peress competition

8/2/2019 Peress Competition

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THE JOURNAL OF FINANCE VOL. LXV, NO. 1 FEBRUARY 2010

Product Market Competition, Insider Trading,and Stock Market Efciency

JOEL PERESS

ABSTRACT

How does competition in rms product markets inuence their behaviorin equity

markets? Do product market imperfections spread to equity markets? Weexamine

these questions in a noisy rational expectations model in which rms operate under

monopolistic competition while their shares trade in perfectly competitive markets.

Firms use their monopoly power to pass on shocks to customers, thereby insulating

their prots. This encourages stock trading, expedites the capitalization of private in-

formation into stock prices and improves the allocation of capital. Sever

al implicationsare derived and tested.

HOW DOES COMPETITION in rms product markets inuence their behavior inequity markets? Do product market imperfections spread to equity markets?These questions are increasingly of interest as product markets are becomingmore competitive in many countries thanks to the relaxation of impedimentsto trade and barriers to entry.1 In this paper, we analyze these questions using

a noisy rational expectations model in which rms operate under monopolisticcompetition while their shares trade in perfectly competitive markets.

The model is guided by recent empirical work showing that stock re

turnsare affected by the intensity of product market competition. Gaspar and Massa(2005) and Irvine and Pontiff (2009) document that more competitive rmshave more volatile idiosyncratic returns, and Hou and Robinson (2006) showthat such rms earn higher risk-adjusted returns. The model is also guided bya direct examination of the data. Our starting point is the nding in Gasparand Massa (2005) that analysts earnings forecasts about rms operating inmore competitive industries are more dispersed. Since differences in opinions

Joel Peress is with INSEAD, Department of Finance. I thank for helpful comments Patrick

Bolton, Philip Bond, Markus Brunnermeier, Murillo Campello, James Dow, Bernard Dumas,Lily Fang, Michael Fishman, Denis Gromb, Charles Jones, Massimo Massa,Jacques Olivier,

Jose Scheinkman, David Thesmar, Laura Veldkamp, Bernard Yeung, and seminar participantsat Columbia Business School, HEC Paris, NYU Stern School of Business, PrincetonUniversity,


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HEC Lausanne, the 2007 WFA meeting, Goldman Sachs Asset Management, the 2006 CEPR Sum-mer Symposium in Financial Markets, the 2007 Adam Smith Asset Pricingmeeting, the 2008European Winter Finance Conference (Klosters), and the Caesarea Center 5th Annual AcademicConference. I am also grateful to an anonymous referee and the editor, CampbellHarvey, for manyinsightful comments and detailed suggestions.

1Such changes may have an impact on equity markets. For example, the rise inidiosyncratic

return volatility (Morck, Yeung, and Yu (2000), Campbell et al. (2001)) may be related to thederegulation of the economy (Gaspar and Massa (2005), Irvine and Pontiff (2009)).

1

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2 The Journal of FinanceR

1

.

0

re 1v .o -nruT

2 .-

3.

-

Weak market power 23 4 Strong market power

Figure 1. Market power and turnover. This gure shows stock turnover across market power

groups. Turnover is dened as the log of the ratio of the number of shares tradedduring a yearto the number of shares outstanding. Firms are sorted every year from 1996 to 2005 into marketpower quintiles. Market power is measured as the excess pricecost margin (PCM). The PCM orLerner index is dened as operating prots (before depreciation, interest, special items, and taxes)over sales (Compustat annual data item 12). Operating prots are obtained by subtracting from


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sales the cost of goods sold (item 41) and general and administrative expenses (item 178). If dataare missing, we use operating income (item 178). The excess pricecost margin is constructed asthe difference between the rms PCM and the PCM of its industry. The industry PCM is the value-weighted average PCM across rms in the industry where the weights are based on market share(sales over total industry sales) and industries are dened using two-digit SIC classications.

are usually a motivation for trading, we expect to nd a greater volume of tradefor these rms. To analyze this conjecture, we sort rms on their market powerand measure the average trading volume in each group.2 As Figure 1 shows, we

nd the opposite of our conjecture. Stocks in the bottom market power quintileare traded less frequently than those in the top quintile. A possible explana-tion for the mismatch between belief heterogeneity and trading volume is thatthe opinions examined in Gaspar and Massa (2005)analysts forecastsarenot representative of the overall market but of informed investors who tradedifferently. We explore this possibility by studying trades initiated by insidersofcers of rms who presumably have

access to privileged information.

Figure 2, in the spirit of Figure 1, reveals that their trading volume is againlarger in rms with more market power. Thus, it appears that investors scale

2Our data and methodology are described in Section V, where we conrm that theresults we

presented graphically in this Introduction are statistically signicant and robustto the inclusionof other factors including rm size.

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Product Market Competition and Stock Market Efciency

3

800.

r 6e 0v 0o .nr 4u 0

tr 0e .

di 2sn 0

I 0.


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0Weak mkt power 2 3 4

Strong mkt power

3.

se

dar

tr 2e .

disn

i

fo 1

.r

ebmu

N 0Weak mkt power 2 3 4

Strong mkt power

Figure 2. Market power and insider trading. This gure shows insider trading activity acrossmarket power groups over the 1996 to 2005 period. In the top panel, insider trading activity ismeasured as the log of the ratio of a rms annual total insider trading dollar volu

me to the rmsmarket capitalization, and it is denoted Insider turnover. In the bottom panel,it is measured as thelog of the ratio of the rms annual number of insider trades to the rms number of acive insidersand is denoted Number of insider trades. Insider trades are open market transactions, excludingsells, initiated by the top ve executives of a rm (CEO, CFO, COO, President, and Chairman ofboard). Active insiders are dened as executives who have reported at least one transaction in anyof the sample years. Firms are sorted every year from 1996 to 2005 into market power quintiles.

Market power is measured as the excess pricecost margin or Lerner index (see Figure 1).

down their trading of more competitive stocks even when they have superiorinformation.

The enhanced trading activity, especially among informed investors, for rmsenjoying more market power raises the possibility that fundamental infor-mation is more quickly capitalized into their stock price. To investigate this


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hypothesis, we measure the stock price reaction to earnings announcementsacross market power groups. Earnings of closely followed rms are anticipatedlong before their ofcial release so their prices do not react to announcements.In contrast, announcements by remotely followed rms provide useful infor-mation that causes investors to revise their valuation and stock prices to ad-just. Figure 3 shows that rms with more market power experience smallerprice changes at announcements after controlling for standard risk factors,

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8.

6.

nrut

er

tneve 4. .nba

.sbA

2.

0Weak market power 2 3

4 Strong market power

Figure 3. Market power and stock price informativeness. This gure

shows stock priceinformativeness across market power groups. Informativeness is measured as the absolute abnor-mal return surrounding an earnings announcement. Abnormal returns are the residuals from theFamaFrench three-factor model. For every rm, we regress stock returns on the market, size, andbook-to-market factors over an estimation window extending from t = 250 to t = 5 relative tothe earnings announcement day 0. We estimate the residuals over an event window


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ranging fromt = 2 to t = +2. Then, we sum their absolute value on each day of the event window to measurethe stock price reaction to the announcement. Finally, we average the absolute abnormal returnsestimates obtained from each announcement during a year to get an annual measure. Firms aresorted every year from 1996 to 2005 into market power quintiles. Market power ismeasured asthe excess pricecost margin or Lerner index (see Figure 1).

suggesting that their prices are more informative. This is consistent with ourprevious nding that insiders in these rms trade more aggressively, whichspeeds up the incorporation of information into prices.3 To summarize the evi

dence, monopoly power in product markets reduces the dispersion of earningsforecasts (Gaspar and Massa (2005)) but stimulates trading, including that byinsiders, and enhances the informativeness of stock prices (Figures 13). In ad

dition, it lowers risk adjusted expected returns (Hou and Robinson (2006)) andidiosyncratic return volatility (Gaspar and Massa (2005), Irvine and Pontiff(2009), Chun et al. (2008)).

The contribution of this paper is to present a rational expectatio

ns modelthat explains these observations and provides further insights into how productmarket competition interacts with information asymmetries. Ours is similar

3In a similar vein, Hoberg and Phillips (2009) document that, in less competitive industries,

analyst forecasts are less positively biased and stock returns comove less withthe market.

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Product Market Competition and Stock Market Efciency

5

to most models of trading under asymmetric information in competitive stockmarkets (e.g., Grossman and Stiglitz (1980)) but for one difference: Our rmssell goods in an imperfectly competitive product market. Specically, they oper

ate under monopolistic competition. Each rm owns a unique patent to producea good, the demand for which is imperfectly elastic, so the rm enjoys somemarket power. Firms are subject to random productivity shocks. Investors donot observe these shocks but are endowed with private information. Tradingcauses private information to be reected in stock prices but only partiallybecause noise precludes their full revelation. Product market power plays an

important role in an uncertain environment. It allows rms to insulate theirprots from shocks by passing the shocks on to their customers. Firmsthatface a captive demand for their good can hedge their prots effectively. Butmore competitive rms yield more risky prots, even though they face thesame amount of technological uncertainty (the variance of productivity shocksis independent of the degree of competition). As Hicks (1935 p. 8) puts it, thebest of all monopoly prots is a quiet life. This insight drives our results.

