do tracking stocks reduce informational asymmetries by elder et al. (jfr 2005)

7/29/2019 Do Tracking Stocks Reduce Informational Asymmetries by Elder Et Al. (JFR 2005)

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The Journal of Financial Research Vol. XXVIII, No. 2 Pages 197213 Summer 2005

DO TRACKING STOCKS REDUCE INFORMATION ASYMMETRIES?AN ANALYSIS OF LIQUIDITY AND ADVERSE SELECTION

John Elder

North Dakota State University

Pankaj K. Jain

University of Memphis

Jang-Chul Kim

North Dakota State University

Abstract

A firms announcement that it intends to restructure based on tracking stock is

usually associated with a positive stock price reaction, at least in the short run.

Typically, this reaction is attributed to expected reductions in a diversification

discount, through reduced agency costs or information asymmetries. We rein-

vestigate this latter hypothesis by focusing on the liquidity provided by market

makers before and after a firm issues a tracking stock. Our results suggest that

such restructurings are not effective at reducing information asymmetries. Rather,

firms that issue tracking stocks exhibit less liquidity and greater adverse selection

than comparable control firms.

JEL Classifications: G14, G34

I. Introduction

Tracking stock is a unique form of corporate restructuring in which a mul-

tisegment firm creates a new class of shares whose value is linked to a particular

business segment. An important feature of a restructuring based on tracking stock is

that additional financial disclosures are required, whereby the parent firm (i.e., gen-

eral division) and the tracked segment (i.e., business group) file separate financialstatements with Securities and Exchange Commission (SEC). Some researchers

suggest that these additional disclosures may improve the information environ-

ment, thereby reducing information asymmetries among investors. The theoretical

and empirical evidence on this effect, however, is ambiguous. We detail the issues

involved and reexamine the effect of restructurings based on tracking stock on

We thank Matthew Billett (the reviewer), Ken Small, and participants at the 2004 Midwest Finance

Association conference for useful comments. Any errors are our own.

197


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198 The Journal of Financial Research

information uncertainties by using market-microstructure-based tools. In particu-

lar, we examine changes in the liquidity provided by market participants following

the issuance of tracking stocks. Our results contribute to the existing literature on

tracking stocks as well as the growing literature on the relation between equityrestructurings and market liquidity.

Although recent research shows that, ex post, tracking stocks underper-

form the usual benchmarks either significantly or insignificantly (e.g., Billett and

Vijh 2004; Clayton and Qian 2002), initial announcements of an intention to is-

sue a tracking stock tend to increase firm value in the short term. Billett and

Mauer (2000), DSouza and Jacob (2000), and Elder and Westra (2000) docu-

ment positive abnormal returns between 2% and 4% in the days surrounding such

announcements. These gains are typically attributed to expected reductions in a di-

versification discount through reduced information asymmetries or reduced agency

costs.For example, Zuta (2000) finds that multisegment firms with tracking

stocks have lower diversification discounts than comparable firms, whereas Billett

and Mauer (2000) find that such firms tend to have lower diversification discounts

before the tracking stock is issued. Harper and Madura (2002) find evidence that

the tracking stock structure reduces agency costs in multisegment firms.

Several studies investigate the effect of tracking stocks on information

asymmetries. The usual premise is that because the SEC requires the disclosure of

additional financial statements detailing the performance of the general division as

well as the tracked business group, analysts can better focus on the performance of

each segment. This increases both the number of analysts following the firm and,because analysts tend to specialize in particular industries, the accuracy of their

forecasts. Both of these factors may reduce information asymmetries.

There are, however, theoretical and institutional factors that may counter

this effect, making the net effect of the tracking stock structure on information

asymmetries ambiguous. The institutional factors include accounting and corporate

governance issues associated with the tracking stock structure. For example, a

tracking stock does not represent a legal claim on the assets of the associated

business group. Instead, a tracking stock represents a claim on a fraction of the

assets of the consolidated firm, where, in the event of liquidation, the claim typically

depends on the proportion of the total market value accounted for by each class of

stock. It may therefore be difficult for analysts to value the general division and the

tracked business groups based on their liquidation values.

A tracked business group is also not governed by an independent board

of directors. Rather, the tracked business group is governed by the directors of

the parent firm, with the shareholders typically having voting rights that float

with the market value of their tracking stock relative to that of the total market

capitalization of all classes of common stock for the firm. Such voting rights imply

that the directors will answer to at least two groups of shareholders with potentially


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Tracking Stocks 199

different and competing interests, with the interests of the tracked group subordinate

to the interests of the consolidated firm. This aspect of the tracking stock structure

may introduce substantial uncertainties about how the tracked business group will

be strategically managed relative to pure plays in the same industry and createdifficulties in valuing the various business segments as going-concerns.

