Disclosure and Efficiency in Noise-Driven Markets
Bing Han, Yu-Jane Liu, Ya Tang, Liyan Yang, Lifeng Yu*
February, 2013
Abstract
Disclosure can negatively affect stock market efficiency and real investment performance
in markets populated with uninformed noise traders whose trading is affected by
disclosure. We formalize this idea by analyzing a model where disclosure attracts noise
traders and where firms infer information from stock prices to guide real investment
decisions. The model predicts that disclosure can reduce the amount of information
learned by firms from stock price, which in turn harms the investment efficiency. We find
strong support for these predictions regarding informational and allocative efficiency
using accounting and financial data from the Chinese market.
Key Words: Noise Trading, Disclosure, Informational Efficiency, Allocative Efficiency,
Informational Feedback Effect
JEL Classification: D61, G14, G30, M41
* Han: McCombs School of Business, the University of Texas at Austin, 1 University Station - B6600,
Austin, TX, USA, 78712; Email: bing.han@mccombs.utexas.edu. Liu: Department of Finance, Guanghua
School of Management, Peking University, Beijing, P.R. China, 100871; Email: yjliu@gsm.pku.edu.cn.
Tang: Department of Finance, Guanghua School of Management, Peking University, Beijing, P.R. China,
100871; Email: yatang@gsm.pku.edu.cn. Yang: Rotman School of Management, University of Toronto,
105 St. George Street, Toronto, M5S3A6, ON, Canada; Email: liyan.yang@rotman.utoronto.ca; Yang is the
corresponding author. Yu: Department of Finance, Guanghua School of Management, Peking University,
Beijing, P.R. China, 100871; Email: yulifeng@pku.edu.cn. We thank participants at the 2012 NBER-CCER
Conference on China and the World Economy and the 2012 Asian Finance Association (AsianFA) Annual
Conference. All remaining errors are our own.
Disclosure and Efficiency in Noise-Driven Markets
1. Introduction
Disclosure has been proposed as the foundation of financial regulation. 1 Existing
theories of disclosure argue that the public release of corporate information can level the
playing field and reduce the risk faced by traders, thereby improving liquidity and
reducing the cost of capital (e.g., Diamond and Verrecchia, 1991; Verrecchia, 2001;
Easley and O’Hara, 2004). Disclosure is generally believed to help price discovery and
improve informational efficiency.
The literature has separately emphasized the important role of noise traders in financial
markets. 2 The noise trading literature suggests that disclosure can attract noise trading in
at least two ways. First, studies on discretionary noise trading argue that noise traders
rationally chase liquidity to minimize their expected trading opportunity cost (e.g.,
Admati and Pfleiderer, 1988; Chowdhry and Nanda, 1991; Foucault and Gehrig, 2008). 3
To the extent that disclosure improves liquidity, discretionary noise traders chase
disclosure as well. Second, disclosure puts firms in the news more often, which tends to
grab the attention of noise investors who have limited capacity processing information
(e.g., Peng and Xiong, 2006; Barber and Odean, 2008; Smith, 2010; Lawrence, 2011; Li
and Yu, 2011). 4 As more noise traders participate in the trading of a firm’s stock, market
1
For instance, Greenstone, Oyer, and Vissing-Jorgensen (2006, p. 399) state: “(s)ince the passage of the Securities Act
of 1933 and the Securities Exchange Act of 1934, the federal government has actively regulated U. S. equity markets.
The centerpiece of these efforts is the mandated disclosure of financial information.”
2
Noise traders are agents that trade randomly for unspecified liquidity or hedging reasons. The idea of “noise trading”
dates back to Black (1986). Noise trading has become a building block of microstructure models which study price
discovery and liquidity (see Vives (2008) for a survey). De Long et al. (1990) argue that noise trading creates its own
risk and affects prices, and recent empirical studies document supporting evidence (e.g. Barber, Odean, and Zhu, 2009).
3
For example, Admati and Pfleiderere (1988, p. 5) write: “It is intuitive that, to the extent that liquidity traders have
discretion over when they trade, they prefer to trade when the market is ‘thick’-that is, when their trading has little
effect on prices.”
4
When discussing the relation between noise trading and information allocation, García and Sangiorgi (2011, p. 3092)
state: “glamour stocks, more visible firms, and companies that show up prominently in the news are more likely to
attract liquidity trading.” Smith (2010) finds that increased disclosure quantity leads naïve investors to show greater
1
price reflects less of the signals of informed investors; therefore, disclosure can
negatively affect informational efficiency through attracting noise trading.
In addition, the financial market is not just a sideshow, as there is a “feedback effect”
from stock prices to real investment. Financial markets aggregate various sources of
information that can help firm managers in their real investment decisions. 5 As a result,
in the presence of feedback effects, the negative impact on informational efficiency
translates to a negative impact on allocative efficiency as well. In this paper, we
theoretically and empirically explore these negative implications of disclosure in
financial markets populated with noise traders.
We start with a noisy rational expectations equilibrium model with differentially
informed speculators and noise traders trading shares of a firm in a financial market. The
firm has one asset-in-place and one growth opportunity whose value depends on the
investment decisions to be made. The information owned by speculators gets reflected in
the stock price, which in turn guides the firm’s investment decisions. The firm discloses
part of its private information to the public. The disclosure has two opposing effects on
the asset-in-place and growth opportunities, respectively. On the one hand, it improves
the price of asset-in-place by reducing the risk faced by speculators. On the other hand,
disclosure attracts noise traders, which reduces the information gleaned by the firm from
the stock price and results in a less informed investment decision. The optimal disclosure
policy trades off these two opposing effects.
Our empirical analysis tests the dark side of disclosure for informational and allocative
efficiency in the presence of disclosure-chasing noise trading. Specifically, our model
formalizes the following two predictions: (1) disclosure reduces the amount of price
judgment confidence in a laboratory experiment. Lawrence (2011) shows that on average, individuals invest more in
firms with readable, concise, and transparent financial disclosures.
5
The idea of the “feedback effect” dates back to Hayek (1945) and Fama and Miller (1972). For recent empirical
evidence, see Luo (2005) and Chen, Goldstein, and Jiang (2007), among others. Baker, Stein, and Wurgler (2003)
suggest that stock prices can also affect firm investments through the financing channel.
2
information conveyed to the firm and, (2) disclosure reduces investment performance (as
a result of the reduced amount of information learned from the price and the feedback
effect). In addition, these two predictions are expected to be more pronounced among
those stocks (or markets) dominated by more noise traders.
We use financial and accounting data from the Chinese market to test these predictions.
As the largest emerging market that had the second-largest market capitalization among
all national markets at year-end 2011, an essential aspect of the market microstructure of
the Chinese market is the dominance of uninformed retail traders (Choi, Jin, and Yan,
2011). For example, individual investors accounted for 75% of the ownership and over 90%
of the trading activity in the Chinese market from 2004 to 2009 (Bailey, Cai, Cheung, and
Wang, 2009). Empirical evidence suggests that individual behavior bias is prevalent in
the Chinese security market and that Chinese individual investors are vulnerable to the
influence of attention-grabbing events (e.g., Seasholes and Wu, 2007; Li, Rhee, and Wang,
2009). So, as a noise-trading-driven market, the Chinese financial market provides an
ideal venue to test our model implications regarding the negative efficiency implications
of disclosure.
We first document evidence supporting our premise that noise traders chase
disclosure—firms with a high level of disclosure, proxied by a high value of Hutton,
Marcus, and Tehranian’s (2009) measure of financial reporting quality, are indeed
associated with a high percentage of retail investor ownership, which proxies for a high
level of noise trading. 6 We then proceed to test our two predictions regarding the
negative implications of disclosure for the information learned by firms (informational
efficiency) and for the real investment performance (allocative efficiency).
6
We also use earnings precision to measure disclosure (Gow, Taylor, and Verrecchia, 2011) and the results are
qualitatively similar (See Section 4.4).
3
We follow the literature and measure allocative efficiency by Tobin’s Q and return on
asset (ROA) (Healy, Palepu, and Ruback, 1992; Chen, Goldstein, and Jiang, 2007;
Duchina, Matsusakab, and Ozbasb, 2010; McLean and Zhao, 2011; Riroud and Mueller,
2011). Previous literature suggests that the overall information contained in the price,
namely the price informativeness, can be decomposed into two components—managerial
private information and information learned by firm managers (Dow, Goldstein, and
Guembel, 2011; Goldstein, Ozdenoren, and Yuan, 2011). We therefore measure the
information learned by the firm using the residual in a regression running from price
informativeness (proxied by firm specific R-square (Morck, Yeung, and Yu, 2000)) on
managerial information (proxied by earnings surprise (Chen, Golstein, and Jiang, 2007)),
and we label it residual price informativeness. Consistent with our predictions, we find
that a firm’s financial reporting quality, as a proxy for disclosure quality, is indeed
significantly negatively related to residual price informativeness and Tobin’s Q and ROA.