Because the prots of rms with more market power are less risky, investors


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trade their stock in larger quantities (even though their private signals aboutproductivity are just as accurate). These larger trades, in turn, expedite the in

corporation of private information into prices. The improved accuracy of publicinformationstock prices are more informativefurther encourages investorsto trade. It also makes their prot and productivity forecasts less dispersedas they rely more on public information and less on their own private sig

nals. Thus, investors disagree less but trade more, and stock prices are moreinformative, in line with the evidence presented above.4

Furthermore, rms with more market power have less volatile, and on aver

age lower returns. These effects obtain in imperfect competition models regard

less of information asymmetries simply because their prots are less risky. Thenovel aspect emphasized here is that monopoly power also exerts an indirect in

uence through the informativeness of prices, which further reduces volatilityand expected returns, even after adjusting for risk. Indeed, stock prices of moremonopolistic rms track future prots more closely, allowing returns to absorba smaller fraction of shocks.5 The ratio of expected excess returns to their stan

dard deviationa measure of expected returns adjusted for risk, known as the

Sharpe ratiois reduced too, indicating that the informational effect of mar

ket power is stronger on the risk premium than on risk. These results suggestthat product market deregulation amplies return volatility not only becauseit deprives rms from a hedge but also because public information, conveyedby stock prices, is less accurate.6

4This nding may explain why stock picking appears to be declining in the United States since

the 1960s (Bhattacharya and Galpin (2005)) as competition in product markets intensies (Gasparand Massa (2005), Irvine and Pontiff (2009), Chun et al. (2008)).

5When information is perfect, for example, prices reect technology sh

ocks perfectly while

returns equal the risk

free rate.6These ndings are consistent with the dramatic increases in idiosyncr

atic return volatility

(Morck et al. (2000), Campbell et al. (2001)) that occurred in the United States following thederegulation of product markets (Gaspar and Massa (2005), Irvine and Pontiff (2005), Chun et al.

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The effects considered so far are essentially nancialthey involve trades,prices, and returns. An important contribution of the paper is to show that theyextend to real variables when rms raise new capital. In that case, investors notonly value rms, but also determine how much capital rms are to receive. Wend that fresh capitalthe proceeds from share issuancesis more efcientlydistributed when rms have more market power. The reason is that theirstock prices are more informative so investors can easily identify the


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bettertechnologies to channel more funds to. In other words, informational efciencyfeeds back to real efciency. Thus, competition, rather than the lack thereof,generates an inefciency when it interacts with information asymmetries. Thatis, product market imperfections, rather than spreading to equity markets, tendto limit stock market imperfections.7

Our paper relates to several important strands of literature. It ispart of

the research agenda that links industrial organization to nancial markets.Starting with the work of Titman (1984) and Brander and Lewis (1986), schol

ars have established, both theoretically and empirically, that rms capitalstructure and the intensity of competition in rms product market are jointlydetermined.8 In particular, debt can be used strategically to relax

informa

tional constraints. In Poitevin (1989), for example, debt signals to investorsthat a rm entering a market dominated by a monopoly has high value, whilein Chemla and Faure

Grimaud (2001) it induces buyers with a high valuationto reveal their type to a durable good monopolist.

Less is known about how other nancial variables such as trading volume

and the informativeness of stock prices are related to market power. Perottiand von Thadden (2003) argue that a rms dominant investors can limit theinformativeness of its stock price by being opaque, which in turn mitigatesproduct market competition. In Stoughton, Wong and Zechner (2001), con

sumers infer product quality from the stock price, so a high qualityentranthas an incentive to go public to expose itself to speculators attention. Tookes(2007) is most closely related to our work. She examines trading and informa

tion spillovers across competing stocks and shows that informed agents preferto trade shares in a more competitive rm, even if their information i

s notspecically about this rm but about a competitor. In her setting, agents arerisk

neutral and capital

constrained so they seek the stock with the greatestsensitivity to shocks. In contrast, we assume that agents are risk

averse andcharacterize how the risk

return trade

off varies with a rms market power.Our paper also belongs to the large body of research on

trading underasymmetric information. This literature studies the impact of information on

(2006)). They are also in line with Hou and Robinson (2006), who document that rms in morecompetitive industries earn higher returns after adjusting for risk.

7In our setting, product market power does not generate a net social gain. Ra

ther, it reduces

the social loss. This is because our solution technique assumes that shocks aresmall. Hence, stockprices and investments differ only slightly from those obtained in a riskless economy.

8For example, rms may choose low leverage ratios to guarantee that they will be able to service

their products (Titman (1984)) or high leverage ratios to commit to aggressive o


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perating strategiesthrough limited liability provisions (Brander and Lewis (1986)).

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Product Market Competition and Stock Market Efciency7

nancial variables. To the best of our knowledge, the role of product marketpower has not yet been examined in this context. Our paper contributes inparticular to the subset of this literature that emphasizes the real benets ofinformational efciency. In our model, stock prices reect the quality of rmsinvestment opportunities and help investors channel capital to the better ones.9

The remainder of the paper is organized as follows. Section I describes theeconomy. Section II solves for the equilibrium and Section III studies how it isaffected by competition. Section IV considers extensions to the baseline model.Section V confronts the model with the data. Section VI concludes. Proofs areprovided in the Appendix.

I. The Economy

Ours is a standard rational expectations model of competitive stock tradingunder asymmetric information (e.g., Grossman and Stiglitz (1980)) but for oneimportant difference: Firms in our setting enjoy monopoly power in their prod uct market. The economy consists of two sectors, a nal and an intermediategoods sector. Intermediate goods are used as inputs in the production of the

nal good. They are produced by rms operating under monopolistic competitionand subject to technology shocks. These shocks are not observed but investorsreceive private signals about them. Monopolies stocks trade competitively onthe equity market. Their prices reect private signals but only partially becauseof the presence of noise. Prices in turn guide investors in their portfolio alloca

tions. Time consists of two periods. In the investment period (t = 1), markets

open and investors observe their private signals and trade. In the productionperiod (t = 2), intermediate and nal goods are produced and agents consume.The model is further dened as follows.

A. Technologies

A.1. Intermediate Good Sector

There are M monopolies operating in the intermediate good sector. Monopolym (m = 1 toM) is the exclusive producer of good m. Its production is determinedby a risky technology that displays constant returns to capital:

Ym AmK0 for all m = 1, . . . , M

where Am is a technology shock specic to rm m and K 0 is the book value of

its capital stock. Firms are endowed with an arbitrary capital stock K 0, whichcannot be adjusted. Our analysis focuses on the interplay between competitionin the product market and information asymmetries in the equity market, forwhich the initial capital stock is irrelevant. We allow rms to changetheircapital stock in the last section of the paper, where they raise fresh capital.


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We

9See, for example, Dow, Goldstein, and Guembel (2006) and the references therein.

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assume that the M intermediate monopolies are entirely nanced with publicequity.

Goods are produced and then rms are liquidated in the production periodm m

(t = 2). Thus, if rm m sells Y goods at a price of Q , its value at t = 2 is

m m m m= Q Y . The technology shocks A (m = 1 to M) are assumed

to be log-normally distributed and independent from one another. Market power allowsrms to insulate their prots from productivity shocks. They increase goodsprices in times of shortage (bad shock) and decrease them in times of abundance(good shock). This behavior complicates modeling under rational expectationsbecause it leads to stock payoffs that are not linear in shocks. Asa result,

the extraction of information from equilibrium stock prices can no longer besolved in closed form.10 For this reason, we resort to a small-risk expansion. We

assume that the productivity shocks are small and log-linearize rms reactionsto these shocks. Specically, we assume that ln Am amz, where am

can be

interpreted as the growth rate of technology m and z is a scaling factor, andamz is normally distributed with mean zero and precision h /z (variance z/h ).

aa

The model is solved in closed form by driving z toward zero. Peress(2004)demonstrates the convergence and the accuracy of such an approximation ina noisy rational expectations economy. Throughout the paper, we assume thatthe scaling factor z is small enough for the approximation to be valid.

A.2. Final Good Sector

Intermediate goods are used as inputs in the production of the nalgood.Many identical rms compete in the nal good sector and aggregate to one rep-resentative rm. The nal good is produced according to a riskless technology,

MG (Ym)1m ,

m=1

m here G is nal output, Y is the employment of the m th type of intermediategood, and m is a parameter bet een zero and one.11

The parameter m is the key parameter of the model. It measures the degree


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of market po er enjoyed by rm m. To see this, note that nal good producers

10Rational expectations models of competitive stock trading under asymmetric information

typically assume that preferences display constant absolute risk aversion (CARA)or risk neutralityand that random variables, including payoffs and signals, are normally distributed. Equilibriumstock prices are conjectured to be linear functions of these random variables. The preferenceassumptions generate stock demands linear in expected payoffs and prices hile the normalityassumption leads to expected payoffs linear in signals including prices, thus validating the initialguess. The canonical examples are Grossman and Stiglitz (1980) ith competitivetraders and Kyle(1985) ith strategic traders. Alternative assumptions are used, for example, byAusubel (1990),Rochet and Vila (1994), Barlevy and Veronesi (2000), and Peress (2009). Bernardoand Judd (2000)and Yuan (2005) use numerical solutions to solve more general models.