In addition, formal theoretical foundations suggest that restructuring a firm

into various business segments does not reduce information asymmetries. For ex-

ample, multisegment firms may diversify away segment-specific information asym-

metries, as formalized by Gorton and Pennacchi (1993).

The empirical evidence on whether an equity structure based on tracking

stock reduces information asymmetry is mixed. With regard to analyst coverage,

DSouza and Jacob (2000) do not find any significant increase in coverage after

a firm issues a tracking stock,1 whereas Zuta (2000) and Chemmanur and Paeglis

(2000) find increased analyst coverage. Chemmanur and Paeglis interpret theirresults as indicating that decreased information asymmetries are likely to have a

positive effect on firm valuation, at least in the short run. In contrast, Billett and

Vijh (2004) measure analyst earnings forecast errors, the dispersion of earnings

forecasts, and the market reaction to earnings announcements, each before the

tracking stock announcement and after issuance. Their analysis suggests that there

is little or no decline in information asymmetry for the general division and some

increase for the tracked business groups.

An alternative and more direct measure of information asymmetry, how-

ever, is based on the liquidity provided by market participants. The extant literature

indicates that market makers provide less liquidity during periods of greater in-formation asymmetry, that is, when they perceive a higher probability of trading

with more informed traders. For example, Lee, Mucklow, and Ready (1993) find

that such adverse-selection costs induce market makers to widen spreads around

earnings announcements. Similarly, if a corporate restructuring based on tracking

stock affects information asymmetries, it should be possible to discern the sign

and magnitude of the effect by examining the liquidity provided by market mak-

ers during the relevant period. Similar empirical investigations are conducted, for

example, by Huson and Mackinnon (2003) in the context of spin-offs.

Given the contradictory theoretical literature, the ambiguous empirical re-

sults, and the availability of more direct measures of information asymmetry, further

empirical investigation seems warranted. As such, we examine the behavior of liq-

uidity providers before and after a firm issues a tracking stock. If the restructuring

effectively reduces information asymmetries through the release of more detailed

financial statements on the various business segments, we should expect to see

1DSouza and Jacob (2000) argue that the main motivation for issuing tracking stocks is their tax-free

nature compared with spin-offs, which create tax liabilities.


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increased liquidity, as measured by narrower spreads, after the tracking stock struc-

ture is implemented. If, however, the other aspects of tracking stocks detailed earlier

substantially counter this effect, or if such restructurings tend not to reduce infor-

mation uncertainties for the reasons cited previously, the effect on liquidity afterthe tracking stock structure is implemented may be negligible, or even negative.

We conduct our analysis on the basis of 28 tracking stock issues between

1984 and 2002, using data for liquidity variables from the Institute for the Study of

Security Markets (ISSM) at the University of Memphis and the Trade and Quote

Database (TAQ) from the New York Stock Exchange. Anticipating the principal

results, we find that after a tracking stock structure is issued, there is a relatively

small and insignificant increase in liquidity for the general division, relative to a

large and marginally significant increase in liquidity for our control sample, which

is consistent with documented market trends. Moreover, the adverse-selection com-

ponent of the total spread increases significantly after firms issue tracking stock.The effects on the tracked divisions are qualitatively similar, although less signif-

icant statistically. Tracked divisions have substantially less liquidity and greater

adverse selection than a sample of matched control firms. Finally, cross-sectional

regressions reveal that the observed effects on the general division are not driven

by a subset of firms with particular characteristics. Rather, the effects are systemic

throughout our sample.

Our results, based on more direct measures of information asymmetry,

reinforce the empirical findings of Billett and Vijh (2004). Markets may interpret

announcements to issue tracking stocks as value increasing events in the short

run, but the actual issue of tracking stocks is not likely to reduce the informationasymmetries. More generally, our results are consistent with those of Huson and

Mackinnon (2003), who find that spin-offs do not improve liquidity, although our

sample is not large enough to discern statistically significant differences between

restructurings that improve focus and those that do not. The failure of restructurings

based on tracking stocks to mitigate information asymmetries is likely the result of

either the additional uncertainties introduced by this unique form of restructuring

or the more general failure of corporate divestitures to cause any improvements in

liquidity.

II. Tracking Stocks

Tracking stock, also known as targeted or lettered stock, is a class

of common stock whose value is linked to the performance of a specific business

group within a diversified firm. Since its introduction in 1984, nearly 60 firms have

issued or announced plans to issue tracking stock, with a disproportionate amount

in the late 1990s. A common justification for issuing tracking stock is that it unlocks

the hidden value of a business segment by separating it, to some degree, from the

parent. Since 1998, the tracked business group has often been, or was intended to


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Tracking Stocks 201

be, an Internet pure play, such as those proposed or issued by Donaldson, Lufkin

& Jenrette (DLJ Direct), Staples (Staples.com), New York Times (Times Company

Digital), Korn Ferry (Futurestep.com), and others.