In addition, the interaction terms between disclosure and noise trading are negative and
significant, indicating that the negative efficiency effects of disclosure are stronger when
there is heavier noise trading.
Our study is broadly related to three lines of research in economics. The first one is the
huge disclosure and regulation literature in accounting and finance, with an incomplete
list including Diamond (1985), Fishman and Hagerty (1990), Diamond and Verrecchia
(1991), Rajan (1994), Bushman and Indjejikian (1995), Shin (2003), Lambert, Leuz and
Verrecchia (2007), and Acharya, DeMarzo, and Kremer (2011). 7 Our paper broadens this
literature by pointing out the dark side of disclosure in those noise-driven markets and
thus has important regulation implications particularly for emerging markets.
The second line of research related to our paper is the noise trader literature, which has
focused on the microstructure and asset pricing implications of noise trading (e.g.,
7
See Verrecchia (2001) and Leuz and Wysocki (2007) for surveys.
4
Admati and Pfleiderer, 1988; De Long et al.,1990; Chowdhry and Nanda, 1991; Barber,
Odean, and Zhu 2009). The impact of noise trading on corporate financial decisions has
received surprisingly little attention. By incorporating the feedback effect into a model
with noise traders chasing disclosure, we show that the economic efficiency consequence
of disclosure can be distorted by noise trading. So, our study suggests that noise traders
play an important role not only in security price discovery but also in corporate’s real
investment decisions.
The third line of related research is the growing literature on the informational
feedback effect which posits that stock prices aggregate information and facilitate
efficient allocation of resources. This idea goes back to Hayek (1945) and Fama and
Miller (1972) and recent empirical research provides supporting evidence for it (e.g.,
Baker, Stein, and Wurgler, 2003; Luo, 2005; and Chen, Goldstein, and Jiang, 2007).
There is also ongoing theoretical work modeling the self-fulfilling phenomenon created
by this feedback effect and its resulting impact on asset pricing and corporate decisions,
such as multiple equilibrium in prices and learning (Ozdenoren and Yuan, 2008; Dow,
Goldstein, and Guembel, 2011) and market-based policy making and project selection
(Dow and Gorton, 1997; Bond, Goldstein, and Prescott, 2010; Goldstein, Ozdenoren, and
Yuan, 2010). Our paper adds new evidence for the importance of the feedback effects.
A recent study by Gao and Liang (2011) also propose a disclosure model in the
presence of feedback effects. Our paper differs from and complements theirs in two
dimensions. First, the underlying mechanisms driving the firm’s trade-offs in setting
disclosure level are different. In our model, the positive effect of disclosure comes from
the reduction of the risk faced by risk-averse rational traders and the negative effect
comes from the disclosure-chasing behavior of noise traders. In contrast, in Gao and
Liang (2011), all investors are risk-neutral so that risk is not priced, and the positive
effect of disclosure does not work through reducing the riskiness of the asset but through
5
improving liquidity by reducing the adverse selection problem. Their negative effect of
disclosure is generated from crowding out private information production, not by
attracting noise traders. Our mechanism is more relevant in noise-driven markets, such as
the Chinese market. Second, Gao and Liang (2011) is a pure theoretical analysis on
optimal disclosure, while our paper uses the financial and accounting data in the Chinese
market, the largest emerging market, to seriously test our model-implied predictions
regarding efficiency consequences.
The rest of our paper is organized as follows. Section 2 presents a model to illustrate
how firms make disclosure decisions and formalize the negative efficiency implications
of disclosure, which works as the basis for the development of our two testable
predictions. Section 3 describes the data that we use to conduct our test. Section 4
presents the empirical results and Section 5 concludes.
2. The Model
We propose a parsimonious model to illustrate the impact of disclosure on
informational efficiency and allocative efficiency when firm managers learn from prices
in a market populated with many noise traders. In our model, disclosure has two opposing
effects on the firm value. First, it will improve trading aggressiveness of rational traders
and hence will increase the value of asset-in-place. Second, it will attract rational
discretionary noise traders who chase liquidity improved by disclosure and irrational
noise traders who simply chase news. The increased noise trading will reduce the
information content learned by firm managers and harm the investment efficiency. The
optimal disclosure is determined by the trade-off of these two effects.
2.1 The Setup
We consider a firm with two components. The first component is asset-in-place, which
� ~ N(0, 1⁄ρA ) with ρA > 0. The other component is a
has a liquidation value of A
6
growth opportunity. We follow Subrahmanyam and Titman (1999) and Foucault and
Gehrig (2008) and assume that the terminal cash flow from the growth opportunity is
� ≡ G�k, A
�� = A
� k − 0.5k 2 ,
G
(1)
� and G
� share
where k is the investment made by the firm manager. By construction, A
� and G
� are
the same source of uncertainty and, if the two sources of uncertainty for A
� and G
� are correlated.
different, our results hold as long as A
The firm is traded at a secondary market. We normalize the number of shares as 1. To
avoid the complicated fixed-point problem, we follow Foucault and Gehrig (2008) in
assuming that the growth opportunity is separate and not tradable, while differentially
informed speculators trade asset-in-place in financial markets and the asset price p�
aggregates their information. Specifically, each speculator i has a Constant Absolute Risk
Aversion (CARA) utility function with a risk aversion coefficient of 1, and is endowed
with a signal s�𝑖 :
� + ε�𝑖 ,
s�𝑖 = A
(2)
where ε�𝑖 ~ N(0, 1⁄ρε ) with ρε > 0. Thus, p� will aggregate the signals s�𝑖 and guide
�. We also assume that there is a risk-free asset available, with
the firm’s investment in G
the net interest rate normalized at 0.
We assume that the firm also has private information s�𝑚 , which it can partially
disclose to the public. Specifically, the firm observes:
�+ m
s�𝑚 = A
�,
(3)
where m
� ~ N(0, 1⁄ρm ) with ρm > 0. After observing s�𝑚 but prior to trading, the firm
considers disclosing some of its private information. Following the literature (e.g.,
Diamond, 1985; Bushman and Indjejikian, 1995; Lambert, Leuz and Verrecchia, 2007),
the public disclosure is given by:
y� = s�𝑚 + δ�,
7
(4)
where δ� ~ N(0, 1⁄ρδ ) and parameter ρδ > 0 controls the disclosure level. We assume
�, ε�𝑖 , m
that the random variables �A
� , � are mutually independent.
We assume there is noise trading in the financial market, which prevents market price
from fully revealing information. That is, there are noise traders who demand
x� ~ N(0, 1⁄ρx ) shares independent of the price. More importantly, the size of noise
trading 1⁄ρx is assumed to be positively related to the precision ρδ of disclosure:
ρx = 𝑓(ρδ ),
(5)
where 𝑓(∙) > 0 and 𝑓′(∙) < 0. As discussed in the introduction, the assumption of
“disclosure-chasing” noise trading, i.e., the negative slope of function 𝑓(∙), can be
justified in two ways—“liquidity-chasing” rational discretionary noise trading and
“news-chasing” irrational noise trading. First, as we will show shortly, disclosure
improves liquidity, which in turn lowers the expected opportunity cost associated with
noise trading. When noise traders minimize their expected opportunity cost (Admati and
Pfleiderer, 1988; Chowdhry and Nanda, 1991; Foucaulta and Gehrig, 2008), they will
rationally chase liquidity, and, as a result, the size of discretionary noise trading will be
positively related to disclosure quality. Second, more disclosure announcements also put
the firm in the news more often, which will grab more of the attention of irrational,
“news-chasing” noise traders (see, e.g., Barber and Odean, 2008; and Li and Yu, 2011). 8
In our empirical analysis, we will provide evidence for the assumption that disclosure
attracts noise trading (i.e., 𝑓′(∙) < 0).
We specify the behavior of the firm as follows, which can be interpreted as the firm
acting in the interest of shareholders and maximizing the firm value. Specifically, after
� from
the trading closes, the firm observes price p� , learns some information regarding A
8
Although in our setting there is only one public signal y� and the firm decides its precision to control disclosure quality,
mathematically it is equivalent to assuming that the firm chooses the number of signals with the same precision to
determine disclosure quality; see Lambert, Leuz, and Verrecchia (2007). In this sense, a higher precision of ρδ
corresponds to more news.