11The nal good technology provides a convenient ay of aggregating the differe

nt goods pro-

duced by the monopolies. It is in the spirit of Spence (1976), Dixit and Stiglitz (1977), and Romer(1990), among others, and is used in much of the industrial organization literature.

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M m m

set their demand for inputs to maximize prots, G m=1 Q Y , taking in

m as given (we use the price of the nal good as

thetermediate goods prices Q

m mm 1/m 12numeraire). The resulting demand for input m is Y = [(1 )/Q ]

. Itsm m m m

elasticity, d ln Y /d ln Q , equals 1/ and declines hen gro s.Thus,

the higher m, the less elastic the demand for good m and the more marketpo er rm m exerts. When m is identical across rms (m = for all m), it can

be interpreted as (the inverse of) the degree of competition in the intermediategoods sector. Indeed, the elasticity of substitution bet een any t o goods m

m m m m


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and m , d ln(Y /Y )/d ln(Q /Q ), equals 1/. So the inverse of measuresthe extent to hich inputs are substitutes for one another, a lo er indicat-ing more substitutability and a more competitive input market. In thelimit

hen = 0, inputs are perfect substitutes and the intermediate goods sectoris perfectly competitive.

The main characteristic of market po er is that it makes monopoly protsless sensitive to technology shocks. Substituting the demand for intermediategoods into the expression for these prots yields m = (1 m)(Ym)1m . Thus,1 m also measures the elasticity of prots to shocks, (ln m)/(ln Am), for a

given stock of capital K0 .

B. Assets

Monopolies equity trades on the stock market. We normalize the numberof shares outstanding to one perfectly divisible share. The price ofa shareof rm m is denoted Pm. To avoid the GrossmanStiglitz (1980) paradox, e

assume that some agents trade stocks for exogenous random reasons, creatingthe noise that prevents prices from fully revealing private signals. We denote

by m the aggregate demand for stock m emanating from these noise tradersas a fraction of investors wealth, that is, m is the number of sharesnoise

traders purchase multiplied by the price of stock m and divided by wealth.13We assume that mz is normally distributed with mean zero and variance 2z,

and i independent from all other random variable and acro tock .Thi

formulation implie

that the level of noi

e trading i

identical acro

ector

and doe

not bia

our re

ult

. There are no

hort-

ale

con

traint

. A ri

kle

a

et i

available in perfectly ela

tic

upply, allowing inve

tor

to borrow and

lend freely. The ri

kle

rate of return i

denoted Rf = 1 + r f z.

C. Inve

tor

The main deci

ion maker

in our economy are inve

tor

. There i

a continuumof them, indexed by l in the unit interval [0, 1]. They derive utility from thecon

umption of the nal good g . Utility di

play

con

tant relative ri

k aver

ion

12The demand for input m i

independent of the demand for input m becau

e the nal good

production function i

eparable. Thi

implie

the analy

i

ub

tantially.13We derive inve

tor

demand for

tock

at the order 0 in z when we

olve the

model. Accordingly,m repre

ent

the order-0 component of noi

e trader

demand.

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(CRRA):

1 1


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U(

) = ,1

where > 0 measures relative risk aversion and = 1 corresponds to lo util

ity. Investors are endowed with a portfolio of stocks and bonds. We denote by wand f m (m = 1 to M) a ent ls initial wealth and the fraction of wealth initially

0,linvested in stock m respectively. We assume for simplicity that investors startwith the same initial wealth w, thou h its composition (the f m s) may

vary

0,larbitrarily. Investors choose new portfolio wei hts flm in the investment period

(t = 1) and consume in the production period (t = 2).

D. Information Structure

Investors do not observe technolo y shocks in the intermediate ood sectorwhen they rebalance their portfolio (t = 1).14 But they are endowed with someprivate information. Specically, investor l receives a private si nal sm about

ls technolo y shock:

rm m

sm = am + m,l l

wh r m is an rror t rm ind p nd nt of th rms prot m and across ag nts.l

Th t rm mz is normally distribut d with m an z ro and pr cision h /z (vari-ls

anc

z/hs ). W

assum

for simplicity that pr

cisions ar

id

ntical across stocksand inv stors.

E. Equilibrium Conc pt

W d n th quilibrium conc pt for this conomy, starting from individualmaximization (conditions (i) and (ii)) and proc

ding to mark

t aggr

gation(conditions (iii) and (iv)).

(i) In th

production stag

, nal good produc

rs s

t th

ir d

mandfor in-

mt

rm

diat

goods to maximiz

prots taking pric

s Q(m = 1 to M) as

giv n. As shown abov , this l ads to a d mand for input m qual to

m m m 1/mY = [(1 )/Q ] .

(ii) In the investment stage, investor l sets her portfolio eights flm guided


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by stock prices Pm(m = 1 to M) and her private signals s . Investors are

l

14The model assumes that investors have private information about the rms prospects hile

managers do not. Accordingly managers, unlike investors, do not make any decision. This assump-tion allo s us to focus on the inuence of product market competitionon investors incentivesto trade and on the aggregation of their dispersed private signals through prices. An alternativeinterpretation of the model is that managers possess private information that they have alreadyconveyed (possibly imperfectly) to the market. This information is encoded in the prior distributionof technology shocks used by investors. Our focus is on the additional trading and informativenessgenerated by investors private signals.

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atomistic and take stock prices as given. Their problem can be expressed

formally as:M

max E[U(c ) | F ] subject to c = Rf + f m Rm Rfw, (1)

l l l l{flm,m=1 toM}

m=1

where c m ml and Fl sl , P for m = 1 to M denote a

ent l s consumption

and information set, and Rm and rmz = ln(Rm) denote the simple and

lo

returns on stock m. Firm m

enerates a prot m before bein

liqui

dated, yieldin a ross stock return of Rm = m/Pm (there is one share

outstandin ). Investors hold a position in every stock, be it lon or short,

since there are no transactions costs nor short

sales constraint

s. Thereturn on their portfolio equals Rf + M f m Rm Rf . Their

problem

m=1 lis simplied by notin that the nal ood production function is separa

ble. This implies that the demand for input m is independent from the

(as stated in condition (i)), andtherefore

quantity employed of input m


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15

that the return on stock m is independent from the return on stock m .m

(iii) Intermediate oods prices Q (m = 1 toM) clear the market for interme

diate oods:

[(1 m m 1/m m)/Q ] = A K0 for m = 1 to M,

here the left-hand side is the demand for good m and the right-handside its supply.

(iv) Stock prices Pm(m = 1 to M) clear the market for stocks:

1 flm dl + m = 1 for m = 1 to M,

0 Pm Pm

where the integral and wm/Pm repre

ent, re

pectively, the number of

hare

demanded by inve

tor

and noi

e trader

, and the right-hand

ide i

the number of

hare

out

tanding. We are now ready to

olve forthe e uilibrium.

II. E

uilibrium Characterization

We di cu the trading and pricing of monopolie tock. From now on, wecon

ider a generic

tock and drop the

uper

cript m when there i

no ambiguityto

implify the notation. We gue

that

tock price

are approximately(i.e.,

at the order z) log-linear function of technology and noi e hock , olve forportfolio

, derive the e

uilibrium

tock price

, and check that the gue

i

valid. We expre

tock price

, prot

, and capital a

P exp(pz ), exp(z), and

K ex

(kz) at the order z. Note that P , , and K are deterministic constants thatmeasure the value of P , , and K when z = 0 (in which case Am = Rf

1 for

15Inde endence across rms im lies that shareholders are not better off limiting one rms

out

ut to favor another. Their o

timal o

erating strategy is to maximize

rots inall rms.

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all m, that is, there is no risk and no value to time). The terms , , and k arefunctions of a and that ca ture the order-z erturbation induced by the shocksand the riskless rate. Our focus throughout the a er is on the interaction ofmarket ower with the shocks, which is reected in the order-z term, that is,


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, , and k.We begin with a brief discussion of a benchmark economy in which

tech-nology shocks, A = ex (az), are observed erfectly. The e

uilibrium inthisriskless economy is solved in closed form without any a

roximation, unlikethe general case. Given its ca ital stock K0, a mono oly generates a

rot= (1 )K 1 exp[(1 )az] at t = 2 (see Section I.A.2). Its stock trades at

0t = 1 for P = /Rf = (1 )K 1 exp[(1 )az r f z]. Investors earn the risk

0less rate on a riskless investment. The followin proposition describes the equi

librium when technolo y shocks are not perfectly observed.

PROPOSITION 1: There exists a lo

linear rational expectationsequilibrium

characterized as follows.

1 Shares trade at a price P = (1 )K0 exp(pz ), here p

= p0() +p ()a + p () ,

a (1 )2 1 (1 )K 1

0 fp0() r ,

(2)h() 2

p () (1 ) 1 ha 0, p () (1 )p (),

a a

h() hs

h2h () s , and h() h + h () + h .