Despite its increasingprevalence, however, tracking stock is not particularlywell understood. The creation of tracking stock for a business group within a

diversified firm is nominally similar to spinning off or carving out the division, in

that each of the restructurings creates a new security whose value is linked to the

associated business group.2 The differences among the three forms of restructuring,

however, are considerable. Under both spin-offs and carve-outs a new corporate

entity is created with shareholders possessing the conventional rights: the right to

elect a board of directors to oversee management, the right to vote on matters of

great importance, and a claim against the new entitys net assets.

A tracking stock, however, does not represent a new corporate entity. A

tracking stock structure is formed by creating a new class of common stock whosevalue is linked to the performance of a specific business group through special

provisions introduced into the firms articles of incorporation. This link is usually

strongest through a limited claim on the earnings generated by the division. Typ-

ically, the dividends paid to the owners of tracking stock depend on the earnings

generated by the tracked group, expressed as function of shareholders equity or

net income, although many firms issuing tracking stock indicate that earnings for

the tracked group are not likely to be positive in the near future. A substantial

complication of the tracking stock structure is that the tracked group may dispro-

portionately share with the parent firm the cost of fixed inputs, such as corporate

offices and payroll services, that it otherwise would not share as a separate corporateentity.

Another complication introduced by the tracking stock structure relates

to the allocation of the firms physical assets to the various business groups. In

particular, a tracking stock does not represent a legal claim on the assets of the

associated business group. Instead, tracking shareholders typically have a claim

on a fraction of the assets of the consolidated firm, where that fraction fluctu-

ates with the proportion of the total market value accounted for by each class of

stock.

Probably one of the most controversial aspects of a tracking stock is that the

tracked group is governed by the directors of the parent firm rather than by its own

board. This suggests that the interests of the tracked group will be dominated by the

interests of the consolidated firm, potentially introducing serious conflicts of inter-

est (Haas 1996). It also creates the opportunity for considerable cross-subsidization

across business groups, either through exposure to the liabilities of the consolidated

2Shares in the tracking stock may be distributed either as a public offering, as dividends to existing

shareholders, or as currency for an acquisition.


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firm or through purposeful redirection of resources. These features of tracking stock

significantly curtail the extent to which a tracked business group can be considered

a pure play. Some firms have even indicated the potential for such conflicts in

regulatory filings, such as Sprint has with its two tracking stocks, FON Group andPCS Group.

These unconventional features may account for the range of opinions ex-

pressed in the financial press. Headlines range from numerous claims, including

many by practitioners, that tracking stocks unlock value3 to On the Wrong Track:

Complex Financial Innovations Like Tracking Stocks . . . Bring Few Benefits to

Shareholders.4 More recently, critical press seems to dominate, with headlines

such as Sprint Shows Pitfalls of Investing in Tracking Stocks.5

III. Tracking Stocks and Liquidity: Testable Hypotheses

Glosten and Milgrom (1985) depict that, in the presence of information

asymmetry, market makers earn the bid-ask spread from uninformed noise traders,

who trade for liquidity reasons, and lose the difference between the full-information

value of the stock and trade price given to informed traders, who trade on the basis

of private information. The magnitude of the spreads depends on the proportion of

liquidity traders and informed traders, which affects the probability of trading with

an informed trader, which is known as adverse selection. Several studies analyze

bid-ask spreads to investigate empirically the adverse-selection environment in a

market. Lee, Mucklow, and Ready (1993) and Krinsky and Lee (1996) examine

earnings announcement effects for the existence of asymmetric information about

expected earnings. They find significant and increasing adverse-selection costs

around earnings announcements, with market makers widening spreads and de-

creasing quoted depth immediately before and after earnings announcements. Kim

and Verrecchia (1994) argue that spreads widen because earnings announcements

provide new information that allows certain traders to make judgments about a

firms valuation that are superior to the judgments of other traders.

The main hypothesis we test is that if the additional financial disclosures

on the tracked business groups effectively mitigate the information asymmetrybetween informed and uninformed investors, and more generally, if the focus-

increasing events reduce information asymmetries, we should expect that bid-ask

3Genzyme Tracking Stocks Are Off Track on Returns, June 24, 1999; Firms Turn to Tracking

Stocks to Unlock Value of Web Units, July 12, 1999, Dow Jones Newswire; Shares that Track Assets Add

Value at a Cost, July 18, 1999, New York Times, p. 3.7.4Complex Financial Innovations Like Tracking Stocks Allow Managers to Retain Control, but Bring

Few Benefits for Shareholders, May 18, 1999, Financial Times, p. 22.5Sprint Shows Pitfalls of Investing in Tracking Stocks, March 7, 2003, Wall Street Journal, p. C1.


