sense, it is independent of the fundamental value of the traded asset.¹ The theoretical literature has so far focused on studying the behavior of investors who trade on private information and it largely ignores how the quantity of noise trading is determined.²

In the modern financial market, much of uninformed trading is engaged by financial institutions. For example, fund managers need to rebalance their portfolios for non-informational reasons when receiving large inflows or redemptions from clients.³ The resulting trading can be viewed as “discretionary liquidity trading,” which has been studied in the microstructure literature (e.g., Admati and Pfleiderer, 1988, Foster and Viswanathan, 1990). Another example of uninformed trading is algorithmic trading, which has become increasingly dominant in the stock market. Skjeltorp et al. (2016) document that algorithmic trading originating from large institutional investors is likely to be uninformed. Uninformed trading may also result from hedging activities of financial institutions. For instance, investment banks may invest in commodity futures to hedge their issuance of commodity-linked notes (CLNs) whose payoffs are linked to the price of commodity futures. Henderson et al. (2015) provide evidence that futures investments of CLN issuers do not convey information about fundamentals but nonetheless significantly impact commodity futures prices.

What determines the size of noise trading in financial markets? What are the implications of this endogenous noise trading for market outcomes? In this paper, we provide theoretical models to answer these important questions. The baseline model in Section 3 generates uninformed trading using the notion of discretionary liquidity traders. These traders are uninformed and may experience future liquidity shocks. Anecdotal evidence suggests that transaction cost is an important factor in determining the behavior of discretionary liquidity traders.⁴ Our mechanism of determining noise trading makes an effort to capture this feature.

Formally, we develop a model with one risky asset. Differentially privately informed speculators and uninformed discretionary noise traders exist. Speculators trade on their private information to maximize expected utility. Noise traders are “discretionary” in the sense that each chooses whether to participate in the market by optimally balancing the expected loss from trading against informed speculators versus a liquidity benefit of market participation. The expected loss is endogenously determined by market illiquidity (price impact) while the constant benefit represents the exogenous liquidity needs (and hence the “liquidity” part in the term “liquidity traders”). This trade-off is central to the transaction-cost minimization behavior of real world investors. The optimal mass of discretionary noise traders participating in the market determines the equilibrium quantity of noise trading.

We use our model with endogenous noise trading to investigate the implications of public information for market liquidity and price efficiency, two key variables that represent market quality and are of central importance to regulators.⁵ Market liquidity refers to a market's ability to facilitate the purchase or sale of an asset without drastically affecting the asset's price. Price efficiency, also called “market efficiency” or “informational efficiency,” concerns how well the price transmits or aggregates the information that is relevant to the asset's fundamental value. We use public information as a way to change market environment, because public-information disclosure has been proposed as the foundation of financial regulations.⁶

We show that disclosing payoff-relevant public information attracts noise trading, improves market liquidity, and harms price efficiency. The intuition is as follows. More public information reduces information asymmetry and adverse selection; thus, for a given amount of noise trading, it improves market liquidity. In turn, better liquidity lowers the expected loss of discretionary noise traders thereby attracting more such traders to the market, leading to more non-informational trading in the market. Hence, the information asymmetry problem weakens, which further improves market liquidity. As a result, both the equilibrium amount of aggregate noise trading and market liquidity increase with the precision of the public signal. Since noise traders are uninformed, increased noise trading negatively impacts the effectiveness of asset price in aggregating speculators' private information, which implies that disclosure negatively affects price efficiency.

In Section 4, we extend our analysis to endogenize speculators' private-information acquisition decisions. The effect of public information on information production is ambiguous, as there are two competing forces. First, a negative crowding out effect has been documented in the literature (e.g., Diamond, 1985): more disclosure can crowd out speculators' trading gains from private information thereby discouraging information production. The second effect is a positive effect highlighted by our analysis. That is, as we show in the baseline model with exogenous information, disclosure attracts noise trading, which in turn can encourage information production. We characterize conditions under which the crowding out effect dominates. For instance, when the public information is sufficiently precise, disclosure harms private information production. When this happens, public information negatively affects price efficiency through two reinforcing channels—i.e., by attracting noise trading and by discouraging information production.

In Section 5, we study an alternative model in which uninformed trading is provided by hedgers. Hedgers can incur a cost to develop a private technology whose return is correlated with the risky asset payoff. So, hedgers can invest in the risky asset to hedge their investment in the developed technology. We endogenize the mass of active hedgers, which in turn determines the size of noise trading in the risky asset market. This model well describes the issuance of CLNs in reality: the tradable asset is commodity futures, while CLNs represent the private technology accessible to investment banks that determine whether to issue CLNs and use commodity futures to hedge issuance. We show that our main insight continues to hold in this alternative model. That is, the equilibrium size of noise trading depends negatively on the transaction cost incurred by hedgers, which is in turn negatively affected by endogenous market liquidity. Thus, public information improves market liquidity but can harm private information aggregation through attracting noise trading.

