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Innovation, Dierentiation, and the Choice of

an Underwriter: Evidence from Equity

Linked Securities

Enrique Schroth

HEC - University of Lausanney

and

FAME

First draft: February 10th, 2003.First Revision: April 17th, 2004.

I am grateful to the members of my thesis committee, Douglas Gale, Boyan

Jovanovic, and Marti Subrahmanyam. I also acknowledge helpful comments by

Franklin Allen, Chris Flinn, Helios Herrera, Eugenio Miravete, Franco Peracchi,

Michael Rockinger and Peter Tufano. Any errors in the paper are mine. I also

thank Ingo Walter, Iftekhar Hasan and the Berkley Center for Entrepreneurial

Studies who made possible my access to the data.yAddress: Route de Chavannes 33, 1007 Lausanne, Switzerland. E-mail: en-

rique.schroth@hec.unil.ch. Tel.: +41 21 692 3346. Fax +41 21 692 3435.

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The last twenty years have witnessed the introduction of a remarkable number of innovations in corporate

securities.1 Most of these have been brought about by investment banks through the business of underwriting

new corporate issues. It is also remarkable that investment banks have found it protable to develop n ew

securities even when their competitors have been able to imitate them almost immediately and at signicantly

smaller development costs. The empirical evidence so far has suggested that, d espite these disadvantages,

innovators are compensated with the largest share of the underwriting market. In this paper I estimate the

demand of rms for the underwriting services of investment banks that use innovative corporate products.

This will allow me to measure the dierent value to rms from raising money using a security engineered by

an innovator or an imitator and thus explain part of the innovators market share advantage. The dynamic

setup of the econometric model will allow a characterization of this advantage over time and shed light on

the question of what makes innovators have a demand advantage over imitators.

Product innovation in nance is particularly interesting because innovators face many disadvantages.

Tufano (1989) provides evidence showing that imitation occurs shortly after the rst issue of a new security,

leaving virtually no time for innovators to be the sole underwriters for that new product. He also observes

that the development cost is signicantly smaller for imitators than for innovators. Further, the design of

new securities is rapidly r evealed to competing banks because of SEC rules of disclosure. Most strikingly,

imitation cannot be precluded by any form of legal protection, e.g., patents.

In his seminal contribution, Tufano (1989) observes that what compensates innovators for these disad-

vantages is the largest sh are of the market for underwriting corporate new issues: given a security, the bank

that creates it is able to underwrite more capital than its largest imitator over all the securitys history. Why

innovators are able to have such an unchallenged lead in these markets is perhaps the rst question that this

evidence raises. This paper takes a rst step towards answering the question empirically. For that purpose I

model the choice of a rm that needs to raise money externally through the issue of a security. This rm has

to choose the type of security to be used and the bank that will underwrite it. Aggregating the choices of all

the rms that need to make an oering, the model predicts the market shares of underwriting by dierent

banks using dierent securities, conditional, among other things, on the characteristics of the banks (e.g., if

they are imitators or innovators). Thus, after estimating this model, we can test whether innovators have

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an advantage because their security is more valuable to the issuers.

This paper introduces two features that allow us to get a better description o f the facts of nancial

innovation. One of them is t he use of a framework of dierentiated products to model and estimate the

demand for underwriting services. An inspection of recent innovations in corporate products suggests clearly

that dierent securities are created to target dierent types of issuers or investors. For example, two similar

debt products that tie the repayment of the principal to the performance of other indices provide dierent

hedging devices to investors: the Stock Market Annual Reset Term Notes (SMARTs) are corporate bonds

that pay a capped oating rate that is tied to the American Stock Exchange Oil Stocks Index while the

Currency Protected Notes (CPNs) are bonds that pay a oating rate that is inversely tied to the Canadian

six-month bankers acceptance rate. By taking into account the location of securities in a product space, it is

possible to identify consistently a demand function for underwriting that depends on the price of underwriting

(the underwriting spread).2 This is possible because we can associate the variation in market shares with the

variation in underwriting spreads of varieties of the same security by dierent banks, and the variation in

underwriting spreads of similar varieties which can be close substitutes.3

The other feature is that this study focuses also on the dynamic pattern of market shares. Instead

of comparing the market shares of innovators and imitators over the whole history of a given innovation, I

observe them over time and estimate the innovators demand advantage accordingly. This will reveal whether

the innovators advantage is preserved steadily through the life cycle of the security or if imitators catch

up with (or continue to fall behind) innovators. This dynamic setup also allows a comparison between the

demands for sequences of securities. In fact, most securities appear sequentially, later ones as imp rovements

of earlier ones. It appears that the life cycle of a security ends when an innovation that modies the older

design is introduced.

The empirical nance literature has not yet addressed extensively the question of why innovators have

advantages over imitators. In fact, most authors have examined extensively the causes of the demand

for innovative securities. The focus h as been on trying to explain what made each particular innovation

attractive to investors or issuers but not on why it is privately protable to develop such instruments. Miller

(1986), for example, argues that the ma jor impulse to nancial innovations between the sixties and the

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eighties came from ever changing tax codes and regulations that brought about prot opportunities (e.g.,

tax money saved) through the design of new nancial products that circumvented these laws.4

Not much work has been done, though, to try to solve the puzzle of whyan unpatentable innovation is

worth its R&D expenditure if imitation is less costly and immediate. In particular, not much has been said

about what gives innovators an advantage over imitators. On one hand, some authors have tried to explain

Tufanos stylized facts at the theoretical level by arguing that innovators are infra-marginal competitors,

i.e., that have lower marginal costs than imitators. By moving rst, innovators may face lower search costs

of identifying potential issuers and investors (Allen and Gale, 1994, Chapter 4) or lower average marketing

costs if there is lumpiness in the set up costs of marketing networks (Ross, 1989) or if innovation signals

skill and creativity credibly (Tufano, 1989). On the other hand, Battacharyya and Nanda (2000) provide

a model in which the innovator is able to appropriate the value of its innovation and prot from it despite

being imitated if it is costly for its clients to switch to other underwriters. 5 ;6

By contrast with these views, I analyze the possibility that the asymmetry between innovators and

imitators is on t he information owned by these two types of banks about the product. If some of the

information that innovators have about the security remains private, a larger proportion of the R&D value

added can b e appropriated. In other words, the innovator can prot because it is imitated imperfectly.