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Tracking Stocks 203

spreadsin particular, the adverse-selection component of spreadsto decrease

after a firm issues tracking stock.

In contrast, rejection of this hypothesis is evidence that the unique aspects

of the tracking stock structure mitigate any effect of increased financial disclo-sure, or more generally, that corporate divestitures do not to reduce information

asymmetries. As discussed previously, both the theoretical and empirical evidence

on this hypothesis is ambiguous, although recent empirical evidence, such as that

provided by Huson and Mackinnon (2003), suggests it may be rejected.

Finally, the magnitude of change in spreads may depend on the motives

for the restructuring and the parent firms characteristics. For example, Harper and

Madura (2002) find that some firm-specific characteristics related to corporate

governance help explain cumulative abnormal returns around a firms announce-

ment of a tracking stock. Such variables may also be associated with changes in

liquidity around the announcement and issue dates. We examine this possibility ina cross-sectional regression.

IV. Data Sources and Empirical Method

Tracking Stock Issues

Our initial sample consists of 28 tracking stock issues occurring between

1984 and 2002, for which ISSM and TAQ data are available. Table 1 catalogs these

issues, with the ticker symbol for the general division, the tracked division, andthe control firm. The control firms are selected based on a matching procedure, as

described later. Because of the accounting irregularities surrounding WorldCom,

we exclude it from our analysis, although its inclusion has no significant effect on

our results.

Liquidity Variables

Data for liquidity variables are obtained from ISSM and TAQ. We extract

bid-ask quotes, transaction prices, and volume for these firms for every transaction

in our sample windows. Our sample windows consist of a benchmark window that

is in the range of (100, 93) days relative to the announcement, an issue window

that is (0, +1) days relative to the issue of the tracking stock, and two post-issue

windows (+13, +14) days and (+30, +31) days relative to the issue of the tracking

stock. The recorded announcement date is the date when news of the tracking stock

appeared in a printed news source.

Each observation in the data file includes the quote date, time stamp, ticker

symbol, bid price, ask price, bid depth, ask depth, and exchange code where the

quote originated. Our initial sample has more than 1 million quote observations. We


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TABLE 1. Tracking Stock Issuances.

Date Parent Parent Track Control

Issued Corporation Notes Ticker Ticker Ticker

1984 1019 General Motors Acquisition GM GME GE1992 0925 USX Focus MRO DGP UCL

1993 0802 Ralston Purina RAL CBG ENE

1994 1216 Genzyme Corp. Acquisition GENZ GENZL SMED

1995 0811 Tele-Communications Inc. Simultaneous TCOMA LBTYA ORCL

1995 0726 American Health Properties Focus AHE AHEPZ ABF

1995 0721 CMS Energy Corp. CMS CPG JR

1995 1101 US West USW UMG NYN

1996 0909 Inco Limited Acquisition N NVB UEP

1998 0302 Delmarva Power and Light Acquisition DEW CIV CLN

1997 0204 Circuit City Stores Inc. CC KMX IGL

1997 0917 Tele-Communications Inc. TCOMA TCIVA PCCW

1998 1117 Genzyme Corp. Acquisition GENZ GZTR PAIR 1997 1217 Georgia-Pacific Corp. Focus GP TGP AMR

1998 1124 Sprint Acquisition/focus FON PCS AUD

1999 0506 Perkin-Elmer PKN CRA TOT

1999 0331 Ziff-Davis ZD ZDZ GIM

1999 0804 Quantum Corporation QNTM DSS PSFT

1999 0526 Donaldson, Lufkin & Jenrette DLJ DIR GD

1999 1029 Snyder Communications Focus SNC CIRC IM

1999 1118 Walt Disney Co. DIS GO PEP

1999 0216 Genzyme Corp. GENZ GZMO CNTO

2000 0427 ATT T AWE SBC

2000 0907 Andrx ADRX CYBA CITC

2001 0330 Cablevision Systems Corp. CVC RMG USM

2000 0928 Apollo Group Inc. APOL UOPX ASBC2000 1020 Alcatel Simultaneous ALA ALAO PWJ

2001 0608 WorldCom Simultaneous WCOM MCIT

2002 0201 Loews Corp. LTR CG LNC

Note: This table summarizes the sample of tracking stock issuances. The first column gives the issue date

and is followed by name of the parent firm; a note on the status of the restructuring; and ticker symbols

for the parent, the tracked business group, and the associated control firm. A tracking stock is defined as

focus improving if it has a different two-digit Standard Industrial Classification code from the parent.

apply the following data filters, which are standard in the microstructure literature

(e.g., Huang and Stoll 1996), to clean the data of errors and outliers:

1. Delete quotes if either the bid price or the ask price is negative.

2. Delete quotes if either the bid size or the ask size is negative.

3. Delete quotes if the bid-ask spread is greater than $4 or is negative.

4. Delete trades and quotes if they are out of time sequence or involve an

error.