(4)p 2 2 2 a p

s (1 )

Inve

tor l allocate

a fraction fl of her wealth to each

tock

uch that

h

(1 )K 1

fl = l + 0 .(5)

(1 )

Proposition 1 conrms our initial guess that prices are approximately log-linear functions of technology and noise shocks. This is illustrated in Figures4and 5, hich depict p , pa , and p . The technology shock a a earsdirectlyin the rice function because individual signals sl , once aggregated, colla se


17/62

totheir mean, a. The noise shock enters the

rice e

uation because it re

resentsnoise traders demand. The rice P reveals a + = (a + (1 )/h ), a

a as

signal for a with error (1 )/hs . Investors cannot tell whether the valuationof an ex ensive stock is justied by a good technology (a large) or by large noise

2 22 2trades ( large). The function var[ (1 )/hs ]/z = (1 ) /h

the noi

ine

of

tock price

and it

inver

e, hp , it

informativene

. Note thath = z/var (az | F ) mea

ure

the total preci

ion of an inve

tor

information.

l lShe u

e

information about prot

from three

ource

, namely, her prior (the

----------------------- Page 13-----------------------


3.5

3

2.5)P(

e 2cirp

k 1.5co

tS 1

0.5

010

510

0 5

0-5

-5-10 -10

Noi

e

hock () Technology

hock (a)

Figure 4. The e

uilibrium

tock price. The

tock price i

plotted again

t the realization

of the technology

hock a and noi

e

hock . The parameter

are = 0.8, ha = 1, 2


18/62

= 0.1, r f =

0.02, M = 10, w = 0.3, = 1, hs = 0.1, and z = 0.1.

ha term), the stock price (the hp term), and her private si

nal (the hs term),and their precisions simply add up (equation (4)). The rst two sources of in formation are public and their total precision equals ha + hp . The equilibriumcoincides with that obtained in the riskless benchmark economy when informa

tion is perfect (h = hs = , p0 = r f , pa = (1 ), and p = 0). Investohold

a osition in every stock, be it long (fl > 0) or short (fl < 0).Their

ortfolioshares are ex ressed in e

uation (5) as the average weight across investors(the order-0 com onent of the rms rot (1 )K 1 divided by in

0 ealth ), minus noise trades tilted by their rivate signal errors l , scal dby risk av rsion , the precision of their si nal hs , and one minus market power.

III. The Impact of Market Po er

In this section e examine ho po er in rms product market affects theequilibrium outcome. We start by analyzing trades. From trades follo infor-mativeness of stock prices, dispersion of investors forecasts, distribution ofreturns, liquidity, and allocative efciency.

A. Trading Volume

We study the impact of product market competition on investors trading

activity. Trading volume is measured as the value or the number of shares

----------------------- Page 14-----------------------


ec

i )r a 1p pk (c so kt cs o 0.5f ho s


19/62

y .t hiv ci 0t ei ts o 1ne t

0S 0.8

0.20.6

Weak0.4

Private signal 0.4 0.6precision (hs) 0.2 0.8 Mkt po er ()

0 1 Strong

ec ) 2i

r

(k sc ko ct 1s of ho s

y et s

ii 0

v oiti n 1sn o

0t


20/62

e 0.8S

0.20.6

Weak0.4

Private signal 0.4 0.6

recision (hs) 0.2 0.8 Mkt

ower()

0 1 Strong

Figure 5. The sensitivities of the stock price to technology and noise shocks.The sen-sitivities to technology shocks pa (top panel) and to noise shocks p (bottom

anel) are

lottedagainst the recision of rivate information hs and the degree of market ower .The parametersare ha = 1, 2 = 0.1, r f = 0.02, M = 10, K0 = 1, w = 0.3, = 1, and z = 0.1.

traded, conditional on the distribution of stock endowments (thef m s) as in

0,lHolthausen and Verrecchia (1990). The number of shares traded coincides withthe stocks turnover given that there is one share outstanding. The following

ro osition characterizes the relation between trading volume and market

ower.

PROPOSITION 2: Trading volume is larger for rms with more market ower.

The ro osition establishes that market ower encouragesinvestors to

trade. This is illustrated in the to left anel of Figure6. Intuitively,

----------------------- Page 15-----------------------

Product Market Com

etition and Stock Market Efciency15

0 0.2 0.4 0.6 0.8 110 100 10

8 80 8e s

sm e

u nl 6 60 e 6o vv i

tg ani md 4 40 r 4a o


21/62

fr nT I

2 20 2

0 0 00 0.2 0.4 0.6 0.8 1 0 0.2

0.4 0.6 0.8 1Mkt ower ()

Mkt po er ()

0.07 30

n 0.06 25o

is 0.05r 20ep y

ts 0.04 i

i dd i

s u 15t qs 0.03 ia Lc 10e 0.02ro

F5

0.01

0 0

0 0.2 0.4 0.6 0.8 1 0 0.20.4 0.6 0.8 1Mkt po er ()

Mkt po er ()

0 0.2 0.4 0.6 0.8 1 0 0.20.4 0.6 0.8 1

0.03 0.1 o 0.351

itar

0.025 e 0.30.08 p

0.8ra 0.25

0.02 hS

yt 0.06 d

0.6


22/62

i 0.2li nt 0.015 aalo m 0.15V 0.04 u

0.40.01 i

me 0.1r

0.005 0.02 p0.2

k 0.05s

iR

0 0 00

0 0.2 0.4 0.6 0.8 1 0 0.20.4 0.6 0.8 1

Weak Mkt po er () Strong Weak M

kt po

er () Strong

Figure 6. The impact of market po er on the equilibrium. on the x-axis measures therms po er in its product market. The top left panel displays the share

turnover (solid curve,right scale) and the dollar trading volume (dashed curve, left scale). The top right panel displaysthe informativeness of stock prices hp . The middle left panel displays the dispersion of investorsforecasts about the rms prot (solid line, right scale) and technology shock (dashed line, leftscale). The middle right panel displays liquidity 1/p . The bottom left anel dis

lays the varianceof log rots (solid curve, right scale), the variance of log rices (dashed curve, left scale), and thevariance of stock returns (dotted curve, right scale). The bottom right

anel dis lays a rms risk

remium (dashed curve, left scale) and its Shar e ratio (solid curve, right scale). The arametersare ha = 1, 2 = 0.1, r f = 0.02, M = 10, K0 = 1, w = 0.3, = 1, hs = 0.1, and z = 0.1.

mono

olies are less vulnerable to

roductivity shocks because they can

assthese shocks on to their customers. This makes their rots less risky.

In-vestors are more condent in their rot forecasts (though they trust their

roductivity forecast just as much) so they trade more aggressively on their ri-vate information. Thus, com etition erodes insiders informational advantage.

----------------------- Page 16-----------------------



23/62

This is a key im

lication of the model, from which the next

ro

ositions willfollow.

It may seem sur rising that stocks of more com etitive rms are not moreintensely traded given that their highly sensitive ayoff offers a more effectiveavenue for trading on rivate information. The reason is that investors arerisk-averse and these stocks are also more risky.16 Were investors risk-neutral

and ca ital-constrained, they would refer to trade stocks of more com etitiverms as in Tookes (2007).17

B. Informational Efciency

The following

ro

osition describes how the increase in informed trading forrms with more market ower affects the informativeness of stock rices.

PROPOSITION 3: Stock rices are more informative when rms enjoy more mar-ket ower.

The ro osition establishes that market ower increases the amount of infor-mation that is revealed through rices. Above we show that investors scale u

their trades of more mono

olistic stocks because of their reduced risk. Conse-

uently, their rivate signals are more fully ca italized into rices. Thus, a lessefcient roduct market (in the sense that rms enjoy more mono oly ower, thatis, face a more ca tive demand for their roduct) leads to a more efcient stockmarket (in the sense that stock rices are more informative). Putting it differ-ently, stock market im erfectionsthe extent of information asymmetriesaremitigated by roduct market im erfectionsmarket ower.18 Pro osition 3 is

illustrated in the to right anel of Figure 6.

C. Dis ersion of Investors Forecasts

The following ro osition shows how the increased accuracy of ublic infor-mation affects the dis ersion of investors forecasts. The dis ersion of investors

16In our setting, a nite number of stocks with inde endent returns trade on the stock market.

Therefore, risk amounts to the variance of these returns. The variancecan be inter reted as a

covariance with the market if these stocks are only a subset of available securities. For exam le,su

ose that the return on the market (which encom

asses other

ublicly traded securities, rivate

assets, human ca

ital, etc.) is rmarketz = M (1 m)amz + bz,

here b is a random variable

m=1independent of the am market m m 2 m

m. Then cov(r z,r z) = (1 ) var(a z) = var(r z

).17Formally, stock returns can be expressed as r = ln(/P ) = (1 )az pz , which

depends on


24/62

productivity shocks throu h (1 )a. As 1 rises (less market po er), returns are mre sensitiveto these shocks. Expected returns increase by a factor (1 ) hile their varianceincreases bya factor (1 )2 . Thus, the ratio of expected excess returns to their variance, hich determines

investors trades, is magnied by a factor 1/(1 ) > 1. It follo s that the dollar trding volumeand turnover increase ith market po er .