This possibility was explored in a t heoretical paper by Herrera and Schroth ( 2000). In it, innovators of

derivatives that move one period in advance receive private informative signals from their clients or the

market. This allows them to oer deals that are more attractive to rms than what imitators can oer. For

recent innovations in corporate securities, it is possible that imitation is imperfect. Equity-Linked securities

and other derivative corporate products are sophisticated securities, specied by many parameters, some

of w hich vary from deal to deal. Thus, it is possible that imitators cannot reverse-engineer perfectly the

observed new securities from only a few deals. For example, the Equity-Linked Note (ELK) was the rst

debt product to tie the repayment of principal to the stock price of another publicly traded company. The

optimal choice of which stock to tie the notes to is observable but the knowledge of what stocks are optimal

for dierent issuers or investors is a private component of R&D. Imitators that want to underwrite issues of

ELKs for a potential client may know h ow to structure such instruments, but may not know exactly, from

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what is observable, what stock t o choose to tie the repayment of the d ebt of their client.

Using data on all the new issues of equity-linked and corporate derivative securities, the (qualitative)

results of the estimation of the demand for underwriting services can be summarized as follows:

the demand for underwriting services using this type of securities is sensitive to the underwriting spread

(i.e., its price);

on average, the issuers demand for the underwriting service by an innovator using its own variety of

the security is bigger than the demand for an underwriter that uses an imitative variety;

this dierence disappears during the securitys life cycle, so imitators catch up with innovators;

imitators catch up with innovators faster if securities are later improvements on past innovations.

Thus, this paper provides the rst empirical t est that the advantage to innovators may come from a

bigger demand for the innovators p roducts. While the empirical work directly shows how the innovators

advantage evolves over time, the particular dynamic patterns found point strongly to the fact that innovators

have superior knowledge on the products engineering and hence provide more value to the issuers. In fact,

the dynamic patterns of competition it identies are consistent with the predictions of Van Horne (1985)

when imitators enter the market.7

This paper also looks into the question of why and when are securities imitated, if so. I propose a simple

model where imitation has the benet of providing some prots in case attempts at innovation of future

generations fail. Imitation is also increasingly protable over generations as the demand for underwriting

services with imitative varieties converges faster to the demand for the innovator. Yet if outsiders can also

learn from observing the imitators complete deals, i.e., imitation spills-over information to non imitators,

then imitation can become less frequent along the sequence of new securities. The evidence s upports this

view: regressions of the probability that a given security is imitated on the generation number of the security

show that imitation is less frequent in late generations.

In the next section of the paper I describe the data set I use and present some preliminary results that

motivate the assumptions of the demand model I estimate. In Section 2 I develop the model that will allow

me to characterize the decision of rms about the dierent types of underwriters (innovators and imitators).

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First I p resent the formal setup for the discrete-choice decision p roblem of rms. I show that rms s hould

derive additional value if they chose an innovator rather than imitators as their underwriter. Then I explain

how this conjecture will be tested using multinomial logit and nested logit models of demand. In Section 3 I

present the results of the estimation and Section 4 tests the robustness of these results to basically dierent

denitions of what can be considered as innovative or as imitative securities. Section 5 derives a simple rule

that rival banks follow to decide to imitate a new security or not. This decision depends on which generation

is the security and how much imitation there has been in earlier generation securities. The predictions are

then test using probabilistic regressions. Section 6 summarizes the main claims of the paper.

1 Some Preliminary Evidence

The data used in this research is obtained from the Securities Data Companys on-line databases of nancial

transactions. I use all the private and public o erings of equity-linked a nd derivative corporate securities

in the New Issues database and record as many details of the oer as possible: the name of the issuer, the

principal issued, the name of the underwriter, the underwriters fees (underwriting spread), and details of

the security, like oered yield to maturity, average life, spread of coupon over treasury notes, call options,

etc. I merge this data set with the quarterly COMPUSTAT database (using six digit CUSIP numbers) to

have nancial information about the issuer.

In his study, Tufano (1989) uses all types of securities between 1974 and 1986. Here I restrict the sample

to equity-linked securities because this type ts better the motivation that rst-mover advantages come

from information asymmetries between underwriters. This type of securities are complex and underwriters

have to choose many parameters to engineer such deals (In Table 1 I show the relative size of this market).

Variations on mortgage backed products, for example, may be already too familiar to investment banks to

hide something in their structure to imitators.

The rst thing to realize about Equity-Linked and Derivative Securities is that they can be classied

into groups. The SDC database identies 50 dierent types of them but a closer look indicates that some of

these s ecurities are related to each other in terms of their structure and p urpose. For example, MIPS and

TOPRS are instruments used by issuers to deduct debt interest payments from their taxable income, but the

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former are issued by a limited liability company while the latter by a specially conformed business trust.8 To

classify all the 50 corporate products in the SDC database I have relied on the experts opinion about the

uses of these products for corporations and investors. 9 I found ten distinct categories, which I summarize

in Table 2. For t he rest of the paper I will refer to these categories as groups or families of securities

interchangeably.

I will refer to each one of these securities as an innovation, since for each one there is a unique feature

that distinguishes it from everything that already existed. However, depending on the group they are in and

the order in which they appeared, I will assign to each security a generation number. For example, since

MIPS were the rst tax advantage preferred note, I will call them the rst generation of this family, and

TOPRS the fourth.

I follow Tufanos (1989) criterion to dene the innovator of a security as the underwriter of its rst oer.

Similar to what Tufano found, for equity-linked corporate securities we do observe that innovators have an

edge over imitators in terms of market shares. 18 of the 50 products are imitated. In 13 of these, innovators

lead in principal underwritten, and in 15 they lead in number of new issues. Also, imitation was fast: for 10

of these securities, the second underwriting deal was made by an imitator (see Table 3).

In this paper I want to study why innovators have a competitive advantage over imitators. In particular,

I want to test if issuers have stronger preferences for innovators than imitators as the underwriter of the

oer. To choose an appropriate framework to study the demand for the underwriter of the issue it is worth

examining the dynamics of the market for a n ew issue within each security and within each family. In Figures

1 and 2 I show the total quarterly principal underwritten by investment banks using the most important

securities of two families. As we can see, each security seems to be popular for a certain period of time until

a next generation appears and leads the market for issues of that group.