8
both p� and its own signal s�𝑚 (and the public signal y� , which is redundant given that y�
is a garbled version of s�𝑚 ), and chooses the optimal investment policy k ∗ = k(s�𝑚 , p� ) to
maximize the value of growth opportunity as follows:
���s�𝑚 , p� ��.
max𝑘 𝐸��G�k, A
(6)
Before trading, the firm sets the disclosure policy ρ∗δ to maximize the sum of the
expected price of the asset-in-place and its expected optimal growth opportunity value
with the full awareness that its disclosure policy will affect the price informativeness in
the future; that is,
���,
maxρδ 𝐸�𝜔p� + (1 − 𝜔)G�k(s�𝑚 , p� ), A
(7)
where 𝜔 ∈ (0,1) captures the ex ante weight of the asset-in-place in the firm’s objective
function, and it reflects the degree of the “myopia” behavior of corporate managers, as
studied by Stein (1989). 9 For example, a firm whose management team has a long tenure
contract might care much the firm’s investment and thus is likely to have a low value of
𝜔. We can think of 𝜔 as the underlying variable driving the cross-sectional difference of
the optimal ρ∗δ chosen by the firms.
To summarize, our model has five stages and the event of order is as follows. At stage
1, the firm sets a disclosure policy ρ∗δ and commits to disclose a public signal y� with
precision ρ∗δ in the future. At stage 2, the firm observes s�𝑚 and makes the disclosure y�
to the public. At stage 3, the size of noise traders is determined according to ρx = 𝑓(ρ∗δ ),
speculators observe signals s�𝑖 , and speculators and noise traders trade the asset-in-place
and the risk-free asset to form the price p� . At stage 4, the firm observes p� and makes
optimal investment decisions k ∗ = k(s�𝑚 , p� ). At stage 5, cash flows are realized and
speculators consume.
9
Stein (1989, p. 655) states: “the more managers are concerned about current share prices, the worse the (managerial
myopia) problem becomes.” Langberg and Sivaramakrishnan (2010) and Kumar, Langberg and Sivaramakrishnan
(2012) have made a similar assumption that managers care about both short-term and long-term prices, with a
parameter representing the degree to which the manager is myopic.
9
2.2 The Analysis
We first derive the financial market equilibrium and then analyze the firm’s investment
decisions to highlight the main tradeoff faced by the firm. Our empirical predictions
focus on the implications of disclosure for price informativeness (informational
efficiency) and the performance of the firm’s real investment (allocative efficiency).
2.2.1 Financial Market Equilibrium
We consider noisy rational expectations equilibrium in the financial market, that is, the
price aggregates information and noisy trading, each speculator invests to maximize their
expected utility conditional on their information sets including the asset price, and the
market clears. As standard in the literature, we consider linear price function:
� + 𝛼𝑥 x� ,
p� = 𝛼0 + 𝛼𝑦 y� + 𝛼𝐴 A
(8)
where the coefficients 𝛼’s are endogenously determined. In particular, parameter 𝛼𝑥
measures illiquidity in the sense of Kyle’s (1985) lambda, i.e., the price impact of a unit
of non-informational order flow. Therefore, a lower 𝛼𝑥 corresponds to a higher liquidity.
� , which is the
By equation (8), the price p� contains information regarding A
liquidation value of the asset-in-place and the productivity of the investment in the
growth opportunity (see equation (1)). Thus, the price is useful for speculators to make a
portfolio investment and for the firm to make the real investment. Specifically, in
combination with the public signal y� (equation(4)), the price is equivalent to the
following signal:
s�𝑝 ≡
�− 𝛼0 − 𝛼𝑦 y
�
p
𝛼𝐴
�+
=A
where the endogenous precision of the noise term is:
𝛼𝑥
𝛼𝐴
ρp ≡ ( 𝛼𝐴 ⁄𝛼𝑥 )2 ρx .
10
x� ,
(9)
(10)
This precision ρp measures the amount of the price information in addition to other
information owned by traders. In particular, it also measures the information learned by
the firm from observing the price, and corresponds to the “residual price informativeness”
that is mentioned in the introduction and used in our subsequent empirical analysis. 10
Given the CARA-normal setup, speculator i’s demand for the firm’s share is
D(p� ; s�𝑖 , y� ) =
� |p
�;s�𝑖 ,y
��−p
�
𝐸�A
.
�
�;s�𝑖 ,y
��
𝑉𝑉𝑉�A|p
(11)
By equations (2), (3), (4), and (9), and applying Bayes’ rule, we have:
−1
−1
D(p� ; s�𝑖 , y� ) = �( 𝛼𝐴 ⁄𝛼𝑥 )2 ρx s�𝑝 + ρε s�𝑖 + �ρ−1
��
𝑚 + ρ𝛿 � y
−1
−1
− �( 𝛼𝐴 ⁄𝛼𝑥 )2 ρx + ρε + �ρ−1
�.
𝑚 + ρ𝛿 � � p
(12)
The market clearing condition for the firm’s share is:
1
∫0 D(p� ; s�𝑖 , y� )𝑑𝑑 + x� = 1.
(13)
Then, plugging equation (12) into equation (13), solving for p� , and comparing
coefficients with equation (8) deliver the equilibrium price function, which is
summarized in the following proposition.
Proposition 1. There is a linear price function in the financial market:
where 𝛼𝑥 =
−
1+𝜌𝜀 𝜌𝑥
𝑝� = 𝛼0 + 𝛼𝑦 𝑦� + 𝛼𝐴 𝐴̃ + 𝛼𝑥 𝑥�,
−1 +𝜌−1 �
𝜌𝐴 +𝜌𝜀 +𝜌𝜀2 𝜌𝑥 +�𝜌𝑚
𝛿
1
−1 +𝜌−1 �
𝜌𝐴 +𝜌𝜀 +𝜌𝜀2 𝜌𝑥 +�𝜌𝑚
𝛿
−1
.
−1
, 𝛼𝑦 =
−1 +𝜌−1 �
�𝜌𝑚
𝛿
1+𝜌𝜀 𝜌𝑥
−1
𝛼𝑥 , 𝛼𝐴 = 𝜌𝜀 𝛼𝑥 ,
and 𝛼0 =
Proposition 1 has two immediate implications. 11 First, the average price 𝐸(p� ) = 𝛼0
increases with disclosure level ρδ , because disclosure reduces the risk faced by each
10
In the microstructure literature (e.g., Brunnermeier, 2005; Peress, 2010), “price informativeness” refers to how much
information is included in the price, or more precisely, it is the conditional precision of the asset cash flow given the
1
price,
, which is jointly determined by traders’ private information, the managerial information and disclosure.
� �
11
𝑉𝑉𝑉�A|p�
These two implications are obtained for a given size 1/ρx of noise trading. In fact, changing ρδ has another
11
speculator, who in turn trades more aggressively, pushing up prices. So, disclosure has a
positive effect on the value of asset-in-place. Second, the liquidity 1/𝛼𝑥 also improves
with disclosure level ρδ , because disclosure alleviates the adverse selection effect and
reduces the risk faced by speculators, thereby lowering the price impact of liquidity order
flows and improving liquidity. The increased liquidity will benefit noise traders. The
� − p� �x� . The expected revenue
revenue that a noise trader receives from buying x� is �A
� − p� �x� � thus proxies for the benefit of noise traders who have to trade shares to
𝐸��A
�−
realize their unmodeled hedging or liquidity needs. By equation (8), we have 𝐸��A
� − p� , x� � = −𝛼𝑥 𝑉𝑉𝑉(x� ), which is positively related to liquidity 1/𝛼𝑥 . This
p� �x� � = 𝐶𝐶𝐶�A
echoes our assumption of 𝑓′(∙) < 0 in the presence of discretionary noise traders who
chase liquidity to maximize trading revenue.
2.2.2 The Firm’s Investment Decisions and Hypothesis Development
After observing price p� , the firm’s information set becomes {s�𝑚 , p� , y� } = {s�𝑚 , p� }, and
based on this new information set, it then chooses the optimal investment k ∗ . By
equations (1) and (6), the optimal investment is:
� �s�𝑚 , p� � =
k ∗ = 𝐸�A
ρm s�𝑚 +ρp s�𝑝
ρA +ρm +ρp
,
(14)
where the second equality follows from equations (3), (9), (10) and Bayes’ rule.
� using its information set {s�𝑚 , p� }, the extra
In particular, when the firm forecasts A
precision obtained from price is ρp , which, by equations (5) and (10) and Proposition 1,
is:
ρp = ρ2ε 𝑓(ρδ ).
(15)
indirect effect through its impact on ρx (i.e., equation (5)). In a full equilibrium with endogenous 𝑓(∙), we believe that
the direct effect dominates.
12
Given that 𝑓′(∙) < 0, we know:
dρp
dρδ
= ρ2ε 𝑓 ′ (ρδ ) < 0.
(16)
That is, disclosure decreases the amount of information that the firm can learn from the
price. Plugging the optimal investment k ∗ back into the growth opportunity (equation
(1)) and taking expectation yields the expected growth value:
��� = 0.5𝑉𝑉𝑉(k ∗ ) = 0.5 � 1 −
𝐸�G�k(s�𝑚 , p� ), A
ρ
ρ
A
1
A +ρm +ρp
�.