18Formally, prices provide a signal for technology shocks a ith error (1 )/hsso their

re-cision, which measures the informativeness of rices, e

uals h 22 2 2

= h /[ (1 ) ]. It increa e

when inve

tor

trade more ( lower or higher) or hen noise traders are less active ( 2 lower).

In particular, price

are perfectly revealing when is close to one.

----------------------- Page 17-----------------------


productivity a, prot , and return r forecasts are measured for any givenrm, conditional on the realization of the roductivity and noise shocks a and

: var E(a | F ) | a, , var E(ln | F ) | a, , and var E(r | F ) | a, .

l l l

PROPOSITION 4: Investors roductivity, rot, and return forecasts arelessdis

ersed when rms enjoy more market

ower.

Pro osition 4 establishes that the dis ersion in investors roductivity, rot,and return forecasts is reduced by market ower. Indeed, investors assign asmaller weight to their rivate signals and a greater weight to stock riceswhen their informativeness im roves. This results in less disagreement among

2

investors. For exam

le, the dis

ersion of

roductivity forecasts e

uals hs /h ,the ratio of the

recision of the

rivate signal to the s

uared

recision of totalinformation (h h + h + h ). As market ower strengthens, hrises, induc-

a

s

ing investors to rely less on their rivate signal. Figure 6 shows the forecast


25/62

dis ersion (middle left anel) at different levels of market ower.

D. Stock Returns

Market ower acts as a hedge that allows rms to ass shocks on to theircustomers. Hence,

rots uctuate less when rms enjoy more market

ower.So do stock rices, a discounted version of rots, and returns, which ca turethe difference between

rots and rices. Ex ected returns are lower tooto

com

ensate investors for bearing less risk. These effects obtain in im erfectcom

etition models, regardless of information asymmetries, sim

ly because

rots are less risky. The novel as ect em hasized in this a er is that market ower also exerts an indirect inuence through the informativeness of rices.We focus on these informational effects. The following

ro

osition summarizesthe im act of market ower on the distribution of stock returns. We considerthe volatility of stock returns (unconditionally and conditional on stock rices),the conditional volatility of rots, the ex ected excess stock return and theShar e ratiothe ratio of ex ected excess returns to their standard deviation.

PROPOSITION 5:

Firms enjoying more market

ower have less volatile returns (uncondition-ally and conditional on ublic information), lower ex ected returns, and

higher Shar e ratios. They also have less volatile rots. Their return and rot volatility, ex ected return, and Shar e ratio

arereduced further by the im roved informativeness of their stock rice.

We know from Pro osition 3 that market ower enhances how much investorscan learn from stock rices. Im roved information in turn makes rots con-ditional on rices less variable. Thus, the informational effect of market owerworks to dam en rot volatility. The behavior of returns mirrors that of rof-

its. They too are less volatile for more mono

olistic rms as a resultof thedirect effect of market ower. Market owers indirect effect through the infor-mativeness of

rices decreases return volatility further. This is because, with

----------------------- Page 18-----------------------


better information, rices track future rots more closely, leaving returns toabsorb a smaller fraction of shocks. In the case of erfect information for exam-

le, rices reect technology shocks erfectly while returns e

ual the risk-free

rate so their variance is zero. The informational im

act of market

ower alsoreduces ex ected stock returns and the average Shar e ratio, indicating thatit is stronger on the risk

remium than on risk. Thus, the indirecteffect ofmarket ower through the informativeness of rices magnies the decrease in

rot and return volatility, ex ected returns, and the Shar e ratio. The bottom

anels of Figure 6 illustrate these ndings.

E. Li

uidity


26/62

We analyze next the im act of market ower on li

uidity. We use the sen-sitivity of stock

rices to (uninformative) noise shocks,

= (ln P )/(z)to

ca ture li

uidity as is common in asymmetric information models.

PROPOSITION 6: Firms enjoying more market

ower have stock

rices that areless sensitive to noise shocks.

Pro osition 6 extends to noise shocks the intuition we develo ed for ro-ductivity shocks a: Firms use their market

ower to shield their

rots fromshocks, whatever their source. Prots and therefore rices of more mono olisticrms are less vulnerable to noise shocks. Figure 6 (middle right anel) showsgra hically that decreases with .

F. Allocative Efciency

The interplay bet een imperfections in the product and equity markets hasimplications that are not only nancial as discussed so far but also real. Toillustrate this point, e consider rms that raise fresh capital through

anequity issuance. Investors not only value these rms, but also determine ho much capital they are to receive. An efcient allocation of capital requires thatinvestors channel more funds to more productive technologies (i.e., those ith

a higher technology shock A ), and less funds to less productive technologies.In this section, e investigate ho competition inuences investors ability toperform this allocation. As before, rms start ith K 0 units of capital and oneshare outstanding. We assume that rms issue new sh res ( n rbitr rypositive number), the proceeds of which will serve to exp

nd their

sset b

se.We denote K = P the

mount of c

pit

l r

ised, where P

g

in represents thestock price (P

nd K

re determined endogenously in equilibrium).As before, we st

rt with the benchm

rk perfect-inform

tion economy.Wedenote prices, prots,

nd c

pit

l in this economy with

superscript P . Th

nksto its exp

nded c

pit

l stock K0 + PP ,

monopoly gener

tes

riskless protP = (1 )(K0 + PP )1 exp[(1 )az] at t = 2. Therefore, its stock trades

at t = 1 for

PP = /Rf = (1 )(K0 + PP )1 exp[((1 )a r f )z].

Pa e 19


PP

We search for a solution to this equation of the form P = P exp(p

z), where wene lect terms of order lar er than z. It is useful to dene a rms dilution factor

as P /(K0 + P ). This f

ctor equ

ls zero when no sh

res

re issue

s in theprece ing sections,

n one when the rm h

s no other c

pit

l but th

t newly

1raised. The term P is the implicit solution to P (1 + ) = (1 )(K0 + P )

n c

n be expresse in terms of K 0

n

s


27/62

/(1 )]1 [(1 ) (K /(1 ))].P = [K0 0

Moreover, pP = [(1 )a r f ]/(1 + )19. We can check thatno

shares are issued ( = 0), PP = (1 )K 1 exp[((1 )a r f )z]Sec

0

P PP

tion II. The

mount of c

pit

l r

ise

is K = P = P exp(p z). The sc

lingf

ctor 1/(1 + ) in the expression for pP accounts for the factthat the

ne ly issued shares allo rms to expand their asset base: A 1% increase inthe amount of capital raised generates a (1 )% increase in prots, of hichne shares claim a fraction . Therefore,

1% incre

se in current stock pricesre uces investors return by less th n 1%, n mely, by (1 + )%. Formally,the stock return is rP = (1 )(a + pP ) pP (given th

t kP = pP )Since theinvestment is riskfree, rP = r f

n pP follows. The el

sticity of investments

to technology shocks, (ln KP )/(ln A), equ

ls pP (, ) (1 )/(1 + a hich is positive, indicating that more capital o s to better rms. We turn tothe analysis of the imperfect-information economy.

PROPOSITION 7: Assume that rms issue new sh

res. There exists

log

line

rr tion l expect tions equilibrium ch r cterize s follows.

Sh

res tr

e

t

price P = P exp(pz ) such th

t p = p (, ) +p (, )

+0

p (, ) ,

K0 (1) (1 ) K0 ,P =

1 1 1 (1 )2 1 (1 )K(1)

0f

p0(, ) r,

1 + h() 2 (1 )(1)

p (, ) 1 1 h

0,p (, ) (1 )p (,

a

1 + h() hs

and h() is dened in Proposition 1.

Firms raise K = P exp(kz) units of c pit l, where k = p .


28/62

The pricing equ

tions presente in Proposition 7

re simil

r to those ofProposition 1. The only ifference is th

t the coefcients p0 , p

,

n p arenow scaled by 1/(1 + ) to account for the capital base expansion as ithe benchmark perfect-information economy. If no ne shares are issued, then = 0

n

the equ

tions coinci

e with those of Proposition 1. The equilibrium

19 P is uniquely ene by this equ

tion.

P

ge 20

20 The Journ

l of Fin

nceR

coll

pses to th

t of the benchm

rk economy when inform

tion is perfect (h =hs = , p0 = r f /(1 + ), pa = (1 )/(1 + ), and p =

We analyze next how the accuracy of information h affects the efciencyof investments, holding xed the degree of market ower . This ill proveuseful for understanding the role of market po er. The elasticity of

invest-ments to technology shocks, (ln K)/(ln A) = pa(, ), me

sures the economys

lloc

tive efciency: A l

rger el

sticity me

ns th

t more (less) pro uctive rms ttr ct more (less) c pit l. The following proposition est blishes link betweeninform

tion

l

n

lloc

tive efciency.

PROPOSITION 8: C pit l is more efciently lloc te when inform tion is more

ccur

te.