Another interesting feature for some families is that the market share advantage of innovators over

imitators seems to be bigger in the early generations. In gures 3 t hrough 5 I show the evolution in time

of the accumulated underwritten principal using a given security. Each gure represents a generation of the

same family (convertible preferred stocks). For later generations, imitators end up accumulating a larger

principal relative to the innovator. In some cases the innovator is overtaken. A similar feature is observed

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in the family of index-tied principal appreciation securities (gures 6 and 7). It is less clear, though, if

this is true for other families, like the tax-advantage debt or equity products. On average, still it seems

the innovatorss advantage is w eaker on late generations: Table 4 shows the average ratios of principal

underwritten by imitators relative t o principal underwritten by innovators. The ratio for rst generation

securities is half the ratio that includes all imitated securities.

The evidence above suggests that innovators seem to have an advantage over imitators that is stronger

for earlier generations of securities within the family. Further inspection s uggests that the appropriate

framework to analyze the securities in this sample may be one of dierentiated products: it is clear from the

denition of the 50 securities that these corporate products oer dierent benets to issuers or investors.

Some provide a tax-advantage, others provide a h edge against the risk of d efaulting on debt. Interestingly,

within imitated s ecurities there seems to b e dierentiation across underwriters. For example, the within-

variety variation for some of these characteristics is smaller for imitators than for the innovator (Table 5).

The price of underwriting, i.e., the underwriting spread, does not seem to dier signicantly, although the

within security sample mean of the underwriting fee for innovators is larger than for imitators (Table 6).

I will interpret this evidence as I present the econometric model and as I discuss the results of the

econometric estimation. What I conclude from this evidence is that a useful model to describe competition

between investment banks to underwrite corporate issues using this type of securities must be one of product

dierentiation in an oligopolistic industry. Dierentiation occurs at the underwriters level, where innovators

are distinguished from their imitators. Thus, from now on I will call a variety a distinct combination of a

security and an underwriter.

2 The Demand Model

2.1 The Theoretical Approach

The model I present below is built to illustrate the decision making process of rms that want to raise capital

and have to choose an appropriate security and the best underwriter, that is, the investment bank or book-

manager that will engineer the security and sell it. I will use a stylized model of the issuers preferences and

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the underwriters information to derive the demand for underwriting services by the dierent banks using

their variety of the s ecurity. The objective is to motivate a reduced form that can then be taken to the

data and will allow us to test if those underwriters that invent the security have an advantage over the other

underwriters (the imitators) that engineer the same security for their potential clients. Since the underwriter

has to make engineering choices for the issue, an underwriter who is better informed on the choices he can

make will provide the issuer with a larger value. I will show that the identity of the underwriter summarizes

the quality of the engineering choices and thus the estimation of a demand function that depend on the

banks characteristics will identify the bankers superior knowledge to underwrite that security.

It is worth to p oint out that the ultimate source of the innovators advantage, the information asymmetry,

is taken as given in this paper. The model that follows illustrates how the asymmetry is built into the demand

function and what would be the empirical implications. 10

2.1.1 The Setup

Firms that want to raise capital, the issuers, demand underwriting services from investment banks. These

banks compete to underwrite the issue of a corporate security, and for this they oer dierentiated products:

debt or equity types of deals that oer investors dierent payo schedules, horizons, call options, convert-

ibility p ossibilities, etc. Thus, they comp ete for issuers that could use a non-standard variety of nancial

instruments.

A rm that needs to issue a security to raise capital is indexed by i 2 I: At a given period, there is a

set of varieties of instruments, J =f1; 2;:::;Jg from which it can choose one. Let g be an index for groups

of varieties, g = 1;:::;G; such that G Jand let there be a partition G= fJ1; J2; :::; JGg ofJ so that each

set Jg contains those varieties which are closer to each other in terms of their characteristics. In this setup,

for example, if consumers were choosing models and brands of a car then a set Jg would contain all brands

of, say, Sport Utility Vehicles and some other set, Jh; would contain all brands of compact models.

In our case, the groups in G are securities that have the same name, e.g., PERCS, LYONS or TOPRS

and each variety would be determined by the name of the bank that underwrote the issue, e.g., PERCS by

Morgan Stanley or PERCS by Merrill Lynch. 11 Let b represent an underwriter in the s et of banks, B.

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The rm chooses one security and one underwriter among those that oer that security. A variety j 2 J

is given by a unique (b; g) combination. Let uij be the value to rm i of choosing the variety, j:The setup

for this decision is illustrated in Figure 8.

The value function uij is central to this paper since it is the function whose parameters I will estimate

using the data. I will characterize this function when I derive the preferences of issuers for underwriters that

are innovators and underwriters that are imitators. The empirical literature that deals with the estimation

of preference parameters in models of discrete choice uses special cases of the general specications of linear

preferences by Caplin and Nalebu (1991) or Anderson et. al. (1989). In these studies, agents value a

product according to a weighted sum of its components. These components, in general, are functions of the

observable characteristics of the product. We shall see, below, how this structure is particularly appropriate

for this study.

2.1.2 Preferences of Firms

In a typical underwriting deal of equity-linked or derivative corporate securities the rm will issue a security

engineered by the underwriter. The rm has preferences over the set of possible structures for that security.

An underwriting deal is dened by a combination of a vector of characteristics and an underwriting fee.

If 2 is this vector, and p the fee, then an underwriting deal between a bank b and a rm i is fully

characterized byfj; i; ; pg:

The vector of characteristics could include, for example, the premium over dividend p aid by common

stock, the date of maturity of the security, the number of periods this security is protected from a call option

by the issuer, or, more broadly, discrete variables that determine whether the security is convertible or not,

if it is convertible t o common stock or debt, etc.