(17)
By equation (16), it is thus clear that disclosure will harm allocative efficiency by
reducing the amount of information learned by the firm, that is,
d
dρδ
���� < 0.
�E�G�k(s�𝑚 , p� ), A
(18)
Equations (16) and (18) summarize the negative effect of disclosure on informational
efficiency and allocative efficiency when noise trading is attracted by disclosure. Based
on these two facts, we make the following two predictions:
Prediction 1. Other things being equal, disclosure reduces the amount of information
learned by firms from the price system; that is,
dρp
dρδ
< 0.
Prediction 2. Other things being equal, disclosure reduces the real investment
performance of firms; that is,
d
dρδ
���� < 0.
�E�G�k(s�𝑚 , p� ), A
In addition, since our mechanism works through noise trading, we expect that our two
predictions are more pronounced in markets (stocks) with heavier noise trading.
Although our empirical analysis concerns the implications of disclosure for efficiency,
for completeness, we briefly discuss the optimal disclosure policy of the firm in stage 1.
In sum, the firm faces the following trade-off. On the one hand, disclosure increases the
average value of the asset-in-place; on the other hand, disclosure decreases the average
value of the growth opportunity. The optimal disclosure level is determined by trading off
these two effects, and an analytical solution will require a specification of 𝑓(∙).
13
3. Sample and Variable Construction
Our sample consists of all firms listed on the Shanghai and Shenzhen stock exchanges
during the period of January 2004 through December 2010 with at least three years of
firm-level accounting data. We exclude all financial institution stocks and special
treatment (ST) stocks. 12 We collect our data from three databases. We obtain firms’ daily
return information, analyst coverage, and the final controller of a company from the
China Securities Market and Accounting Research (CSMAR), financial statement data
and institutional holding data from WIND Information Co., Ltd. (WIND), and Tobin’s Q
from the China Center for Economic Research (CCER). 13 Our final sample consists of
7,817 firm-year observations with 1,478 public firms.
The key empirical variables of interest in this paper include noise trading, disclosure,
residual price informativeness, and investment performance, which correspond
��� in our theoretical model. Now we
respectively to ρx , ρδ , ρp , and E�G�k(s�m , p� ), A
discuss their definitions and constructions in details, with their shorthand notations
enclosed in brackets.
Noise trading (NOISE) is defined as the percentage of retail investor ownership,
measuring the extent of noise trader activism. The variable NOISEit is calculated as 1
minus percentage of firm i’ s A-share holdings in year t by mutual funds, brokerage,
financial products issuing by brokerage, QFII, insurance companies, social security funds,
occupational pension schemes, trusts, financial companies, banks, and listed and
non-listed non-financial companies.
Disclosure (DISCLOSURE) measures the quality of a firm’s financial reports.
Following previous accounting and finance literature (see, e.g., Dechow, Sloan, and
12
The financial institution stocks are defined as those stocks with Shenyin & Wanguo industry index being equal to
801190, where the Shenyin and Wanguo industry index is the most widely used industry category in Chinese A share
market.
13
CSMAR, WIND, and CCER are the three main data providers in China.
14
Sweeney, 1995, 1996; Hutton, Marcus, and Tehranian, 2009), we apply a simple and
intuitive measure, the minus of the three-year moving sum of the absolute value of annual
discretionary accruals, DISCLOSUREit, to proxy a firm’s disclosure quality. That is, in
each year t, we define:
DISCLOSUREit= −��DiscAcci,t−1 � + �DiscAcci,t−2 � + �DiscAcci,t−3 ��,
where DiscAccit is the discretionary accrual for firm i in year t. 14 The rationale behind
this measure is that firms with consistently small absolute values of discretionary accruals
are less likely to manipulate reported earnings, thus providing a more transparent
financial report environment (Hutton, Marcus, and Tehranian, 2009). To confirm the
robustness of our results, we replicate our analysis in Section 4.4 using an alternative
disclosure quality measure, earnings precision (Gow, Taylor, and Verrecchia, 2011).
Specifically, for each year t, the variable PRECISION3it (respectively, PRECISION5it) is
defined as the inverse of the variance of the time-series of annual earnings of firm i over
the prior three-year (respectively, five-year) period scaled by average market value.
Residual price informativeness (RES_INFO) measures the information contained in
the price that is new to the manager and thus useful to improve investment decisions.
Intuitively, the overall stock price informativeness, i.e., the total information included in
the price, includes two components—managerial private information and information
new to the firm manager (Dow, Goldstein, and Guembel, 2011; Goldstein, Ozdenoren,
and Yuan, 2011); therefore, after filtering out the managerial private information from the
14
We compute the discretionary accrual for firm i at year t as follows: DiscAccit =
∆Salesit −∆Receivablesit
β�1
+ β�2
Asseti,t−1
PPEit
Asseti,t−1
TAit
Asseti,t−1
− (α
�0
1
Asseti,t−1
+
), where TAit denotes total accruals for firm i during year t (i.e., TAit =
operating profit + impairment losses - net change in fair value - net investment income - cash flow from operating
activities), Asset it denotes total asset for firm i at the end of year t, ∆Salesit denotes change in sales for firm i in year
t, ∆Receivablesit denotes changes in receivables for firm i in year t, and PPEit denotes property, plant, and
equipment for firm i at the end of year t. Parameters α
�0 , β�1 , β�2 are estimated from the modified Jones (1991) model,
TAit
which is a cross-sectional regression using the firms in each Shenyin & Wanguo industry for each year:
=
α0
1
Asseti,t−1
+ β1
∆Salesit
Asseti,t−1
+ β2
PPEit
Asseti,t−1
Asseti,t−1
+ εit .
15
overall price informativeness, we can get the information that is new to the firm manager
in a residual form. Specifically, we first follow the literature and estimate price
nonsynchronicity as a proxy for the overall price informativeness (Roll, 1988; Morck,
Yeung, and Yu, 2000; Durnev et al., 2003; Durnev, Morck, and Yeung, 2004; Veldkamp,
2006; Chen, Golstein, and Jiang, 2007); that is, for each firm i in year t, we run a
regression from firm returns on market and industry returns at daily frequency to get the
𝑅 2 and compute the price nonsynchronicity ln �
1−𝑅 2
𝑅2
�. Then, we use earnings surprise,
measured by abnormal return around the earnings announcement dates, 15 to proxy for the
managerial private information. As managers know the earnings before they are released
to the public, earnings surprise captures information that managers have before it is
1−𝑅 2
reflected in the price (Chen, Goldstein, Jiang, 2007). Finally, we regress ln �
𝑅2
� on
earnings surprise and compute the residual to proxy the information learned by managers
from the price, and label it “residual price informativeness” (RES_INFO). A higher value
of RES_INFO indicates more information learned by managers.
Investment performance measures allocative efficiency and is proxied by two
different variables: Tobin’s Q (Q), calculated as the market value of tradable A shares,
tradable B shares, and H shares plus book value of non-tradable A shares, plus book
value of debt, scaled by book value of asset (Duchina, Matsusakab, and Ozbasb, 2010;
Riroud and Mueller, 2011; McLean and Zhao, 2011); and return on asset (ROA),
calculated as operating earnings as a percentage of book value of asset, where operating
earnings are earnings before interest, taxes, depreciation, and amortization (Healy, Palepu,
and Ruback, 1992; Chen, Goldstein, and Jiang, 2007).
Other variables that will be used in our empirical analysis include firm size, leverage,
analyst coverage, market liquidity, and a dummy variable indicating whether the final
15
We also calculate earnings surprise as the difference between the announced earnings and the average earnings
forecast scaled by share price (Kothari, 2001). The results are qualitatively similar.
16
controller of an enterprise is state-owned. These variables are denoted as SIZE,
LEVERAGE, ANALYST, LIQUIDITY and SOE, respectively, and their definitions are
provided in Table 1.
[Insert Table 1 Here]
Table 2 presents summary statistics of all variables as well as the pairwise correlations
between them for our sample. Panel A reports the summary statistics of all variables. The
mean (median) of DISCLOSURE in our sample is -0.21(-0.16), consistent with the
empirical finding in Hutton, Marcus, and Tehranian (2009). The mean (median) of
NOISE is 75% (82%), which means that, on average, Chinese listing firm has 75% of its
tradable A-shares held by non-institutional investors, which explains why the Chinese
A-share market can be regarded as a typical noise-driven market. The mean (median) of
RES_INFO is -0.13 (-0.13). The mean of SOE is 0.69, i.e., 69% of the public firms in
Chinese A share market is state-owned.