5. Delete before-the-open and after-the-close trades and quotes.

6. Delete trades if the price or volume is negative.

7. Delete trades and quotes if they changed by more than 10% compared

with last tick.


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Tracking Stocks 205

8. Delete unlisted firms and other firms that are missing in TAQ on any

event date.

9. Delete firms that are missing in the Center for Research in Security

Prices (CRSP) or Compustat.

This filtering process reduced the number of usable observations by about 2%.

We compare spreads, volume, number of trades, and an adverse-selection

component of spreads for the periods indicated earlier. Spreads are defined as

follows:

Quoted spread= (Ask price Bid price), (1)

Effective spread= |Transaction price Quote midpoint| 2, (2)

Percentage (orRelative) spread= Quoted spread/Quote midpoint, (3)

Percentage (orRelative) effective spread= Effective spread/Quote midpoint.(4)

Quoted spreads represent the ex ante expected costs of trading. Effective

spreads reflect the price improvement received in a trade and represent the actual

ex post cost of liquidity. Although we present results for both quoted and effective

spreads, these two measures should be viewed as alternative expressions of the same

concept. Spreads are inverse measures of liquidity, and higher spreads indicate poor

liquidity. When an adverse-selection problem is severe, market makers widen their

spreads to recover the increased costs of trading with informed traders. To better

gauge changes in adverse selection, we use Glosten and Harriss (1988) model to

decompose the spread. In their model, the adverse-selection, inventory-holding,

and order-processing components are expressed as a linear function of transaction

volume. The model can be described in the following equation:

Pt = c0Qt + c1QtVt +z0 Qt +z1 QtVt + t, (5)

where Qt is a trade indicator that is+1 if the transaction is buyer initiated and1ifit

is seller initiated,Pt is the transaction price at time t, Vt is the volume traded at time

t, andt captures innovations in public information and specification error. In themodel, the adverse-selection component is Z0 = 2(z0 + z1Vt), which, if estimated

to be negative or greater than 1, is dropped from the sample. The inventory-holding

and order-processing components are given by C0 = 2(c0 + c1Vt). Employing

the usual procedure for trade classification,6 an estimate of the adverse-selection

component is

6Trades are defined as buys (sells) if the trade price is greater (less) than the bid-ask midpoint. We

define the quotes as the most recent quotes that were time stamped at least five seconds before the trade.

Changing this interval from five seconds to zero seconds in a robustness test did not affect our conclusions.


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Zi =2(z0,i +z1,i Vi )

2(c0,i + c1,i Vi ) + 2(z0,i +z1,i Vi ), (6)

where Vi is the weekly average transaction volume for stock i. This measure yieldsthe proportion of total spread that is due to adverse selection. To obtain the adverse-

selection component of the spread in dollar terms, we multiply these proportions

by the dollar spreads.

Changes in spreads are calculated as the difference between mean value of

the liquidity variable over the window of interest relative to the mean value over

the benchmark. For example, the abnormal spread (AS) for each firm over the issue

window is computed as

ASIssue = Issue spreadBenchmark spread. (7)

We then perform t-tests to examine whether these differences are statistically dif-

ferent from zero.

Robustness Tests

To test for robustness, we first use an alternative benchmark window of

(14,13), in addition to the primary benchmark of (100,93), to ensure that our

results are not driven by nonrepresentative benchmark windows. These alternative

benchmarks yield qualitatively similar results.

Second, we add a matched control sample to rule out the possibility thatour results are driven by a trend in spreads over time. For each announcement

we find a matching firm by employing Huang and Stolls (1996) method. The

matching criteria include (1) share price, (2) market capitalization, and (3) volume

to minimize the following expression:

3i=1

XG Di X

Controli

XG Di + X

Controli

2

2, (8)

whereXGD

i denotes matching variable i for the general division issuing the trackingstock, andXControli denotes the value of matching variable i for the control stock. We

find relatively good matches for each firm in the sample, with composite matching

scores of 0.10 or less. Studies adopting this type of matching procedure typically

impose a maximum value of 1.00. Table 2 reports the mean, standard deviation, and

percentile statistics of share price, market capitalization, and trading volume for

the 28 tracking stock issuers and the matched control sample of 28 firms. Table 2

reveals that firms issuing tracking stocks tend to be large in terms of market


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Tracking Stocks 207

TABLE 2. Descriptive Statistics.