Let us start with a general random value function for the ith rm in a given period, t. If a rm chooses

some varietyj its value depends on how the security has been engineered, i.e., on j; and its net income

after the fee is paid. Let this value function be denoted by

uijt = u(yit pjt; jt)+"ijt : (1)

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As shown by McFadden (1981), this function is continuous in y p and and twice continuously dieren-

tiable in the same arguments provided that the function is continuous and twice continuously dierentiable

in other goods and in : The term "ijt is assumed to be an additive random component that captures the

random preferences of a given rm for a particular variety. It is unobservable to underwriters, and distributed

independently with a continuously dierentiable cumulative distribution function, G("):

2.1.3 Underwriters

At each period underwriters engineer and price their own varieties of securities such that they maximize

their prots. I assume that underwriters are Bertrand-Nash competitors in prices and . Their prots are

given by

jt = pjtqjt c(qjt); (2)

where the demand for the particular variety j is

qjt = q(pt; t) =Mt ZAj(u)

dG("); (3)

and c(:) is the total cost, such that c 0(:) 0 : The demand for a particular variety is a function of the prices

of all varieties the vector ; and the observable economy-wide shocks. The set Ajt (u) is the set of all the

possible realizations of the individual shocks, ("i1t;:::;"iJ t); such that uijt > uikt; k 6= j; i.e., the set of all

the states that lead an issuer to a choice of variety j: M is a measure of the total number of rms, so that

qj is obtained by multiplying

Mby the share of rms that choose

jof the total number of rms that want

to raise capital.

The next lemma will simplify our work s ignicantly. It shows that an underwriters choice of jt that max-

imizes each rms individual utility conditional on its available information also maximizes the underwriters

prots. I assume that u (:) is twice continuously dierentiable, strictly increasing and strictly concave.

Lemma 1 For a given price pjt; ifjtmaximizes uijt (yit pjt; jt ;"ijt; :) then

jtmaximizesjt :

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I prove this lemma in the appendix, but the intuition is very simple. Since the unobservable component

of utility is independent of the characteristics of the security then the aggregate demand for a variety j is

strictly increasing in t he utility of any issuer. I have derived t his result now b ecause it will allow me to

eliminate jt from the issuers value function, by substituting the optimalchoice of jt: The objective in the

next section is to explain how that choice diers across underwriters with dierent information sets and, as

a consequence, how the value of rms diers depending on the chosen underwriter. 12

2.1.4 Asymmetric Information

Suppose that the innovator has superior information about the engineering choices h e can make for the

potential issuers, given a particular security design. To formalize this idea, let0 be the prior (common)

knowledge set of all the possible characteristics that a security can have, and uijt the utility function in (1),

which is also common knowledge to all underwriters a t some p oint in time. To relate this abstraction to

the case of equity-linked or derivative securities, imagine 0 as a set of all possible engineering choices for

a security before convertible debt was invented. Before the rst innovation, debt securities would be zero

coupon or would have paid a xed or a oating rate, so any vector in 0 would have zero entries for other

characteristics yet to be used, e.g., for convertibility to common stock.

If an underwriter spends resources on R&D to come up with a new security, it will discover new possible

combinations of characteristics a rm may nd valuable, possibly changing zero entries to add new dimensions

to the structure of a security. The PERC, for example, was the rst issue of preferred stock convertible to

common stock with capped and oored appreciation.13

Denition 1 An innovation is a new security designg; tied to the discovery of an engineering set g

such thatg 0 6=fg:

When the rst underwriting deal is made using a new security the innovator reveals the new security

design and a particular vector :The whole set of possible engineering choices for this particular security, g

is kept private, though. The goal now is to show that the optimal choices of for innovators and imitators

will dier, in general, and this will be reected in the value function of a rm signing an underwriting deal

with either. Lemma 1 allows me to eliminate the vector of the said function and express the indirect utility

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as a function of the banks identity. To see this, note that what distinguishes an innovator from an imitator

is the set from which it can choose any : For the innovator, this set is 0[ g ; and for the imitator this

set is 0[ k : Thus, we can summarize the identity of an investment banks by b = 1 if the banker is the

innovator of the given security, and b= 0 if not.

Lemma 2 Letv(yi p; b) max2b

u(yi p;): For any couple of varietiesj; l of the same securityg; if j is

issued by an innovator thenvj(yi p; 1) vl(yi p; 0).

The r esult is trivial. 1g is the set of choices of the innovator and 0g is the set of choices of the

representative imitator. Since 1g 0g ; the result follows.

The Lemma above has established that given prices, innovators have an a dvantage over imitators. This

advantage can be measured by the additional value to issuers if they were to choose an innovators variety.

Let this dierence be named

vj v (yi p; 1)v(yi p; 0): (4)

Note that an underwriting deal can be dened in a simpler way: now it can be summarized by fj; i; b; pg:

This result is convenient for the estimation because what I want to capture is exactly the dierence in pref-

erences for the dierent underwriters. Also, working with a value function, in which has been eliminated,

avoids losing a large proportion of observations for which the full is not available. In other words, my

interest is to distinguish preferences for these two types of banks more than to estimate the marginal valua-

tion (and the derived elasticities) for a given characteristic of a security, e.g., years of call protection, yield

advantage, etc.

Another reason is that preferences for the choices of each attribute in may be complicated functions

that make the estimation dicult. Thus, using a simpler function that summarizes all the attributes seems

reasonable. This approach has b een used by C aplin and Nalebu (1991), who use a utility function that

is linear in functions that map the dimensions of the product characteristics onto a dierent space. Using

their own example, the benets of a car could include only comfort and speed, but these could be more

complicated functions of the physical attributes of the car.

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2.1.5 The Demand Function

Here I discuss what Lemma 2 implies for the equilibrium in the market for underwriting. In this type of setup

with dierentiated products, there is a demand function for each variety. The next proposition establishes

that, ceteris paribus, the demand for an innovators variety of a given security is bigger than the demand for

an imitators variety of the same product.

Proposition 1 Lemma 2 implies that for two varietiesj; k of the same securityg and for a xed vector of

underwriting spreads, p2 RJ+ such thatpj = pk then

qj(p; :) qk(p; :)

if j is the innovators variety.

I prove this proposition in the appendix. Note that ifvj >0 then the proposition above holds with a

strict inequality.