[Insert Table 2 Here]
Panel B shows that the key variables of interest are correlated in the expected
directions. DISCLOSURE is positively correlated with the variable NOISE, negatively
with the price information efficiency measure, RES_INFO, and also negatively correlated
with investment performance measures Q and ROA. Those significant pairwise
correlations provide preliminary evidence that a high level of disclosure tends to attract
uninformed noise traders, thereby reducing the amount of price information that can be
learned by firm managers and hence harming their investment performance.
4. Empirical Results
4.1 Disclosure Attracts Noise Trading
Before turning to our main results, which link disclosure quality to informational and
allocative efficiency in the presence of noise trading, we pause to first illustrate the
17
relation between noise trading and disclosure, which is the assumption of our model. We
expect to see that firms with a high level of disclosure are associated with a high level of
noise trading. Specifically, we estimate the following equation:
NOISEit = 𝛼𝑡 + 𝜆𝑖 + β1 DISCLOSUREit + β2 SIZEit
+β3 SOEit + β4 LIQUIDITYit + εit ,
(19)
where 𝛼𝑡 and 𝜆𝑖 control year and firm-fixed effects. Since DISCLOSURE is computed
using accruals information in previous years, this equation partly captures the idea of
disclosure attracting subsequent noise trading.
[Insert Table 3 Here]
The estimation results are reported in Table 3. Indeed, in the Chinese A-share market, a
public firm with a high level of disclosure has heavy noise trading. NOISE is positively
correlated with DISCLOSURE, with the coefficient β1 estimated at 4.84, and is
significant at the 1% level. This result supports our assumption that disclosure attracts
noise trading.
4.2 Testing Prediction 1: Disclosure and Informational Efficiency
Our first prediction states that disclosure reduces the amount of information in the
price that can be learned by firms. To support our prediction, there should exist a negative
relation between the residual informativeness of stock price (RES_INFO) and disclosure
quality (DISCLOSURE), and this relation should be significantly stronger in those firms
with heavier noise trading.
We first employ regression analysis to examine the relation among information learned
by firm managers (RES_INFO), disclosure quality (DISCLOSURE), and noise trading
(NOISE). We test our prediction using the following three equations:
RES_INFOit = 𝛼𝑡 + 𝜆𝑖 + β1 DISCLOSUREit + εit ,
RES_INFOit = 𝛼𝑡 + 𝜆𝑖 + β1 DISCLOSUREit + γCONTROL𝑖𝑖 + εit ,
18
(20)
(21)
RES_INFOit = 𝛼𝑡 + 𝜆𝑖 + β1 DISCLOSUREit + β2 NOISEit
+β3 DISCLOSUREit ∗ NOISEit + γCONTROLit + εit ,
(22)
where 𝛼𝑡 and 𝜆𝑖 represent year and industry-fixed effect, CONTROL represents a set of
control variables, namely, the firm size (SIZE), debt ratio (LEVERAGE), Amihud
illiquidity ratio (LIQUIDITY), and a dummy variable indicating whether the firm is
state-owned (SOE) based on prior studies (Roll, 1988; Durnev, Morck, and Yeung, 2004;
Hutton, Marcus, and Tehranian, 2009; Wang, Liu, and Wu, 2009). The most interesting
coefficients are β1 in euqations (20)-(22) and β3 in equation (22).
[Insert Table 4 Here]
Table 4 reports the regression results. The coefficients for DISCLOSURE, β1, are
estimated at -0.17 and -0.13, in columns (1) and (2), which correspond to regression
equations (20) and (21), respectively. In both cases, the estimates are highly significant at
the 1% level. In column (3), we run equation (22) and re-examine the relation between
residual informativeness and disclosure, that is, we add variable NOISE and interaction
term DISCLOSURE*NOISE into the regression equation. We are especially interested in
β3 , the coefficient for DISCLOSURE*NOISE, which measures the effect of noise trading
on the sensitivity of residual informativeness to disclosure. As our theory predicts, β3 is
negative (-0.0081) and is significant at the 1% level. This suggests that the sensitivity of
residual price informativeness to disclosure is higher for firms with higher level of noise
trading. More interestingly, the coefficient of DISCLOSURE per se, β1 , becomes
significantly positive (0.4257). This result provides strong evidence for our premise that
disclosure harms informational efficiency by attracting uninformed noise traders.
The sign of the other control variables are largely consistent with prior studies (Roll,
1988; Durnev, Morck, and Yeung, 2004; Hutton, Marcus, and Tehranian, 2009; Wang,
Liu, and Wu, 2009). Specifically, large firms have high levels of informational efficiency;
a high analyst coverage causes more managerial information to be reflected in the stock
19
price and reduces the residual price informativeness; state-owned firms have less efficient
stock prices due to the monopoly role of state ownership; a firm’s debt ratio
(LEVERAGE) is significantly positively correlated with residual informativness of its
stock price.
Overall, Table 4 provides evidence supporting the first prediction of the model:
disclosure negatively affects the information in the price that can be learned by the firm
through attracting noisy trading.
4.3 Testing Prediction 2: Disclosure and Allocative Efficiency
Prior studies find that the information contained in the price helps to improve
investment decisions (Bond, Goldstein, and Prescott, 2010; Goldstein, Ozdenoren, and
Yuan, 2010). Therefore, if disclosure impedes information that managers can learn from
the price, it may eventually reduce investment performance. This is the second prediction
of the model: disclosure is expected to have a negative effect on the investment
performance of the firm and this negative effect is expected to be prominent in the
presence of heavy noise trading.
Before formally testing the second prediction which links disclosure and investment
performance, we first provide supporting evidence for the link between price information
and investment performance in the Chinese market. That is, we show that the residual
price informativeness (RES_INFO) has a positive effect on firms’ future investment
performance (Tobin’s Q and ROA) and this positive effect diminishes in the presence of
noise trading (NOISE). Specifically, we estimate the following equations:
Iit = 𝛼𝑡 + 𝜆𝑖 + β1 RES_INFOit + γCONTROL𝑖𝑖 + εit ,
Iit = 𝛼𝑡 + 𝜆𝑖 + β1 RES_INFOit + β2 NOISEit
+β3 RES_INFOit ∗ NOISEit + γCONTROL𝑖𝑖 + εit ,
20
(23)
(24)
where I𝑖𝑖 indicates firm i’s future investment performance, measured by Tobin’s Q and
ROA in the next year. 𝛼𝑡 and 𝜆𝑖 represent year and industry-fixed effect. CONTROL
represents the same set of control variables as in equations (21)-(22). Table 5 summarizes
the results.
[Insert Table 5 Here]
In columns (1) and (3), we run equation (23) and use Q and ROA as investment
performance measures, respectively. The coefficients of RES_INFO are estimated at
0.4420 and 0.0043, respectively, and both are significant at the 1% level, consistent with
the idea that the information that managers learn from the stock price helps to improve
investment performance. 16 In columns (2) and (4), we run equation (24) and find that the
coefficients on interaction term, RES_INFO*NOISE, are significantly negative at
-0.0052 and -0.0003, respectively, indicating that the sensitivity of investment
performance to residual price informativeness is weakened in the presence of noise
trading. Thus, Table 5 provides evidence that firms look into the stock price to guide their
investment decisions, which makes us comfortable formally test our prediction 2.
We next estimate the following equations to test prediction 2:
Iit = 𝛼𝑡 + 𝜆𝑖 + β1 DISCLOSUREit + γCONTROL𝑖𝑖 + εit ,
Iit = 𝛼𝑡 + 𝜆𝑖 + β1 DISCLOSUREit + β2 NOISEit
+β3 DISCLOSUREit ∗ NOISEit + γCONTROL𝑖𝑖 + εit .
(25)
(26)
These equations are similar to equations (23)-(24) expect that we replace RES_INFO
with DISCLOSURE. We are still interested in the coefficients β1 and β3.
[Insert Table 6 Here]
Panel A of Table 6 reports the results for the whole sample. Column (1) corresponds to
equation (25) when Tobin’s Q is used to measure investment performance. We can see
16
This is also consistent with Wurgler (2000) who shows that efficiency of capital allocation is positively correlated
with the amount of firm-specific information.
21
that Q is negatively correlated with DISCLOSURE, with the coefficient β1 estimated at
-0.1007 (p-value 0.1596). This result supports the model prediction that disclosure
reduces investment efficiency. Column (2) is the estimation result for equation (26),
whose regressors include the interaction term DISCLOSURE*NOISE. We find that the
coefficient β3 has the right sign and is significant: it is estimated at -0.0083 and
significant at the 1% level, indicating that the negative effect of disclosure on the Tobin’s
Q is stronger for firms with heavier noise trading. More interestingly, in column (2), the
coefficient of DISCLOSURE, β1 , becomes significantly positive, suggesting that
disclosure impedes allocative efficiency only through the channel of attracting noise
trading.