PercentileStandard

Variable Sample N Mean Deviation Min 25th 50th 75th Max

Share price ($) Issued 28 40.56 21.80 6.50 22.23 35.47 58.05 91.34

Control 28 42.61 23.95 6.78 23.17 39.69 50.42 110.53

Market cap Issued 28 11,640.92 21,166.55 433.97 1,970.35 4,554.35 9,273.43 100,497.25

($M) Control 28 11,088.77 20,061.35 449.48 1,842.26 4,567.46 8,973.59 95,815.10

Volume (000) Issued 28 1,005.77 1,310.69 27.20 227.43 499.20 926.60 5,185.30

Control 28 1,000.45 1,323.20 27.00 233.60 432.20 985.72 4,204.65

Note: This table presents summary statistics on the share price, market capitalization, and trading volume

for 28 firms that issued tracking stock, and for the firms serving as matched controls for each sample.

To construct the matched control samples, we minimize an objective function over three observable firm

characteristics: average share price, market capitalization, and average daily trading volume. The objective

function is3

i=1

XGDi X Controli

XGDi

+ X Controli

2

2

,

where XG Dki denotes the value of matching variable i for each f irm issuing a tracking stock, and

X Controli denotes the same value for each control firm.

capitalization (approximately $4.5 billion) and relatively liquid, with high trad-

ing volumes. This is not surprising given that a tracking stock is a restructuring tool

for multisegment f irms.

Cross-Sectional RegressionsFinally, we examine whether the changes in liquidity are associated with

cross-sectional differences in firm characteristics. In particular, we regress abnor-

mal spreads during the issue window on a dummy variable for whether the tracking

stock was issued to finance an acquisition, the dollar value of sales, dollar value

of total debt, the ratio of price to earnings, the ratio of market to book value, the

dollar value of assets, the size of the tracked group relative to the parent, and a

dummy variable for whether the tracking stock was focus improving. We follow

Huson and Mackinnon (2003) and define a restructuring as focus improving if the

tracked division has a different two-digit Standard Industrial Classification (SIC)

code from the general division. These data are obtained from CRSP, Compustat,

and Lexis-Nexis.

V. Empirical Results

Our empirical results are reported in Tables 3 and 4 and Figure I. Ta-

ble 3 presents the liquidity and volume statistics for the sample of 28 issuers of

tracking stock and their controls. The first column indicates the statistic reported,


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TABLE 3. Liquidity and Volume Before, During, and After Tracking Stock Issues.

Post-Issue

(+30,+31)

Benchmark Issue Post-Issue Post-Issue Minus

Sample N (100,93) (0,+1) (+13,+14) (+30,+31) Benchmark

Quoted spreads (cents)

Issued 28 16.84 16.35 15.81 15.68 1.16

Nonfocus 23 16.76 16.67 16.40 16.23 0.53

Focus 5 17.11 15.09 13.22 13.27 3.95

Control 28 17.22 13.96 13.84 14.34 2.88

Relative quoted spreads

Issued 28 0.54 0.50 0.47 0.47 0.07

Nonfocus 23 0.55 0.50 0.46 0.46 0.09

Focus 5 0.49 0.62 0.51 0.52 0.03

Control 28 0.46 0.42 0.43 0.47 0.01

Effective spreads (cents)Issued 28 13.58 12.64 12.27 11.88 1.70

Nonfocus 23 13.61 13.16 12.58 12.36 1.25

Focus 5 13.93 10.49 10.86 10.23 3.70

Control 28 13.89 10.96 10.86 10.86 3.03

Adverse selection (% of total spread)

Issued 28 19% 30% 28% 27% 0.08

Nonfocus 23 18% 31% 29% 28% 0.10

Focus 5 31% 28% 27% 26% 0.05

Control 28 22% 22% 21% 22% 0.00Average daily volume ($ millions)

Issued 28 43.78 83.15 60.27 58.45 14.67

Nonfocus 23 40.14 89.29 56.74 54.93 14.79

Focus 5 59.07 57.33 75.81 73.96 14.89

Control 28 37.15 41.31 37.25 37.29 0.14

Average number of trades

Issued 28 483 652 566 677 194

Nonfocus 23 480 683 572 700 220

Focus 5 498 521 544 573 75

Control 28 682 735 662 659 23

Note: This table reports three measures of liquidity (spread, percentage spread, and effective spread),

a measure of adverse selection, and two measures of volume (dollar volume and number of trades) for

two samples: the sample of 28 firms implementing the tracking stock, and the 28 firms serving as a

control sample. The measurement windows include the benchmark window (100,93) days before the

announcement and three windows relative to the actual issue: (0,+1), (+13,+14), and (+30,+31). The

final column reports the difference between the post-issue value (+30,+31) and the primary benchmarkvalue (100,93).