I will not show formally that in equilibrium innovators have bigger within-securities market shares than

imitators. In fact, it is not obvious that this w ill b e the equilibrium outcome. It is true though that, under

mild regularity conditions on G (:)and v (:), the game becomes one of strategic complementarity. 14 Moreover,

if the best response function of the innovator shifts right if his advantage is positive, i.e., ifv > 0; and

if marginal costs are the same among underwriters then in equilibrium the innovator will charge a larger

underwriting spread for his variety of the same security higher and have a larger market share within that

security. If this advantage eventually decreases, then the innovators equilibrium price should decrease and

converge to a symmetric equilibrium as the advantage goes to zero.1 5

2.1.6 The Demand Advantage through time

Proposition 1 has established that, if part o f the set of engineering choices the innovator can make to engi neer

a security is kept private, then the demand for his variety will be larger than the demand for any imitators

variety, t hat cannot be engineered as well as the innovators. Clearly, though, as the innovator completes

more deals, more possibilities for engineering are disclosed to potential imitators. With time, the set from

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which the imitator picks becomes larger, and eventually gets arbitrarily close to the innovators set. In

fact, in this setup the imitator only learns the innovators set element by element through observations, and

it may be the case that the imitator can reverse engineer this set much faster, from the observation of the

innovators choices and some p rior kn owledge of the issuers preferences. In any case, it is clear that w ith

time the imitators choice set gets larger.

Lemma 3 Ifvj is decreasing in time, then the demand for the imitators variety converges monotonically

to the demand for the innovators variety.

As a corollary, if next generation innovations are marginally decreasing improvements of the innovators

then convergence should b e faster for later generations. Namely, if the initial innovators a dvantagevj gets

smaller as new products are introduced, we would expect the advantage to be shorter lived, which would

show up empirically by a demand function for the imitative varieties that, ceteris paribus, converges more

rapidly to the innovators.

2.2 The Econometric Model

I argued above that the market of underwriting new issues using equity-linked securities and other corporate

derivatives may be well approached as one of dierentiated products. Each variety oered is given by a

combination of a security structure or name and the identity of an underwriter. In this section I present

the model that I take to the data. This mod el will be a reduced form equation obtained from aggregating

the individual rms demands for the d ierent varieties. I establish dierent sets of assumptions for the

aggregation of individual rm demands, and the results will b e dierent reduced forms to estimate: the

multinomial logit and the nested logit demand models, each one requiring a dierent method of estimation.

2.2.1 The General Setup

I consider each time period t = 1;:::;T a dierent market in which an issuer i chooses its desired variety

j: As the standard of the empirical literature of discrete models of demand, I will specify the value of this

issuer as a function of observed and unobserved characteristics of the issuer and of the product oered by

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the underwriter, and of the relevant parameters. Let

uijt =(yit pjt) + xjt+jt + "ijt: (5)

The rst term on the right are the net proceeds of the issue, which are assumed to be separable and

linear.16

The second term captures the indicators tha t distinguish an innovator from an imitator. The vector xjt

is then an index ofvj that will include all the variables that reect dierences in the information owned

by underwriters. As we shall see below, I will not only employ dummy variables that capture the preference

rms have, on average for investment banks with superior information. I will also try to identify the dynamics

of this advantage by interacting the identity of banks with the number of time periods after the security has

been imitated, and the order in which the security appears within its family. I will also account for the

fact that banks may acquire reputation as experienced underwriters of this type of securities based on their

history as innovators within a family or any other type of equity-linked or derivative corporate security.

The value of choosing alternative j to raise capital can be decomposed into its mean, jt = pjt +

xjt+ jt and a deviation from it, yit + "ijt : The unobservable (to the econometrician) characteristics of

the securityj itself are captured by j; while the deviation term is used to account for the heterogeneity of

rms preferences. "ijt would be a purely idiosyncratic, mean zero, shock. Below I w ill explain briey the

dierent ways I will estimate the parameters of the value function.

2.2.2 Logit Demand

Berry (1994) shows that if we under some distributional assumptions about "ijt we can identify the pa-

rameters of (5) using the observed market shares of all varieties, j: In particular, assuming that "ijt has a

density function f(") = exp( exp(")) the market shares predicted by the model,bsjt ; which are obtainedby integrating all the realizations of unobservables that lead to a choice ofj over all other varieties, are given

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by the well-known closed form solution (the logit formula):

bsjt(t) =

ejt

1 +

PJl=1e

lt: (6)

Note that the average utility of the outside alternative is normalized to zero ( 0t = 0) and that the term

yit drops out because it is common to all the choices. The logit formula has the property that the market

shares are uniquely pinned down by the average utility of a choice j: Thus, and can be obtained from

a regression of the dierence of the logarithms of the observed market share ofj and an outside alternative,

on x jt and pjt ; e.g.:

lnsjt ln s0t= pjt + xjt+ jt : (7)

The estimation of this model is simple. The challenge is t o nd appropriate instruments for the price

because it is very likely that it is correlated with the unobservables, jt: This is a typical problem found

in s tudies that u se product characteristics as regressors. In this case, xjt uses issuers characteristics that

summarize the full description of the product, so it is less likely that jt contains product unobservables

correlated with the price.

There may still be other costs of imposing this particularly convenient s tructure. As a consequence of

assuming that the "ijt are independent and homoskedastic, the odds ratios of choosing one variety over

another do not depend on the value ofother varieties. This can be problematic: suppose the d emand for

MIPS were evenly split between Goldman, Sachs and Merrill Lynch, and each were half the demand for

Salomon Brothers ELKS. This model would predict that the ratio of the market share o f G oldmans MIPS

to Salomons ELKS would still be one half, even if Merrill increases its underwriting fee by any magnitude.

Implicitly, the business lost by Merrill Lynch would be absorbed by both Goldman and Salomon so as to

preserve the r atio, n ever mind that Morgan Stanley and Merrill L ynch oer close varieties and Salomon

oers a dierent product.

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2.2.3 Nested Logit

The nested logit allows a richer p attern of substitution than the simple logit and at a small additional

computational cost. The decision to choose a variety is now represented by a tree as in Figure 8. The

preferences of rms are allowed to be correlated within groups. In this case, dierent varieties of the same

securities oered by dierent investment banks would be closer substitutes of each other than any other

security.