Section snippets

The literature on public information

There is a voluminous literature examining the implications of public information for firm value, market liquidity, efficiency, prices, and investor welfare (for excellent surveys, see Verrecchia, 2001 and Leuz and Wysocki, 2007). Previous studies have used REE models to explore the implications of public information for the cost of capital (Hughes et al., 2007, Lambert et al., 2007), for private information acquisition and price informativeness (Lundholm, 1991, Demski and Feltham, 1994), and

The setup

Time is discrete, and there are three dates: $t = 0, 1$ , and 2. The timeline of the economy is described in Fig. 1. At date 1, two assets are traded in a competitive market: a risk-free asset and a risky asset, which can be understood as a firm's stock or an index on the aggregate stock market. The risk-free asset has a constant value of 1 and is in unlimited supply. The risky asset is traded at an endogenous price $\tilde{p}$ and has a fixed supply, which is normalized as one share. It pays an uncertain

Characterization of the overall equilibrium

We now extend our baseline model presented in Section 3 by adding an information acquisition date, $t = \frac{1}{2}$ , which is between dates 0 and 1, again as shown in Fig. 1. Our analysis of information acquisition closely follows Verrecchia (1982) and Holmström and Tirole (1993). Specifically, at date $\frac{1}{2}$ , speculator i can acquire a private signal with precision $ρ_{ε i}$ according to an increasing and convex cost function $C (ρ_{ε i}) \geq 0$ , with $C (0) = C^{'} (0) = 0$ . For example, the cost function can take the quadratic form of

The setup

The model setup is almost identical to the baseline model except for the behavior of liquidity traders, who are now modeled as rational hedgers. Specifically, there are still three dates, $t = 0, 1$ , and 2. At date 1, a risk-free asset (with a zero net rate of return) and a risky asset are traded in the financial market. The risky asset has an endogenous price $\tilde{p}$ and it pays off $\tilde{v} \sim N (0, 1 / ρ_{v})$ (with $ρ_{v} > 0$ ) at date 2. There are three groups of traders in the financial market: informed speculators (with

Empirical relevance and model predictions

A leading example of discretionary liquidity trading that we seek to model is uninformed trades by institutional investors. Chordia et al. (2011) find that institutions are the driving forces behind the uptrend in stock market trading activities over the last two decades. Uninformed trading by institutional investors may arise due to their need to rebalance portfolios around index recompositions or liquidate their positions following shocks to their portfolios such as large losses in part of

Conclusion

We construct a tractable REE model to endogenize noise trading and examine the implications of public information. The crux of our analysis is that noise trading endogenously chases market liquidity, which implies that the size of aggregate noise trading in the market responds to changes in the market environment. We use two approaches to implement our idea. In the baseline model, noise trading comes from uninformed, discretionary liquidity traders, while in the alternative model, it comes from

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^☆: We are grateful to the editor (Xavier Vives), the associate editor, and three anonymous referees for constructive comments that have significantly improved the paper. We thank Giovanni Cespa, Itay Goldstein, Wei Jiang, Pierre Jinghong Liang, Wei Xiong, and participants at various seminars and conferences. We thank the TCFA for awarding this paper the Best Paper Award. Yang thanks the Social Sciences and Humanities Research Council of Canada (SSHRC Insight Grants 435-2012-0051 and 435-2013-0078) for financial support.

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Public information and uninformed trading: Implications for market liquidity and price efficiency☆

Abstract

Introduction

Section snippets

The literature on public information

The setup

Characterization of the overall equilibrium

The setup

Empirical relevance and model predictions

Conclusion

J. Financ. Econ.

J. Financ. Econ.

J. Account. Econ.

J. Financ. Econ.

J. Financ. Mark.

J. Econ. Theory

J. Financ. Econ.

J. Financ. Econ.

J. Econ. Theory

J. Econ. Theory

J. Financ. Econ.

J. Account. Econ.

A theory of intraday patterns: volume and price variability

Rev. Financ. Stud.

Learning from prices: public communication and welfare

J. Polit. Econ.

Efficient use of information and social value of information

Econometrica

Shaping liquidity: on the causal effects of voluntary disclosure

J. Finance

The role of dissemination in market liquidity: evidence from firms' use of Twitter™

Account. Rev.

The real impact of passive investing in financial markets

The beauty contest and short-term trading

J. Finance

Rise of the machines: algorithmic trading in the foreign exchange market

J. Finance

Trading volume, information asymmetry, and timing information

J. Finance

Multimarket trading and market liquidity

Rev. Financ. Stud.

Information acquisition and welfare

Rev. Econ. Stud.

Price pressure from coordinated noise trading: evidence from pension fund reallocations

Optimal release of information by firms

J. Finance

Disclosure, liquidity, and the cost of capital

J. Finance

Noise traders

Opaque trading, disclosure, and asset prices: implications for hedge fund regulation

Rev. Financ. Stud.

A theory of the interday variations in volume, variance, and trading costs in securities markets

Rev. Financ. Stud.