Columns (3) and (4) repeat the same analysis with the other investment performance
measure, ROA, and report evidence similar to those based on Tobin’s Q. In column (3),
ROA is negatively correlated with DISCLOSURE, with the coefficient β1 estimated at
-0.0240 (with a significant level less than the 1% level). In column (4), the coefficient for
the interaction term DISCLOSURE*NOISE, β3 , is negatively significant at -0.0008, and
the coefficient of DISCLOSURE, β1, turns significantly positive.
There is a vast literature documenting that state-ownership is negatively correlated
with allocative efficiency (e.g., Shleifer, 1988; Boardman and Vinning, 1989; Boubakri
and Cosset, 1998; Wurgler, 2000; Dewenter and Malatesta, 2001, Gupta, 2005; Dollar
and Wei, 2007; among many others). It is likely that the investment performance of
state-owned companies does not reflect the consequence of firms’ information-related
investment decisions, but instead, it is a result of their government ties or monopoly
power in the industry (Zhang and Ming, 2003; Wu, 2008). If this premise holds, our
results are expected to be weaker in state-owned firms because firm managers in those
firms will be less likely to look into the stock price to guide their investment decisions.
22
We test this conjecture by estimating equation (26) for state-owned firms and non-state
owned firms separately, and report the results in Panel B of Table 6.
Indeed, Panel B of Table 6 shows that the sensitivity of investment performance to
disclosure is much lower for state-owned firms than non-state-owned firms: the
coefficients for the interaction term DISCLOSURE*NOISE, β3 , are insignificant and
marginally significant for state-owned companies in columns (1) and (2), but are
negatively significant at less than the 1% level and have a larger magnitude for
non-state-owned companies in columns (3) and (4).
Collectively, those results support our second model prediction that the informational
inefficiency consequence of disclosure can be transformed into allocative inefficiency
consequence in the presence of an information feedback mechanism.
4.4 Robustness of Disclosure Measure
Our results are robust to different proxies of disclosure quality. We follow Dichev and
Tang (2009) and Gow, Taylor, and Verrechia (2011) to construct alternative disclosure
measures using the precision of a firm’s earnings, which is defined as the inverse of the
standard deviation of the time-series of annual earnings over the prior three-year or
five-year period scaled by the average market value, denoted as PRECISION3 or
PRECISION5.
[Insert Table 7 Here]
Table 7 reports the results for these measures. Panel A repeats the same analysis as
Table 3 and shows that noise trading (NOISE) is significantly positively correlated with
the alternative disclosure measures, PRECISION3 and PRECISION5, consistent with the
idea that noise trading chases disclosure. In Panels B and C, we re-run equations (22) and
(26), respectively, using PRECISION3 and PRECISION5 instead of DISCLOSURE, and
find that the results are qualitatively similar. For example, the coefficient on the
23
interaction term in column (1), PRECISION3*NOISE, has the negative sign in both
panels as we expected and is significant at less than the 1% level, indicating disclosure
deteriorates informational and allocative efficiency by attracting noise traders whose
trading is uninformed.
Overall, the results presented in the above tables strongly support our model
predictions in Section 2 and convey an important message: the classic view that
disclosure helps price discovery and improves informational efficiency may be limited to
well-developed and institutionalized markets, and for those under-institutionalized
emerging markets, the efficiency-improving effect can be significantly distorted.
5. Conclusion
A more efficient stock market can improve resource allocation in the real economy
because firm managers can infer valuable information about prospective investment
projects from the stock prices. Therefore, financial reporting or disclosure quality, as one
of the key determinants of price informativeness, has direct economic efficiency
implications.
In this paper, we provide a theoretical model and empirical evidence on informational
and allocative efficiency consequence of disclosure. In our model, noise traders chase
disclosure and dilute the private information from informed speculators, which weakens
the informational role of stock prices and therefore harms investment efficiency through
managers’ learning. Using a sample from the Chinese market, a large emerging financial
market dominated by noise traders, we find evidence in support of these efficiency
predictions of disclosure. Specifically, we find that a firm’s financial reporting quality is
significantly negatively associated with the amount of information that managers can
learn from the stock price. Firm’s investment performance (Tobin’s Q, and ROA)
decreases with the amount of disclosure. In addition, the negative effects of disclosure on
24
informational efficiency and investment efficiency are stronger among firms with more
noise trading.
Our study contributes to three economic literatures, which, respectively, study
disclosure, noise trading, and corporate finance. By incorporating feedback effects into a
model where noise traders chase disclosure, we demonstrate that the economic
consequence of disclosure can be negative, which is new to the disclosure literature. Our
analysis suggests that noise traders play an important role not only in security price
discovery but also in real investment decisions.
The findings of our paper are particularly useful for under-institutionalized emerging
markets dominated by noise traders. In these markets, policy makers should not blindly
push for more transparency before the financial market becomes more mature and
institutionalized. As long as there is a large group of noise traders whose behavior are
affected by disclosure, regulatory policies aiming at enhancing financial reporting could
have unintended negative effects on stock market efficiency and real economy.
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29
Table 1
Variable Definitions
Variables
Definition
Main Variables
DISCLOSURE
Minus of financial reporting opacity as in Hutton, Marcus, and Tehranian (2009),
which is the moving sum of the absolute value of annual discretionary accruals of
the previous three years, where discretionary accruals are calculated using the
modified Jones (1991) model as in Dechow, Sloan, and Sweeney (1995).
RES_INFO
Residual informativeness, which is the residual of regressing price
nonsynchronicity on earnings surprise, where price nonsynchronicity is calculated
as ln((1 − R2 )/R2 ) and R2 is the R-squre in the regression from firm returns
on market and industry returns, as in Morck, Yeung, and Yu (2000).
Operating earnings (i.e., earnings before interest, taxes, depreciation, and
amortization) as a percentage of book value of asset of next year.
ROA
Q
NOISE
Market value of tradable A shares, tradable B shares, and H shares plus book
value of non-tradable A shares plus book value of debt, scaled by book value of
asset.
One minus percentage of shares held by institutional investors; i.e., retail investor
ownership.
Other Variables
SIZE
Logarithm of market capitalization.
LEVERAGE
Book value of total debt scaled by book value of total asset.
ANALYST
SOE
Number of analysts issuing forecasts of the firm.
Equals to 1 if the final controller of an enterprise is the state or a state-owned
company, and equals to 0 otherwise.
LIQUIDITY
Logarithm of Amihud illiquidity ratio as in Amihud (2002).
PRECISION3
Earnings precision, which is the inverse of the standard deviation of the
time-series of annual earnings over the prior three-year period scaled by average
market value as in Gow, Taylor, and Verrecchia (2011).
Earnings precision, which is the inverse of the standard deviation of the
time-series of annual earnings over the prior five-year period scaled by average
market value as in Gow, Taylor, and Verrecchia (2011).
R2 from regressing daily return on market and industry index over year t.
PRECISION5
R2
Earnings Surprise
Average of the absolute abnormal returns (market adjusted) in the three-day
period centering on each of the four quarterly earnings announcement dates.
30
Table 2
Summary Statistics
This table reports key statistics of 1,478 distinct Chinese A-share stocks during the period from 2004 to
2010. DISCLOSURE is minus of financial reporting opacity as in Hutton, Marcus, and Tehranian (2009),
which is the moving sum of the absolute value of annual discretionary accruals of the previous three years,
where discretionary accruals are calculated using the modified Jones (1991) model as in Dechow, Sloan,
and Sweeney (1995). RES_INFO is the residual informativeness, which is the residual of regressing price
nonsynchronicity on earnings surprise, where price nonsynchronicity is calculated as ln((1 − R2 )/R2 ), as
in Morck, Yeung, and Yu (2000) and where earnings surprise is the average of the absolute abnormal
returns (market adjusted) in the three-day period centering on each of the four quarterly earnings
announcement dates. ROA is operating earnings (i.e., earnings before interest, taxes, depreciation, and
amortization) as a percentage of book value of asset of next year. Q is market value of tradable A shares,
tradable B shares, and H shares plus book value of non-tradable A shares plus book value of debt, scaled by
book value of asset. SIZE is the logarithm of market capitalization. LEVERAGE is the book value of total
debt scaled by book value of total asset. ANALYST is the number of analysts issuing forecasts of the firm.
SOE is a dummy variable, which equals to 1 if the final controller of an enterprise is the state or a
state-owned company, and equals to 0 otherwise. NOISE equals to one minus percentage of shares held by
institutional investors; i.e., retail investor ownership. PRECISION3 is the inverse of the standard deviation
of the time-series of annual earnings over the prior three-year period scaled by average market value (we
exclude observations with less than three annual earnings over their prior three-year periods).