In this case, the utility of a given choice, j; can be modelled as a restricted version of (5), allowing for

random coecients, ig ; on security specic dummies. Thus, we have

uijt = jt + Xg

djgig + (1 )"ijt; (8)

where djg = 1 i f j 2 Jg ; and "ijt is still assumed to be independently and identically drawn from a Weibull

distribution.

If the random coecients are also assumed to be drawn from a Weibull distribution, then so is the term

+( 1 )":The degree of within group correlation is given by : if it approaches one then so does the within

security correlation of utility levels, and if it approaches zero then there is n o within security correlation

and we are back to the logit model. Due to this assumption there is an analytical solution for the predicted

market shares of underwriters within the security:

bsj=gt(t; ) = ejt=1Dgt

; (9)

Dgt =Xh2Jg

eht=1: (10)

The overall market share is

bsjt(t ; ) = ejt=1Dgt[

Pf2GD

1f t ]

: (11)

Normalizing 0 = 0; which implies D0 = 1 then ; and can be recovered from an Instrumental

Variables regression of the dierence of the logs of the observed market share ofj and the outside alternative

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on xjt ; pjt ands j=gt because

lnsjt ln s0t= lnsj=gt pjt + xjt + jt : (12)

Again, instruments for prices and additional instruments for within security market shares must be used to

obtain consistent estimates because both variables are endogenous.1 7 ,1 8

2.2.4 Issuer Heterogeneity

To enrich the substitution patterns in the demand model I incorporate data from COMPUSTAT about all

the rms that did new issues using equity-linked security in the sample. This will introduce h eterogeneity

that will make the cross-price elasticity depend on the issuers characteristics. In this case, I consider a

vector of f rm characteristics ft; each one to be interacted with the price to obtain the following estimable

relationship:

ln sjt lns0t =pjt + xjt+pjt ft +jt: (13)

As we shall see in the results, the cost is that we will lose a signicant proportion of the observations in

the sample. Many of the issuers of equity-linked securities had no record in COMPUSTAT.

2.3 The Data

As I mentioned before, the SDC Database of New Issues records all the public and some private oerings

made since 1962. For s ecurities dened in SDC as equity-linked or derivative corporate securities there are

662 oerings up to March 2001 (the rst issue, a LYON, was made in April of 1985). There are 50 securities

and a total of 98 varieties. I compute the varieties market shares over the whole market of new issues and

over the varieties within the security at dierent time periods.

I divide the whole sample in time periods rather than aggregate the data by varieties over the whole

time span studied. Overall aggregation would reduce signicantly the number of observations (to 98) and

would also eliminate the time variation of market shares and underwriting fees, compromising seriously the

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consistency of the estimators. Thus, I treat each time period as an independent market, so t hat there is

a demand function for each variety at each time. The parameters of this function are identied by cross-

sectional variation in prices, in the identity of the underwriter, and the issuers characteristics and by the

time variation in prices and issuer experience. The panel structure o f the data is crucial since I want to

study the dynamics of the advantage to innovators.

To form the panel I must choose the length of each time period, though. The shorter the length of each

period increases the size of the usable data set but increases the risk of aggregating very few or unique deals

per period, which would increase dramatically the variation in the market s hares. To avoid arbitrariness

in the choice of the length I do the estimations at four dierent levels of aggregation: using 16 periods

(annually), 8 periods (biannually), 11 periods (18 months) and 12 periods (16 m onths). In this way we can

also have an assessment of the robustness of the results to this choice.

The panel is unbalanced b ecause not all securities a re oered at each p eriod. Only two varieties are

oered in the rst period and 98 in the last. I consider standard equity as the outside o ption to issuers, i.e.,

standard equity is the variety j = 0: I approximate the total size of the market for new issues using

M =q0+ q1+ :::+qJ: (14)

The unit of demand is number of deals, not dollars underwritten. This assumes that rms set ex-ante

the amount of cash they want to raise in the oer, and the choice I model here is the choice of the security

and the underwriter.

2.3.1 Variables

Market Shares Overall market shares, sj; are the observed aggregate number of deals for that variety in

a given period divided by the total number of new issues. Within-security market shares divide the number

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of deals by the total number of issues using the relevant security: 19

sjt = qjt

Mt;

sj=gt = qjtPl2Jg

qlt:

Prices Prices o f underwriting are the fees charged by the investment bank that leads the syndicate of

book managers of an oer. They are expressed as a percentage of the p rincipal underwritten and called

underwriting spreads. Usually this spread can be disaggregated in the underwriting fees and management

fees. This disaggregation is seldom observable though, so the price variable I will use is the total spread. 20

Demand Shifters in xjt The demand shifters that do not capture the information asymmetry between

underwriters are variables about the underwriters experience and reputation issuing this type of securities. I

use the total number of innovations in equity-linked products and innovations within the particular family of

the security accumulated by the u nderwriter. I use time period dummies to control for observable economy-

wide shocks and group dummy variables.

Advantage to Innovators One way to test if innovators have advantages on the revenue side is by

including a dummy variable that equals one when the u nderwriter was the rst to issue that s ecurity.

A positive estimate of the coecient of this variable would imply that, on average, rms h ave stronger

preferences for innovators.

In the model presented above, the innovator has an advantage because it holds private information about

the security issued. However, this advantage could diminish as more deals are completed by imitators. Thus,

we would expect the estimate of the coecient of the innovator dummy interacted with the number of deals

after the security was imitated to be negative. Moreover, if the security is a late generation of a given group,

more information about this type of securities would have been aggregated, and we would expect imitators

to learn the innovators private information faster. Thus, I also interact this dummy with the generation

number to get a richer characterization of the dynamics of learning by doing.

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Formally, I model these dynamics by specifying the component vj of the rms utility as:

vjt =0ij +1ij genj +2ij genj

et; (15)

where the dummy variable i j = 1 if the variety j is the innovators variety, g en is the generation number of

the security andetthe number of time periods since the rst imitation.

Issuers Data I use nancial data from COMPUSTATs quarterly database that m atches the period of the

oer. I use the total market capitalization to measure the size of the rm. I also use indicators of common

equity, preferred equity, short term, long term debt and subordinated debt all expressed as percentages of

capitalization.