Significant at the 1% level.Significant at the 5% level.Significant at the 10% level.

the second column indicates the sample, and the third column indicates the num-

ber of observations. The remaining columns indicate the respective windows over

which the liquidity and volume statistics are calculated. For example, the fourth

column reports the statistics over the benchmark period, the fifth column reports


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Tracking Stocks 209

TABLE 4. Cross-Sectional Regression of Liquidity During Issue of Tracking Stocks.

Variable Post-Issue Spread Minus Benchmark Spread

Intercept 0.0163

(0.43)Acquisition 0.0396

(1.10)

Sales 0.1567

(1.27)

Total debt 0.0001

(0.08)

PE ratio 0.0582

(0.66)

Market-to-book ratio 0.0003

(0.15)

Total asset 0.1278

(

1.16)Relative size (track/parent) 0.0156

(0.31)

Nonfocus dummy 0.0291

(0.84)

N 28

R2 0.21

Note: This table reports the results of a regression of the abnormal spread during the issue window on

variables related to firm and event characteristics. The variables related to the firm and event characteristics

are a dummy variable equal to 1 if the tracking stock was issued to finance an acquisition, and 0 otherwise;

dollar value sales; dollar value of total debt; ratio of price to earnings; ratio of market value to book

value; dollar value of assets; a variable for the size of the tracked group relative to the parent; and a

dummy variable for whether the tracking stock was focus improving. A tracking stock is defined as focusimproving if the tracking stock has a different two-digit Standard Industrial Classification code from the

general division, and nonfocus improving otherwise.

Significant at the 1% level.Significant at the 5% level.Significant at the 10% level.

the statistics over the post-issue period, and the last column reports the change in

liquidity resulting from the issue of tracking stock.

For the sample of 28 firms issuing tracking stock, the mean quoted spread

over the benchmark window is 16.84 cents, or 54 basis points; the mean effective

spread is 13.58 cents. Mean daily volume is about $44 million in an average of

483 trades. We report spreads and volume at issue and at 14 days and 30 days after

issue, but we focus on the spread and volume measures 30 days after issue. At these

times, the parent company has submitted all the regulatory filings and made the

necessary accounting disclosures, breaking out the results for the general division

and the tracked business group. If the net effect of these additional disclosures,

relative to the other aspects of the tracking stock structure discussed previously, is

to reduce information asymmetries, we should observe significantly lower spreads.


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The data indicate, however, that quoted spreads for the sample of firms that is-

sued tracking stock are not statistically different from those during the benchmark

period. Although there is some decline in the quoted spread, the decline is not sta-

tistically significant. In contrast, the decline in quoted and effective spreads for thecontrol group is about double in magnitude and statistically significant for both.

For example, the quoted spreads for the control group decline from 17.22 during

the benchmark period to 14.34 during the post-issue window, and this drop of 2.88

cents is statistically significant at the 10% level. The drop in spreads for the group

of firms that issued the tracking stock is much smaller (about 1.16 cents) and is

not significant. Similar quantitative results are obtained for effective spreads (3.03

cent drop for the control group vs. 1.70 cent drop for firms implementing tracking

stocks). Note also that the lack of significance for changes in the relative spread

is not surprising, given that before decimalization market makers tended to quote

absolute spreads that, for example, clustered around 1/8 and 1/16 with small varia-tions relative to the underlying share price. This tends to make the sample variance

of the relative spread much greater. Thus, statistical tests applied to measures of

the absolute spread are most appropriate for detecting changes in spreads over our

sample.

Such a decline in quoted and effective spreads for the control group is not

surprising, given the documented effects (cf. Jones 2002) of alternative trading

mechanisms and reductions in tick size that were implemented over our sample.

Note that a direct comparison of the magnitude of the decline in our control sample

with published sources such as Jones (2002) is not possible because our sample

is in event time rather than calendar time. However, the magnitude of the declinein our control sample is not inconsistent with published sources, suggesting that

our control sample is approximately representative of market trends. The sharp

decline in spreads from the control sample relative to the tracking stock sample

reinforces the conclusion that implementing tracking stock has not reduced, and

may tend to increase, information asymmetry. There is a larger decline in spreads

for tracking stocks that were focus improving, in contrast to the analysis of spin-

offs by Huson and Mackinnon (2003), although our statistical tests on this item

suffer from low power because of the few observations (only five focus-improving

restructurings).