The el

sticity of investments to technology shocks p

incre

ses with the level

of inform

tion, hol

ing xed. Hence, better-informed economies distributecapital more efciently across rms. In the perfect information limit (h = ),the elasticity collapses to pP = (1 )/(1 + ) as derived above. It fall

ap = (1 )/(1 + ) 1 h /h when inform

tion is imperfect. A worsening

(

)of inform

tion (lower h) pushes it

w

y from its v

lue un er perfect inform

tion(1 h /h further from one). In the limiting c

se of no inform

tion (h= h ),

p

= 0 so investments

re in epen ent from technology shocks.20

We procee to the imp

ct of m

rket power. Since its intensity inuencesthe inform

tiveness of prices (Proposition 3), m

rket power

ffects theef

ciency of investments. To

ssess its imp

ct, we nee to neutr

lize the

irecteffect of m

rket power, which c

n be i entie in the perfect inform

tionc

se. The el

sticity of investments with respect to technology shocks


29/62

equ

lspP (1 )/(1 + ) hen information is perfect. We are interested in the

aindirect effect of market po er on allocative efciency, hich e measure rela-tive to the perfect-information benchmark as pa/pP . We establish the follo ing

aresult.

PROPOSITION 9: Capital is more efciently allocated hen rms enjoymoremarket po er.

Proposition 9 sho s that imperfect competition has an efciency impactthrough the distribution of capital across rms. It combines Proposition2,

hich establishes that the informativeness of stock prices improves as marketpo er strengthens, ith Proposition 8, hich sho s that information improvesthe quality of investments. Formally, pa/pP rises ith market po er . Thus,

athe social loss of market po er is tempered by improvements in the capital

20Proposition 8 also makes apparent the informational role of the stock market. It can best be

understood by comparison to an economy in hich prices do not convey any information. In such aneconomy, investors total precision (the combined precisions of price and privatesignals) is reducedto h + h < h and the elasticity of investments to technology shocks to [1 1/(1 + h /h )](1

ss

)/(1 + ) < pa . (The equilibrium in this economy can be derived from thgeneral case by

driving the volatility of noise, 2 , to innity to make

tock price

uninformative.) The allocation of

capital i

not a

efcient, though the

ame private

ignal

were ob

erved.

----------------------- Page 21-----------------------


1

0.9

0.8

0.7

yc 0.6ne

i


30/62

ciff 0.5E

0.4

0.3

0.2

0.10 0.2 0.4 0.6 0.8

1Weak Mkt power () St

rong

Figure 7. The impact of market po er on the economys allocative efciency. The plotdisplays the efciency of investments pa/pP . on the x-axis measures the rms po er in its

aproduct market. The dilution factor is = 0.1, n the other p r meters re h

= 1, 2 = 0.1, r f =

0.02, M = 10, K0 = 1, w = 0.3, = 1, hs = 0.1, and z = 0.1.

allocation.21 Puttin it differently, competition, rather than the lack thereof,

results in an inefciency. This effect, illustrated in Fi ure 7, results from theinteraction of monopoly power in the product market with informational fric

tions in the equity market.

The proposition implies that the dere ulation of product markets hasan

additional effect that operates throu

h the stock market. Openin

productmarkets reduces the information content of stock prices, which dama es theefciency of the capital allocation within these markets. This ndin

has im

plications for policy desi n. It su ests that product market reformsshouldnot be conducted in isolation but in combination with stock market reforms.Since product market competition can hurt stock markets, policies aimed

atimprovin nancial efciency, such as the liberalization of the nancial sector,should be implemented simultaneously.22

21The social loss stems from the fact that monopoly power induces rms to produ

ce fewer

oods

and sell them at prices that exceed their mar

inal cost. On the other hand, a literature initiatedby Schumpeter (1912) ar ues that competition is detrimental to innovation because it reduces themonopoly rents that reward it. Our ndin s reinforce the Schumpeterian view: Competition impliesthat ood ideas stru le to attract capital, which further weakens the

incentives to innovate.


31/62

Empirically, Chun et al. (2008) show that competition boosts the volatility of rms productivity.

22This recommendation echoes that of interest roup models of nancialdevelopment such

as Rajan and Zin

ales (2003). They su

est that incumbent rms oppose nancial developmentbecause it breeds competition. They ar

ue that dere

ulation should take place inboth product andnancial markets to overcome the resistance from these roups.

Pa

e 22


IV. Discussion and Extensions

In this section we explore various extensions of the model. First, we allowinvestors to learn from rms past performance. Next, we discuss the role ofnoise tradin . Finally, we examine whether our ndin s eneralize to factorsother than product market competition such as levera e.

A. Learnin from Past Prots

So far, investors information consists of their private si

nalsand stockprices. In this section, we allow investors to learn about technolo yshocksfrom rms past prots. We assume that rms operate at t = 0, durin a periodthat precedes the tradin round (t = 1). The prot rm m enerates at that

m mtime is m = (1 m)(AmK )1 = (1 m)(K )1 exp[(1 m)amz],

0 0 0 00

Am exp(amz) denotes its technology shock at t = 0.0 0

To connect past and future prots,

e make t

o assumptions about rmms technology shock and prot in period 0. First, e assume that technologyshocks display some persistence. Therefore, past shocks are informative aboutfuture shocks. Specically, e assume that

am = am + m,(7)

0

w

e

e is a positive pa

amete

and m is an e

o

te

m, t

at is independent ofam, of all ot

e

andom va

iables, and ac

oss

ms, and is no

mally dist

ibuted

wit

mean ze

o and p

ecision

/z (va

iance z/

). T

e pa

amete

s and

cont

ol t

e pe

sistence of s

ocks: T

e co

elation between am and am equals

02

1/ 1 +

/(

), w

ic

inc

eases wit

o

. In pa

ticula

, cu

ent s

ocks

a a

e un

elated to past s

ocks if o

equals ze

o.


32/62

Second, we assume t

at p

ots in pe

iod 0 a

e impe

fectly

epo

ted. T

us,t

e past p

ot p

ovides a noisy signal fo

(1 m)am, denoted m (all the other0 0

com onents of the ast rot are deterministic). S ecically, rm m re orts

m m m m0 = (1 )a0 + ,

(8)

where m is a

error term that is i

depe

de

t of am, of all other ra

dom vari-0

ables, a

d across rms, a d is ormally distributed with mea zero a d pre-cisio h /z (varia ce z/h ). Observi g is e

uivalent to observinga signal

0am + m/(1 m) about am. This signal is less accurate for a rm that enjoys

0 0m 2

mmore market po er (the precision of the signal (1 ) h is lower whe is larger). This is once again because rms use their market po er to insulateprots from shocks, thus eakening the link from productivity to prots.

As before, e consider from no on a generic stock and drop the superscript

m to simplify notations. Substituting equation (7) into equation (8) yields 0=(1 )a + (1 ) + . Thus, observi g 0 is e

uivalent to observing a signalabout a, namely, a + u were u ( + /(1 ))/. T e p ecision of t is signalis denoted

0 and e

uals

----------------------- Page 23-----------------------

Product Market Com etition and Stock Market Efciency23

2

0 () = 2 .1/h + 1/[(1 ) h ]

Note that h0 increases with and

as past s

ocks a

e mo

e co

elated tocu

ent s

ocks, and wit

as the reporti g error shri ks, but itdecreaseswith as rms exert their market po er to hedge prots. In particular, thesignal is uninformative hen = 1 because the prot at t = 0 is unrelated tothe shock at t = 0 and hence to the shock at t = 1.

Finally, e note that this extension reverts to the model solved so far hen,

, o

equals zero. I that case, past prots do ot provide a y i formatio

about future prots. The followi

g propositio

describes the equilibrium.

PROPOSITION 10: There exists a log-li

ear ratio

al expectatio

s equilibrium i which shares trade at a price P = (1 )K 1 exp(pz ), here

0

p = p () + p ()u + p ()a + p (), u a + ( + /(1 ))/,0 0 a


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(1 )2 1 (1 )K 1

0 fp () , p () (1 ) 1

0 a

() 2 h()

(10)

p () (1 )2 1 + 2 hs 2 2 , p0 () (1 )h0

h() (1 ) h()

h2 2

p () 2 s 2 2 , h0 () = 2 ,(12)

(1 ) 1/h + 1/[(1 ) h ]

a d

h() h + h () + h () + h .(13)

a 0

s

The ricing e

uations resemble those in Pro osition 1. They are altered intwo ways. First, the rice is now a function of the ast rot. The corres ondingsignal error u enters with a weight 0 . Naturally, 0 rises with the

recisionof the signal h0 , so it decreases when market ower rises. In particular,p0 = 0 when = 1 because the past prot is then uninformative. Second,investors total precision h is larger by the amount h0 , the recisionof thenew signal (com aring e

uation (13) to e

uation (4)). This extra term makesh nonmonotonic in market ower . Hence, t o opposing forces are at or

kas market po er strengthens. On the one hand, more market po er implies amore informative stock price (hp is higher, as in Proposition 3). On the otherhand, it means a less informative past prot (h0 is lower). Thistrade-off isillustrated in the to left anel of Figure 8, which shows that market oweris not unambiguously benecial to stock market efciency. Informational and

----------------------- Page 24-----------------------


0 0.2 0.4 0.6 0.8 1

10 10100

8 880

s ese m


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n ue 6 l 6

60v o

i vta gm n

ir 4 d 4

40o a

fn r

I T2 2

20

0 00

0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1

Mkt ower ()Mkt po er ()