2.3.2 Instruments

Since it is very likely that the price is endogenous, instruments are needed to obtain consistent estimates of

the parameters of the mod el. In the case of the nested logit specication, the within-securities market shares

are used a s a r egressor and these a re possibly endogenous too. To choose appropriate instruments I follow

the s uggestions of Berry, Levinsohn and Pakes (1995) and Berry (1994). Instruments for the underwriting

spread (price) include the averages of characteristics of the security over the competingvarieties, like years

of call protection, years prior to call at par, percentage yield, which should not be correlated with the error

term since the advantage term summarizes all characteristics of the security. By the same token instruments

for the within-security market shares include characteristics of otherunderwriters in the same group (e.g.,

total and within family accumulated innovations by the other underwriters of the same security). To test if

these instruments over-identify the parameters of the models I perform a Hausman test of over-identifying

restrictions.

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3 Results

3.1 Logit Demand

To serve as a benchmark, rst, I t the simplest, yet most restrictive, demand model: the multinomial logit.

I only report here the results for the aggregation at 12 time periods for the sake of parsimony (the subsequent

estimations w ill include all aggregation levels to show the robustness of the results). Table 7 reports the

estimates of the parameters of (7), allowing for unobservable (to the econometrician) attributes in the dierent

varieties and using an instrumental variables method to account for the correlation between the price and

the unobservables (the standard errors were estimated using the Huber/White variance estimators, allowing

for heteroskedasticity a nd serial correlation within securities). I t two models: one that only includes the

innovator indicator from equation (15) (reported in the Base Case column) and another one that species

the full dynamics in (15). I use time perio d-specic d ummies and xed eects for the security group.

We can s ee, for bo th columns, that most estimates have the expected s ign. The underwriters fee (the

price) is signicant at the 90% level. In both cases the estimated coecient is n egative, which indicates

that the market demand for a bankers variety is downward sloping in the fee. Note that I report the

estimated coecient of the underwriting spread, so the estimated is p ositive. If, as assumed, the data

of the underwriting markets were generated by a model of oligopoly then the elasticity of demand at the

fees observed should be bigger than 1. In other words, bankers should be pricing their deals in the inelastic

portion of the demand curve. In Table 7 I report the number of estimated demand curves (of 323) that

violate this condition (i.e., that are elastic at the observed fee). Both logit models imply 50 and 49 inelastic

demands, respectively, out of 323 estimates. The average elasticity, though, is well above 1.

The average value to an issuer increases if the number of innovations in equity-linked securities accu-

mulated by its chosen underwriter increases. The negative sign of the number of accumulated innovations

within that securitys group, though, is an unexpected anomaly.

In both col umns, the innovator dummy has a positive coecient, signicant to the 99% level of condence,

suggesting stronger preferences for the innovators variety, ceteris paribus. The second column reveals an

interesting result. The co ecients on the innovator dummy, on t he dummy interacted with the generation

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number, and on the dummy interacted with the generation number and the time after imitation are all

signicant to the 99% level of condence. The estimate of the coecient of the rst interaction term, i gen;

is positive, revealing that the later the generation, the higher the average initial advantage of the innovator.

The second interaction term, i gen et; has a negative estimated coecient, showing that this advantagedecreases in the number of periods that imitators have been in the market, and that this advantage diminishes

faster the later the generation.

Since I estimate this model using instrumental variables, I test if the restrictions imposed by using

the chosen instruments o ver-identify the parameters of the model. I perform a 2 that tests jointly if the

model is correctly specied and the instruments over-identify the variations in the endogenous variables.

The test statistic and its p-value are also reported in Table 7. In any case, the null hypothesis of correct

specication and over-identication is rejected. Rejection is inconclusive about the source of the problem

(misspecication or lack of identication), but, as we mentioned before, the logit model is a simple version

that imposes restrictive substitution patterns across dierent varieties of underwriting services. In fact, the

previous results were obtained under t he assumption that varieties of the same security were as close to

each other in the product space as varieties of dierent securities. Group dummies may have accounted for

proximity within the family, but not within the security. The r esults that follow are for the nested logit

model, that deals with t his problem.

3.2 Nested Logit Demand

The estimation procedure for the nested logit demand model is similar to the one used for the multinomial

logit. The dierence is that, here, I include as a regressor the within-security market shares for each

variety in order to obtain an estimate of the intra-security substitution eect. For this matter, additional

instruments must be used since the new regressor is believed to be correlated with the varietys unobservable

characteristics. This model was tted for the four dierent aggregations of data: 8,11,12, and 16 periods.

The results are shown in Table 8.

The estimated coecient of price still h as the correct sign for all the a ggregations. It is signicant at

least at the 95% level but for the case where t= 8 (where its p-value is 0.121). The estimated elasticities

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increases sharply after accounting for the substitution eect of the underwriting services of banks oering

similar varieties. As a consequence the implied number of inelastic demands is much smaller (10 at most).

The estimated coecient for the substitution parameter is signicant in all cases, and the estimate is

within the appropriate bounds, 0 and 1 (0:618

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instruments themselves are correlated w ith other excluded exogenous variables. However, it is interesting

that the model is over-identied when time periods cover 2 years (when t= 8). It is p ossible that within

shorter intervals, the instruments used are strongly correlated between themselves, while this may not be the

case for longer periods. It is also worth pointing out that, the rejection of over-identication at some levels

of aggregation is not strong evidence against our choice of instruments, since almost all of the estimates are

consistent across all the aggregations.

3.3 Issuers Heterogeneity

The estimation by instrumental variables of the logit and nested logit demand models above may have

allowed us to obtain consistent estimates of the own-price elasticity, but it may still yield implausible cross-

price elasticities for varieties in dierent groups. Also, the test of over-identifying restrictions for the nested

logit specication revealed that the model may s till have not been completely specied. In Table 9 I show

the results after adding the characteristics of the issuer to the model via interactions with the price variable.

Although it is not my goal to estimate these cross-price elasticities, adding heterogeneity will dierentiate

own-price elasticities by the type of rms.