PRECISION5 is the inverse of the standard deviation of the time-series of annual earnings over the prior
five-year period scaled by average market value (we exclude observations with less than three annual
earnings over their prior three-year periods). LIQUIDITY is logarithm of Amihud illiquidity ratio as in
Amihud (2002). *, ** and *** indicate significance at 10%, 5%, and 1% levels, respectively.
Panel A: Summary Statistics
Variable
MEAN
SD
5%
25%
50%
75%
95%
DISCLOSURE
-0.21
0.17
-0.55
-0.26
-0.16
-0.10
-0.05
RES_INFO
-0.13
0.68
-1.26
-0.54
-0.13
0.27
0.97
ROA
0.03
0.08
-0.08
0.01
0.03
0.07
0.15
Q
1.91
1.20
0.95
1.13
1.51
2.21
4.36
NOISE
74.61
23.92
28.61
57.15
81.50
96.32
99.93
SIZE
21.89
1.13
20.29
21.08
21.77
22.56
23.95
LEVERAGE
0.52
0.21
0.19
0.38
0.52
0.64
0.79
ANALYST
14.34
27.09
0
0
3
16
70
SOE
0.69
0.46
0
0
1
1
1
LIQUIDITY
-20.70
1.37
-22.8
-21.7
-20.83
-19.60
-18.30
PRECISION3
160.67
226.00
12.25
39.31
85.52
181.10
568.20
PRECISION5
94.10
101.81
12.00
30.07
60.44
117.29
290.01
0.48
0.15
0.23
0.38
0.48
0.58
0.74
-0.01
0.03
-0.05
-0.02
-0.01
0.01
0.04
R
2
Earnings Surprise
31
Table 2 (Cont.)
Panel B: Pearson Correlation Matrix
DISCLOSURE
RES_INFO
ROA
Q
NOISE
SIZE
DISCLOSURE
RES_INFO
1.000
-0.003
1.000
ROA
Q
NOISE
SIZE
LEVERAGE
ANALYST
SOE
LIQUID
-0.043***
-0.017
0.110***
-0.037***
0.110***
0.124***
1.000
0.257***
1.000
-0.053***
-0.141***
-0.070***
0.103***
-0.201***
0.069***
-0.278***
-0.072***
-0.331***
0.302***
-0.241***
0.271***
-0.317***
0.093***
-0.177***
1.000
-0.591***
1.000
LEVERAGE
ANALYST
SOE
LIQUIDITY
PRECISION3
PRECISION3
PRECISION5
0.060***
0.101***
0.138***
0.254***
0.039***
0.069***
-0.012
-0.210***
-0.043***
-0.027**
0.155***
-0.137***
-0.205***
0.030***
-0.007
0.033***
-0.529***
-0.045***
0.519***
0.082***
0.130***
-0.071***
0.603***
0.129***
-0.800***
-0.112***
-0.147***
1.000
-0.037***
0.023**
0.045***
-0.104***
-0.128***
1.000
0.039***
-0.507***
-0.087***
-0.127***
1.000
-0.035***
0.040***
0.058***
1.000
0.129***
0.187***
1.000
0.607***
1.000
PRECISION5
32
Table 3
Disclosure and Noisy Trading
This table provides evidence that noise trading chases disclosure. DISCLOSURE is minus of financial
reporting opacity as in Hutton, Marcus, and Tehranian (2009), which is the moving sum of the absolute
value of annual discretionary accruals of the previous three years, where discretionary accruals are
calculated using the modified Jones Model as in Dechow, Sloan, and Sweeney (1995). NOISE is one minus
percentage of shares held by institutional investors. SIZE is the logarithm of market capitalization. SOE is a
dummy variable, which equals to 1 if the final controller of an enterprise is the state or a state-owned
company and equals to 0 otherwise. LIQUIDITY is the logarithm of Amihud illiquidity ratio as in Amihud
(2002). We control for fixed-year effects and industry effects. P-values of the two-tailed test are reported in
the parentheses. *, ** and *** indicate significance at 10%, 5%, and 1% levels, respectively.
Dependent Variable
NOISE
INTERCEPT
262.3055***
(<0.001)
4.8433***
DISCLOSURE
(<0.001)
SIZE
-14.3015***
(<0.001)
SOE
-0.7832*
(0.074)
LIQUIDITY
-5.5113***
(<0.001)
Year-fixed effect
√
Industry-fixed effect
√
No of Obs
7649
Within R-square
0.5068
Ad R-square
0.5048
33
Table 4
Disclosure and Residual Informativeness
This table provides evidence that disclosure reduces the amount of price information that can be learned by
the firm manager. DISCLOSURE is minus of financial reporting opacity as in Hutton, Marcus, and
Tehranian (2009), which is the moving sum of the absolute value of annual discretionary accruals of the
previous three years where discretionary accruals are calculated according to Dechow, Sloan, and Sweeney
(1995). RES_INFO is the residual informativeness, which is the residual of regressing price
nonsynchronicity on earnings surprise, where price nonsynchronicity is calculated as ln((1 − R2 )/R2 ), as
in Morck, Yeung, and Yu (2000), and where earnings surprise is the average of the absolute abnormal
returns (market adjusted) in the three-day period centering on each of the four quarterly earnings
announcement dates. ANALYST is the number of analysts issuing forecasts of the firm. NOISE is one
minus percentage of shares held by institutional investors; i.e., retail investor ownership. SIZE is the
logarithm of market capitalization. LEVERAGE is the book value of total debt scaled by the book value of
total asset. SOE is a dummy variable, which equals to 1 if the final controller of an enterprise is the state or
a state-owned company, and equals to 0 otherwise. We control for fixed-year effects and industry effects.
P-values of the two-tailed test are reported in the parentheses. *, ** and *** indicate significance at 10%,
5%, and 1% levels, respectively.
Dependent Variable
Residual Informativeness (RES_INFO)
INTERCEPT
DISCLOSURE
(1)
(2)
(3)
-0.1655***
2.5826***
3.2413***
(<0.001)
(<0.001)
(<0.001)
-0.1628***
-0.1330***
0.4257***
(<0.001)
(<0.001)
(<0.001)
NOISE
-0.0043***
(<0.001)
DISCLOSURE * NOISE
-0.0081***
(<0.001)
ANALYST
SIZE
LEVERAGE
SOE
LIQUIDITY
-0.0039***
-0.0042***
(<0.001)
(<0.001)
0.1126***
0.0815***
(<0.001)
(<0.001)
0.1709***
0.1684***
(<0.001)
(<0.001)
-0.0949***
-0.0939***
(<0.001)
(<0.001)
0.2400***
0.2253***
(<0.001)
(<0.001)
Year-fixed effect
√
√
√
Industry-fixed effect
√
√
√
No of Obs
7649
7649
7649
Within R-square
0.3353
0.4219
0.4284
Ad R-square
0.3327
0.4192
0.4256
34
Table 5
Investment Performance and Residual Informativeness
This table provides evidence that the price information can improve investment performance. RES_INFO is
the residual informativeness, which is the residual of regressing price nonsynchronicity on earnings
surprise, where price nonsynchronicity is calculated as ln((1 − R2 )/R2 ), as in Morck, Yeung, and Yu
(2000), and where earnings surprise is the average of the absolute abnormal returns (market adjusted) in the
three-day period centering on each of the four quarterly earnings announcement dates. Q is market statistics
of equity plus book value of asset minus book value of equity, scaled by book value of assets. ROA is
operating earnings as a percentage of book value of asset of next year. NOISE is one minus percentage of
shares held by institutional investors; i.e., retail investor ownership. SIZE is the logarithm of market
capitalization. LEVERAGE is the book value of total debt scaled by the book value of total asset. SOE is a
dummy variable, which equals to 1 if the final controller of an enterprise is the state or a state-owned
company, and equals to 0 otherwise. We control for fixed-year effects and industry effects. P-values of the
two-tailed test are reported in the parentheses. *, ** and *** indicate significance at 10%, 5%, and 1%
levels, respectively.