0 0.2 0.4 0.6 0.8 10.014 3 1

0.012 2.5noi 0.9s 0.01r 2e yp cs 0.008

i nd e1.5 i 0.8

s ct i

0.006 fs fa Ec 1e 0.004r 0.7oF

0.002 0.5-3

x 100 0 0.6

0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1

Mkt po er ()Mkt po er ()

0 0.2 0.4 0.6 0.8 1


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0.8 0.1 5

4.50.08

0.64

y yt 0.06 ti ili dt 0.4 i 3.5a ul qo iV 0.04 L

30.2

0.022.5

0 0 20 0.2 0.4 0.6 0.8 1 0 0

.2 0.4 0.6 0.8 1

Weak Mkt po

er () Strong WeakMkt po er () Strong

Figure 8. The impact of market po er on the equilibrium hen a rms past prot isinformative about its technology shock. on the x-axis measures the rmspo er in itsproduct market. The top left panel displays the total precision of public signals (solid line) and itsbreakup bet een the information revealed by stock prices (hp , dashed line) andthat revealed byrms past performance (h0 , dash-dotted line). The to right anel dis lays the share turnover

(solid curve, right scale) and the dollar trading volume (dashed curve, left scale). The middle left anel dis lays the dis ersion of investors forecasts about the rms rot (solid lineright scale)and technology shock (dashed line, left scale). The middle right anel dis laysallocative efciency

P

a/

a . The bottom left

anel dis

lays the variance of log

rots (solid curve, right scale), thevariance of log rices (dashed curve, left scale), and the variance of stock returns (dotted curve, rightscale). The bottom right

anel dis

lays li

uidity 1/

. The

arameters are ha= 1, 2 = 0.1, r f =

0.02, M = 10, K0 = 1, w = 0.3, = 1, hs = 0.1, h = 0.1, h = 0.01, = 08, and z = 0.1.

allocative efciency a

e

u

t by ma

ket powe

for lo values of becausemuch information from the past prot is lost. They improve for high values of because much information from the stock price is gained. Similarly, volatilityand liquidity are nonmonotonous functions of market po er. The exception is


36/62

----------------------- Page 25-----------------------


trading volume, hich continues to gro ith . This is because the past protis a public signal so it does not generate differences in opinions and hencetrading.23

B. Noise Trading

Our analysis of market po er assumes that it cannot inuence the intensityof noise trading, that is, that 2 i

not a function of . One mayask ho

our ndings are affected by this assum tion. It is not clear a riori how noisetrading would change with market ower. On the one hand, if noise stems fromthe trades of rational agents subject to li

uidity needs, then it may strengthenin rms with more market ower as these agents build u a recautionary

osition in less volatile stocks. On the other hand, if noise is generated by risk-

neutral ca

ital-constrained investors whose

rivate signals contain systematicerrors, then it may be larger in stocks with less market ower as these agentstrade more aggressively stocks that are more sensitive to their signal.

If we su ose that noise trading is more intense among rms with moremarket ower (e.g., noise originates in li

uidity shocks), then s eculative tradesin these stocks are more easily concealed, which encourages informed trading.In this case, our ndings on trading activity are strengthened: The volumeof informed trading and total volume increase even more than when 2

i

independent of . But the impact of market po er on informativeness is no ambiguous since both informed and noise trading are more intense. It follo sthat the impacts on dispersion of forecasts and allocative efciency are

alsoambiguous. If e suppose instead that noise trading is less intense among rms ith more market po er (e.g., noise originates in correlated signal errors), thenthe results are reversed: Informed and total trading gro ith less than hen 2 i

independent of and may even be reduced in more monopolistic rms.

The net effects on informational and allocative efciency are again ambiguous.

C. Leverage

Product market com

etition inuences investors trading behavior becauseit affects the robability distribution of the cash ows rms generate. Morecom etitive rms offer ayoffs that are more sensitive to shocks and thereforeriskier (their variance is larger), so their stock is less actively traded. Weex ect the same argument to carry over to rms with higher o erationalor


37/62

nancial leverage. It is well known that rms for which o erating costsare

redominantly xed (e.g., rms with large R&D ex enditures) or those thatnance themselves mostly with debt offer ayoffs, are more sensitive to shocksand therefore more volatile. Investors will therefore be less rone to trade theirstock.

23Indeed, investors ortfolio shares do not de end on u, the error in the ast

rot signal. They

are still given by e

uation (5).

----------------------- Page 26-----------------------


The argument a lied to nancial leverage has interesting im lications forsecurity design. Asymmetric information is generally an im ortant concern forissuers. As Boot and Thakor (1993) show, rms nd it o timal to s lit claims totheir cash ows into informationally insensitive a d i formatio ally se si-tive claims, such as debt a d equity. This partitio stimulates i formed tradi g(i the i formatio ally se sitive security) a d the collectio of costly private

i

formatio

.24 Our

di

gs differ from those of Boot a

d Thakor (1993). While

i vestors i Boot a d Thakor (1993) favor the more i formatio ally se sitivesecurities, they shy away from them i our framework. This is because tradersi Boot a d Thakor (1993) are risk- eutral a d capital-co strai ed, whereashere they are risk-averse a d free to borrow. I Boot a d Thakor (1993), split-ti g claims avoids the eed for traders to tie their limited fu ds to securitieswith k ow payoffs, from which they have little to gai , a d allows them to co -ce trate i stead o assets with the greatest i formatio asymmetries. Putti git differe tly, they trade the most i formatio ally se sitive securities because

they are the riskiest, while they avoid tradi

g them i

our model precisely be-cause they are the riskiest. These co trasti g results illustrate the importa ceof i

vestors attitude toward risk a

d

a

ci

g co

strai

ts to the desig

ofsecurities. I the ext sectio , we co fro t the model with data.

V. Empirical Evide

ce

I this sectio , we test whether some of the models predictio s are supportedempirically. We describe i tur the sample formatio , the methodology a dvariable co

structio

, a

d the results.

A. Sample

Our sample co

sists of over 5,000 U.S. rms followed over a decade. It startsfrom all NYSE-, Amex-, a d NASDAQ-listed securities that are co tai ed i the CRSP-Compustat Merged database for the period 1996 to 2005. We retai stocks with share codes 10 or 11, remove a cial compa ies a d regulated i -dustries, a

d wi

sorize variables at the 1% level. The resulti

g sample co

tai

s28,172 rm-year observatio s a d 5,497 differe t rms with a average of 5years of data for each rm. We obtai corporate data a d ear i gs a ou ce-


38/62

me t dates from Compustat, daily stock retur s from CRSP, a d i sider tradesfrom Thomso

Fi

a

cial.

B. Methodology a d Variables

We co

duct a test of Propositio

s 2 a

d 3, which predict that rmsoper-ati

g i

more competitive i

dustries have a lower volume of trade a

dless

24Our paper treats the precisio of traders i formatio as exoge ous, u like Boot a

d Thakor

(1993). Nevertheless, i

vestors prope

sity to trade is suggestive of how valuableprivate i forma-tio

is.

----------------------- Page 27-----------------------

Product Market Competitio a d Stock Market Efcie cy27

i formative stock prices. For that purpose, we eed proxies for market power,tradi g volume a d stock price i formative ess. We describe them i tu

r

.Table I prese ts descriptive statistics.

Market power: We proxy for a rms market power usi g its pricecostmargi or Ler er i dex, de ed as the rms operati g prot margi (salesmi us costs divided by sales).25 Followi g Gaspar a d Massa (2005

), we

subtract the i dustry average pricecost margi to co trol for structuraldiffere ces across i dustries u related to the degree of competitio .26 T

he

resulti g excess pricecost margi (the differe ce betwee a rms operat-

i

g prot margi

a

d the average of its i

dustry) captures a rms abilityto price goods above margi al cost, adjusti g for i dustry-specic factorsu related to market power. A larger pricecost margi i dicates stro germarket power (weaker competitio

). Tradi g volume: To measure tradi g volume, we use a stocks tur over

,de ed as the log of the ratio of the umber of shares traded d

uri

g ayear to the umber of shares outsta di g. We also exami e the tr

adesi itiated by i siders to capture i formed tradi g. The Thomso Fi a cialI

sider Fili

g database compiles all i

sider activity reported to the SEC.

Corporate i

siders i

clude those that have access to

o

-public, material,i sider i formatio a d are required to le SEC form 3, 4, a d 5 whe theytrade i

their compa

ies stock. We follow most studies (e.g., Seyhu

1986,Lako ishok a d Lee (2001), Be eish a d Vargus (2002)) by limiti g i sidertrades to ope market tra sactio s i itiated by the top ve executi

ves(CEO, CFO, COO, Preside

t, a

d Chairma

of Board), as they are morelikely to possess private i formatio , a d by excludi g sells, because they

are more likely to be drive by hedgi g rather tha i formatio motives


39/62

(e.g., whe optio s vesti g periods expire). We measure i sider tradi

gactivity i two ways: rst, as the log of the ratio of a rms a ual total

i sider tradi g dollar volume to the rms market capitalizatio , de otedI sider tur over; a d seco d, as the ratio of the log of the rms a ual

umber of i

sider trades to the

umber of its active i

siders, de oted

Number of i

sider trades. Active i

siders are de ed as executives who

25The pricecost margi

is used i

Li

de

berg a

d Ross (1981), Gaspara d Massa (2005),

a d most of the empirical i dustrial orga izatio literature. Alter ative proxies based o

assetor sales co ce tratio such as the Her dahlHirschma i dex are i dustry- rather tha

rm-specic. Moreover, because data are limited to U.S. public rms, they do ot accou tfor private orforeig rms. This is especially problematic give that our sample (1996 to 2005)covers a periodof i te se global competitio .