The most striking result is that although the spread measures drift lower, in

absolute terms, at issue, the proportion of the spread due to adverse selection actu-

ally increases. Moreover, the indicated increase in the adverse-selection component

is large in magnitude and significant at the 1% level. During the benchmark window,

the adverse-selection component of the spread is 19% for the sample of firms that

subsequently issued the tracking stock. At issue, the adverse-selection component

of the spread is 30%. Thirty days after issue, the adverse-selection component is

still 27%, significantly different from the benchmark at the 5% level. For the con-

trol sample, the adverse-selection component is virtually unchanged at 22% from


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Tracking Stocks 211

-1.5

-1

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0

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toImp

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t 1 2 3 4 5 6 7 8 910

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Days Before Announcement/Days After Implementation

Cents

Spread: Sample Minus Control

Adverse Selection: Sample Minus Control

Figure I. Difference in Total Spread and Adverse-Selection Implementation Sample Minus Control

Sample. This graph plots the difference in total spread and adverse selection for a sample of

firms that issued a tracking stock and a sample of control f irms matched by share price, market

capitalization, and volume. The data are reported for the 15-day period before the announcement

that a tracking stock would be issued and the 15-day period after the tracking was issued. The

intervening period, which varies for each firm, is represented by the time-series average across

the sample and is labeled Ann to Imp.

the benchmark to the issue windows. With regard to the focus-increasing versus

non-focus-increasing sample, the unfavorable effect is most severe for non-focus-increasing restructurings, but it is difficult to make a conclusive determination on

this issue because of the small sample sizes.

Figure I reinforces these points. This figure plots the difference, for firm

issuing tracking stocks versus the control sample, in both the total spread and the

adverse-selection component of the spread (in cents). Before the announcement

window, the two lines fluctuate around zero, whereas just before the announcement

both the difference in spreads and the difference in adverse selection tend to in-

crease. After issue, the difference remains positive, fluctuating between .5 cents

and 2 cents.

We also examine spreads and the adverse-selection component of spreads

for the tracked divisions after issue and find similar qualitative results, although

they are less significant statistically. That is, tracked divisions tend to have less

liquidity and greater adverse selection than a sample of control firms matched by

the procedure described in section IV.

We conclude from this analysis that the information asymmetries intro-

duced by tracking stock restructurings are likely to outweigh any benefits obtained

from additional financial disclosures. Our results, based on more direct measures of

information asymmetry, reinforce the empirical results of Billett and Vijh (2004).


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Markets may have interpreted announcements to issue tracking stocks as value-

increasing events in the short run, but the actual issue of tracking stocks is not

likely to improve liquidity or reduce the information asymmetries affecting multi-

segment firms, and it may increase the information asymmetries.Finally, in Table 4 we report the results of the cross-sectional regression,

where the dependent variable is the change in spreads during issue window, and

several firm- and event-specific characteristics are used as independent variables.

The regression explains 21% of the variation in the dependent variable, although

none of the individual firm-specific characteristics is statistically significant. This

suggests that the observed variation in spreads is not driven by a subset of firms with

particular characteristics. Rather, the effects are systemic throughout the sample of

firms that have implemented the tracking stock structure.

VI. Conclusion

Several studies investigate the effect of tracking stocks on information

asymmetries. The usual premise is that because the SEC requires the disclosure of

additional financial statements detailing the performance of the general division as

well as the tracked business group, analysts can focus better on the performance

of each segment. This would increase both the number of analysts following the

firm and, because analysts tend to specialize in particular industries, the accuracy

of their forecasts. Both of these effects should reduce information asymmetries.

There are, however, theoretical and institutional factors that may counterthis effect, making the net effect of the tracking stock structure on information

asymmetries ambiguous. We explore these issues and reexamine the effect of track-

ing stocks on information uncertainties by using a relatively new data set and mi-

crostructure approach. Rather than examining the behavior of equity analysts, which

produces some conflicting results, we examine the behavior of market makers, who

provide liquidity to the market by posting bid and ask prices. If tracking stocks

reduce information asymmetries, market makers should respond by providing ad-

ditional liquidity to the market.

Our results, however, indicate only a marginal increase in liquidity for the

general division after a firm issues a tracking stock, relative to a large and significant

increase in liquidity for our control samplea trend that is at least consistent with

documented marketwide effects. In addition, the adverse-selection component of

the total spread significantly increases as a firm implements the tracking stock

structure while remaining essentially flat for our control sample. Similar qualitative

effects are observed for the tracked divisions relative to a sample of matched control

firms, although the difference is not statistically significant.

We conclude that the actual issue of tracking stock is not likely to signifi-

cantly reduce the information asymmetries affecting multisegment firms. Rather,


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Tracking Stocks 213

the uncertainties induced by the tracking stock structure substantially mitigate any

potential benefits associated with more detailed financial disclosure and may even

increase information asymmetries.

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do tracking stocks reduce informational asymmetries by elder et al. (jfr 2005)

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