Dependent Variable
INTERCEPT
RES_INFO
Q
(1)
(2)
(3)
(4)
1.8895***
4.7484***
-0.4524***
-0.2239***
(<0.001)
(<0.001)
(<0.001)
(<0.001)
0.4420***
0.7936***
0.0043***
0.0224***
(<0.001)
(<0.001)
(0.0053)
(<0.001)
NOISE
RES_INFO * NOISE
SIZE
ROA
-0.0105***
-0.0008***
(<0.001)
(<0.001)
-0.0052***
-0.0003***
(<0.001)
(<0.001)
0.0579***
-0.0413***
0.0253***
0.0167***
(<0.001)
(0.0033)
(<0.001)
(<0.001)
-1.1832***
-1.1572***
-0.0843***
-0.0833***
(<0.001)
(<0.001)
(<0.001)
(<0.001)
-0.1838***
-0.1972***
-0.0103***
-0.0106***
(<0.001)
(<0.001)
(<0.001)
(<0.001)
Year-fixed effect
√
√
√
√
Industry-fixed effect
√
√
√
√
No of Obs
7649
7649
6229
6229
Within R-square
0.3726
0.3970
0.2192
0.2515
Ad R-square
0.3699
0.3942
0.2150
0.2473
LEVERAGE
SOE
35
Table 6
Disclosure and Investment Performance
This table provides evidence that disclosure can harm investment efficiency. DISCLOSURE is minus of
financial reporting opacity as in Hutton, Marcus, and Tehranian (2009), which is the moving sum of the
absolute value of annual discretionary accruals of the previous three years, where discretionary accruals are
calculated using modified the Jones Model as in Dechow, Sloan, and Sweeney (1995). Q is market statistics
of equity plus book value of asset minus book value of equity, scaled by book value of assets. ROA is
operating earnings (i.e., earnings before interest, taxes, depreciation, and amortization) as a percentage of
book value of asset of next year. NOISE is one minus percentage of shares held by institutional investors;
i.e., retail investor ownership. SIZE is the logarithm of market capitalization. LEVERAGE is the book
value of total debt scaled by book value of total asset. SOE is a dummy variable, which equals to 1 if the
final controller of an enterprise is the state or a state-owned company, and equals to 0 otherwise. We control
for fixed-year effects and industry effects. P-values of the two-tailed test are reported in the parentheses. *,
** and *** indicate significance at 10%, 5%, and 1% levels, respectively.
Panel A: Disclosure and Investment Performance-Full Sample
Dependent Variable
INTERCEPT
DISCLOSURE
Q
(1)
(2)
(3)
(4)
2.9137***
6.3995***
-0.4433***
-0.2066***
(<0.001)
(<0.001)
(<0.001)
(<0.001)
-0.1007
0.5302***
-0.0240***
0.0402***
(0.1596)
(0.005)
(<0.001)
(0.0057)
NOISE
DISCLOSURE * NOISE
SIZE
ROA
-0.0131***
-0.0010***
(<0.001)
(<0.001)
-0.0083***
-0.0008***
(0.0012)
(<0.001)
0.0047
-0.1135***
0.0247***
0.0163***
(0.7119)
(<0.001)
(<0.001)
(<0.001)
-1.0213***
-1.0317***
-0.0859***
-0.0861***
(<0.001)
(<0.001)
(<0.001)
(<0.001)
-0.2196***
-0.2251***
-0.0101***
-0.0097***
(<0.001)
(<0.001)
(<0.001)
(<0.001)
Year-fixed effect
√
√
√
√
Industry-fixed effect
√
√
√
√
No of Obs
7659
7659
6229
6229
Within R-square
0.3282
0.3545
0.2207
0.2518
Ad R-square
0.3253
0.3516
0.2165
0.2476
LEVERAGE
SOE
36
Table 6 (Cont.)
Panel B: Disclosure and Investment Performance-Sorted by SOE
SOE
Non-SOE
Dependent Variable
Q
(1)
ROA
(2)
Q
(3)
ROA
(4)
INTERCEPT
5.4708***
-0.1744***
8.6941***
-0.3449***
(<0.001)
(<0.001)
(<0.001)
(<0.001)
0.2150
0.0159
1.7441***
0.1213***
(0.2828)
(0.3185)
(<0.001)
(<0.001)
-0.0109***
-0.0008***
-0.0211***
-0.0015***
(<0.001)
(<0.001)
(<0.001)
(<0.001)
-0.0045
-0.0005*
-0.0214***
-0.0019***
(0.1064)
(0.081)
(<0.001)
(<0.001)
-0.0915***
0.0149***
-0.1804***
0.0219***
(<0.001)
(<0.001)
(<0.001)
(<0.001)
-1.0512***
-0.0939***
-1.0644***
-0.0596***
(<0.001)
(<0.001)
(<0.001)
(<0.001)
Year-fixed effect
√
√
√
√
Industry-fixed effect
√
√
√
√
No of Obs
5294
4388
2355
1841
Within R-square
0.3490
0.2776
0.3967
0.2641
Ad R-square
0.3449
0.2722
0.3881
0.2506
DISCLOSURE
NOISE
DISCLOSURE * NOISE
SIZE
LEVERAGE
37
Table 7
Robustness Check
This table uses alternative disclosure measures to repeat the analysis in Tables 3, 4, and 6. PRECISION3 is
the inverse of the standard deviation of the time-series of annual earnings over the prior three-year period
scaled by average market value (we exclude observations with less than three annual earnings over their
prior three-year periods). PRECISION5 is the inverse of the standard deviation of the time-series of annual
earnings over the prior five-year period scaled by average market value (we exclude observations with less
than three annual earnings over their prior three-year periods). RES_INFO is the residual informativeness,
which is the residual of regressing price nonsynchronicity on earnings surprise, where price
nonsynchronicity is calculated as ln((1 − R2 )/R2 ), as in Morck, Yeung, and Yu (2000), and where
earnings surprise is the average of the absolute abnormal returns (market adjusted) in the three-day period
centering on each of the four quarterly earnings announcement dates. ANALYST is the number of analysts
issuing forecasts of the firm. NOISE is one minus percentage of shares held by institutional investors; i.e.,
retail investor ownership. Q is market statistics of equity plus book value of asset minus book value of
equity, scaled by book value of assets. ROA is operating earnings (i.e., earnings before interest, taxes,
depreciation, and amortization) as a percentage of book value of asset of next year. SIZE is the logarithm of
market capitalization. LEVERAGE is the book value of total debt scaled by book value of total asset. SOE
is a dummy variable, which equals to 1 if the final controller of an enterprise is the state or a state-owned
company, and equals to 0 otherwise. LIQUIDITY is Amihud illiquidity ratio as in Amihud (2002). We
control for fixed-year effects and industry effects. P-values of the two-tailed test are reported in the
parentheses. *, ** and *** indicate significance at 10%, 5%, and 1% levels, respectively.
Panel A: Noise Trading and Disclosure
Dependent Variable
NOISE
INTERCEPT
PRECISION3
(1)
(2)
259.6852***
259.7326***
(<0.001)
(<0.001)
0.0028***
(0.0011)
0.0040*
PRECISION5
(0.0551)
SIZE
SOE
LIQUIDITY
Year-fixed effect
Industry-fixed effect
-14.1487***
-13.9830***
(<0.001)
(<0.001)
-0.6717
-0.9980**
(0.1225)
(0.033)
-5.4020***
-5.2341***
(<0.001)
(<0.001)
√
√
√
√
No of Obs
7740
6668
Within R-square
0.5070
0.5089
Ad R-square
0.5050
0.5066
38
Table 7 (Cont.)
Panel B: Disclosure and Informational Efficiency
Dependent Variable
INTERCEPT
PRECISION3
Residual Informativeness (RES_INFO)
(1)
(2)
3.1350***
3.2494***
(<0.001)
(<0.001)
0.0002*
(0.0591)
PRECISION5
0.0009***
(<0.001)
NOISE
PRECISION3* NOISE
-0.0022***
-0.0013***
(<0.001)
(0.0046)
-2.3E-06**
(0.05)
-1E-05***
PRECISION5* NOISE
(<0.001)
ANALYST
-0.0042***
-0.0040***
(<0.001)
(<0.001)
0.0859***
0.0895***
(<0.001)
(<0.001)
0.1703***
0.1656***
(<0.001)
(<0.001)
-0.0974***
-0.1043***
(<0.001)
(<0.001)
0.2303***
0.2423***
(<0.001)
(<0.001)
Year-fixed effect
√
√
Industry-fixed effect
√
√
No of Obs
7740
6754
Within R-square
0.4235
0.4312
Ad R-square
0.4207
0.4281
SIZE
LEVERAGE
SOE
LIQUIDITY
39
Table 7 (Cont.)
Panel C: Disclosure and Allocative Efficiency
Dependent Variable
INTERCEPT
PRECISION3
Q
(1)
(2)
5.9595***
6.0003***
(<0.001)
(<0.001)
0.0013***
(<0.001)
PRECISION5
0.0028***
(<0.001)
NOISE
PRECISION3 * NOISE
-0.0094***
-0.0082***
(<0.001)
(<0.001)
-1.3E-05***
(<0.001)
PRECISION5 * NOISE
-3E-05***
(<0.001)
SIZE
-0.1052***
-0.1145***
(<0.001)
(<0.001)
-1.1091***
-1.0416***
(<0.001)
(<0.001)
-0.2367***
-0.235***
(<0.001)
(<0.001)
Year-fixed effect
√
√
Industry-fixed effect
√
√
No of Obs
7735
6749
Within R-square
0.3633
0.3510
Ad R-square
0.3604
0.3477
LEVERAGE
SOE
40