26Specically, the pricecost margi (PCM) is de ed as operati g prots (before depeciatio ,

i

terest, special items, a

d taxes) over sales (Compustat a

ual data item 12).Operati g prots areobtai ed by subtracti g from sales the cost of goods sold (item 41) a d ge erala d admi istrativeexpe ses (item 178). If data are missi g, we use operati g i come (item 178). The excess pricecostmargi is co structed as the differe ce betwee the rms PCM a d the PCM of its i dustry. Thei dustry PCM is the value-weighted average PCM across rms i the i dustry where the weightsare based o market share (sales over total i dustry sales) a d i dustries are de ed usi g two-digit SIC classicatio s.

----------------------- Page 28-----------------------

28 The Jour

al of Fi

a

ceR

Table IDescriptive Statistics

This table prese

ts summary statistics for the variables used i

the empirical study. The samplestarts from all NYSE-, Amex-, a d NASDAQ-listed securities that are co tai ed i the CRSP-Compustat Merged database for the period 1996 to 2005. We retai

stocks withshare codes 10

or 11, remove

a

cial compa

ies a

d regulated i

dustries, a

d wi

sorizevariables at the 1%level. Market power is measured as the excess pricecost margi

(PCM) or Ler

er i

dex. The PCMis de ed as operati g prots (before depreciatio , i terest, special items, a d taxes) over sales(Compustat a

ual data item 12). Operati

g prots are obtai ed by subtracti g fromsales the costof goods sold (item 41) a d ge eral a d admi istrative expe ses (item 178). If data are missi g,


40/62

we use operati g i come (item 178). The excess pricecost margi is co structed asthe differe

cebetwee the rms PCM a d the PCM of its i dustry. The i dustry PCM isthe value-weightedaverage PCM across rms i the i dustry, where the weights are based o market share (salesover total i dustry sales) a d i dustries are de ed usi g two-digit SIC

classicatio s. Size ismeasured as the log of rms assets. Illiquidity is measured usi g the Amihud (2002) illiquidityratio a d equals the ratio of a stocks absolute retur to its dollar tradi g volume i

a day, averagedover all days i a year, a d scaled by 106 . Retur o assets is de ed as i comebefore extraordi

ary

items (item 18) over total assets. Leverage is computed as total lo

g-term debt(item 9) dividedby total assets (item 6). Market-to-book is the ratio of the market value of equity (year-e d stockprice times the umber of shares outsta di g) to its book value. Book

equity is co structed asstockholders equity (item 216, or 60 + 130, or 6-181, i that order) plus bala ce sheet deferredtaxes a d i vestme t tax credit (item 35) mi us the book value of preferred stoc

k (item 56, or 10,or 130, i that order). Tur over is de ed as the log of the ratio of the umber of shares tradedduri g a year to the umber of shares outsta di g. I sider trades are ope market tra sactio s,excludi g sells, i itiated by the top ve executives of a rm (CEO, CFO,

COO, Preside t, a dChairma of Board). I sider tradi g activity is measured i two ways: rst, as thelog of the ratioof a rms a ual total i sider tradi g dollar volume to the rms market capitalizatio, de otedI sider tur over; seco d, as the ratio of the log of the rms a ual umber of i sider trades to

the

umber of its active i

siders, de

oted Number of i

sider trades. Active i

siders are de edas executives who have reported at least o e tra sactio i a y of the sample years. Stock pricei formative ess is measured as (the i verse of) the absolute ab ormalretur surrou di g a ear i gs a ou ceme t. Ab ormal retur s are measured as the residuals from the FamaFre

chthree-factor model, obtai ed by regressi g for every rm stock retur s o the market, size, a dbook-to-market factors over a estimatio wi dow exte di g from t = 250

to t = 5

elativeto t

e ea

nings announcement day 0. We estimate t

e

esiduals ove

an event wind

ow

angingf

om t = 2 to t = +2. T

en, we sum t

e absolute value of abno

mal

etu

ns on eac

day of t

eevent window. Finally, we ave

age t

e absolute abno

mal

etu

ns estimates obtained f

om eac

announcement du

ing a yea

to get an annual measu

e. We also

epo

t t

e ave

ageabsolute

aw

etu

n ove

t

e event window.

Mean Median Std. Dev. Min.


41/62

Max. N

Numbe

of yea

s 5.125 4 3.321 110 5,497

pe

mMa

ket powe

0.142 0.115 0.119 0.1073.614 26,264

Size 5.607 5.516 1.891 0.501 11.743 26,946Illiquidity 4.792 0.053 44.737 0.000 3 194 28,103Retu

n on assets 0.029 0.045 0.374 432.188 26,972

Ma

ket

to

book 0.380 1.923 555 90,0226,365 26,495

Leve

age 0.191 0.143 0.206 0.000 3.862 26,816Tu

nove

0.127 0.076 1.006 6.52910.313 26,405

Inside

tu

nove

0.007 0.001 0.025 0.000 1.051 26,795Numbe

of inside

0.246 0.147 0.337 0.000 4.868 24,676

t

ades

(continued)

Page 29

P

oduct Ma

ket Competition and Stock Ma

ket Efciency29

Table IContinued

Mean Median Std. Dev. Min.Max. N

Abs. 5

day

aw 0.151 0.135 0.078 0.0000.845 24,827

etu

n a

oundea

ningsannouncements

Abs. 5

day abn. 0.149 0.133 0.078 0.0090.855 24,827

etu

n

elativeto t

et

ee

facto

model a

oundea

nings

announcements

ave

epo

ted at least one t

ansaction in any of t

e sample yea

s (Ke,Hudda

t, and Pet

oni (2003)). Ea

nings announcements: We use t

e stock p

ice

eaction to ea

nings an

nouncements to assess t

e info

mativeness of stock p

ices. La

ge (small)p

ice c

anges a

e indicative of

emotely (closely) followed

ms, as s

ownby nume

ous studies sta

ting wit

Beave

(1968). We measu

e t

e abso

lute abno

mal

etu

n ove

a 5

day window cente

ed on days ea

nings a

e


42/62

announced. Abno

mal

etu

ns a

e dened

elative to t

e FamaF

enc

(1993) t

ee

facto

model.27 We sum t

ei

absolute value on eac

day of

t

e event window to measu

e t

e stock p

ice

eaction to t

e announce

ment, and take t

e ave

age ove

all announcements in a yea

toobtain

an annual measu

e (consistent wit

ou

p

oxy fo

ma

ket powe

and ot

e

cont

ol va

iables).

We examine t

e impact of ma

ket powe

in sepa

ate panel

eg

essions fo

tu

nove

, inside

t

ading, and stock p

ice info

mativeness. We co

ect standa

de

o

s fo

se

ial and c

oss

sectional co

elation using yea

and

m cluste

s.28

Ou

eg

essions include cont

ols fo

seve

al facto

s, suc

as m size, equityma

ket

to

book

atio, liquidity, p

otability, and leve

age, t

at may be associ

ated wit

t

ading activity o

wit

eactions to announcements.29

27Specically, absolute abno mal etu ns su ounding an ea nings announcement a

e dened as+2 m

t=2 ut , w

e

e t = 2, 1, 0, +1, and +2 count t

ading days

elative to t

e announcement day 0

fo

m m, um = Rm (m + m MKT + m SMB + m HML ), Rm is the returm mst t 0 mkt t SMB t HML t t

stock on y t, n MKT , SMB , n HML re respectively the returns onthe m

rket, size

n t t t

book

to

m

rket f

ctors on

y t. The coefcients m, m , m ,

n m

re estie for every

0 mkt SMB HMLrm over

win ow r

nging from t = 250 to t = 5.28We correct st

n

r errors using the proce ure outline in Thompson (2006)

n C

meron,

Gelb

ch,

n

Miller (2006)).29Firm size is me

sure

s the log of rms tot

l

ssets (Compust

t item 6). Lever

ge is compute

s tot

l long

term ebt (item 9) ivi e by

ssets. The m

rket

to

book r

tio is ene

s the r

tioof the m

rket v

lue of equity (ye

r

en stock price times the number of sh

res outst

n

ing) to itsbook v

lue. Book equity is constructe

s stockhol ers equity (item 216, or 60 +130, or 6 181,in th

t or er) plus b

l

nce sheet eferre t

xes

n investment t

x cre

it (item 35) minus the

P

ge 30

30 The Journ

l of Fin

nceR

T

ble IIM

rket Power

n TurnoverThis t

ble prese

peress competition

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