Despite the loss of observations when using issuers data, Table 9 shows results that do not dier qual-

itatively to the previous ones. The s ame dynamic b ehavior of the innovators advantage is observed in all

four cases. For all aggregations over time, the initial advantage is bigger than in the previous specication

but it also d ecreases at a much faster rate. On average, for all cases of the model with interactions, the

advantage of each generation would be gone almost by the time predicted in the nested logit model without

interactions (see Figure 13 for the case when t= 8):

Of the ve issuers variables that I interact with price, only market capitalization and preferred stock

as a p ercentage of market cap were found to be signicant at a level higher than 90%. Their estimated

coecients were both positive. One possible explanation is that market capitalization is an approximation

for the available sources of nance to the issuer. Similarly, since most of the varieties are forms of preferred

stock or convertible to preferred stock, rms with a larger proportion of this type of stock have more available

instruments to raise capital and thus their demands are more elastic to underwriting spreads.

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Adding rm specic data has improved the t of the model in the sense that the estimated elasticities

of d emand are higher and the number of predicted inelastic demands has decreased. Moreover, with the

additional rm specic regressors the null hypothesis of over-identication and correct specication cannot

be rejected in any case, even with 90% condence. And as before, the estimates are consistent over all the

estimations.

4 Robustness Analysis and Further tests

In this section I analyze the data set further to discuss the appropriateness of some of the assumptions made

earlier and test empirically if the results presented above are robust to changes in some of these assumptions.

4.1 Trademarks on Securities Names

It is possible that some securities that have been considered here as innovations may only be replications of

existing designs but marketed with dierent names. If innovators register a trademark for the original name

of the security, its imitators would be confused as innovators of later generations. Thus, it could be incorrect

to consider such varieties as innovative and their rst underwriters as innovators rather than imitators. An

inspection of all the 50 securities in the data set identies dierences in the design of almost all securities

with respect to their previous generations.2 1 Moreover, the setup of the estimation allows for intergenerational

substitution eects. However, these eects are by construction smaller than the intragenerational substitution

eect. Also, the fact remains that probably the aggregation used so far denes too many varieties as

innovative. It is worth noting, though, that labeling more b anks as innovators rather than imitators in

later generations is likely to bias the innovators advantage estimate downwardsbecause late generations

have typically small market shares and are not as imitated as early generations. The current denition

of an innovator associates observed small market shares to innovators, some of which could be imitators.

Thus, since the possibly downward-biased estimate of the innovators demand advantage over its imitators is

already positive and signicant, our inference from the current results should not change much qualitatively.

To assess the r obustness of the results I propose two alternative exercises. First, I estimate the same

parameters having excluded from the data set all those securities that were not imitated and whose name

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was trademarked. I searched for all the 50 security names in the United States Patent and Trademark Oce

records to s ee if there is a trade mark for any. This criterion eliminated 7 s ecurities, and 77 issues from

the sample. The remaining securities were either imitated or could have been imitated. Table 10 shows the

new estimates, which are very similar than the ones obtained before. The estimated coecient of price still

has the correct sign f or all the aggregations. The estimates of the price elasticity of underwriting demand

are similar, and so is the estimated coecient for the substitution parameter. The estimate is s till always

between 0 and 1 (0:654

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always signicantly dierent from zero and between the proper bo unds). The goodness of t measures have

increased also. These results imply that banks with good prospects to sell t he oering of equity-linked

securities successfully can actually retain their advantages for longer periods.

4.3 Product Groups as Observational Units

The second approach that I use to overcome the problem of identifying correctly the level of competition

in this data is to recompute u nderwriter market shares, this time over product groups rather than over

individual security names. I redene a variety as a banker-product group pair, and compute market shares

accordingly in all time periods. This aggregation is the other extreme of the previous o ne: here, only the

products that start a category are considered as innovations, while before, any security name was considered

as such. Thus, this aggregation rules out the fact that some generations are actually innovations in their own

right. Note that, with this aggregation, the generational eects cannot be a ssessed because all subsequent

generations within a group are considered imitative varieties. A variety now is dened by a b ank-group pair.

The number of total varieties falls from 98 to 60, and so does the number of usable observations for the

estimation.

The results change little with this aggregation (see Table 12). In fact, this aggregation seems to t the

data better: the joint hypothesis of correct specication and over-identication through the instruments

are never rejected with 99% condence, while the R-squared coecients are higher. The same dynamic

pattern concerning the innovators advantage is o bserved: with time it disappears. In this case, the size of

the advantage is initially smaller, but this is not surprising given that this estimate measures the innovators

advantage over a typical bank in the product group, rather than over a bank underwriting the same security

only.

For this aggregation, the cumulative equity-linked principal that the bank has underwritten the period

before also increases the demand for its underwriting services within the category of securities, as so does

the accumulated number of innovations. Controlling for these eects, the coecient of the underwriting fee

is also signicantly dierent from zero, as well as the substitution parameter.

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4.4 Further Comments

Another concern may be that securities underwriters actually cooperate, so the demand for a given banks

variety of a security is not independent of the demand for other banks varieties, if these banks were part

of t he others underwriting syndicates. It is well known that underwriters frequently form syndicates to

structure the deals and s ell t he securities. Moreover, as Nanda and Yun (1995) argue, comanagement of

underwriting may be a form of cooperation that overcomes the free-rider problem in securities innovation:

if all the competing banks underwrite the rst issue of an innovative security jointly, they can share the

risks a nd the prots of the innovation in order to minimize the spillover of development costs. Thus, they

argue, one would expect that underwriters would team up in syndicates more often when the market for

the innovation is small, or when a potential innovator does not expect to capture a large share of the new

market. However, there are strong reasons to b elieve that the joint management of deals is not common

practice in the Equity-Linked and Derivatives class. Of the total 66 2 equity-linked deals observed, only 17

are co-managed. In 15 of these cases, one of t he co-managers was indeed the innovator, but in only t hree

cases the co-managed deal happened before the 5th deal (in fact only one rst deal is co-managed). We only

observe two deals where the innovator shared the management with a bank that also underwrote any other

deal using that security.2 4 ;2 5

Another important point worth noting is that the in the sample use one cannot observe when where

varieties made available by imitators, i.e., since when were rivals competing. The econometrician can only

compute imitators market shares after they have underwritten their rst issue. For example, if an imitator

appears on period 5 there are no observations for this variety in period 4 or anytime before when the security

was in the market but not yet imitated. There is the possibility that the bank was oering this variety well

before we can observe its rst deal. The exclusion of these observations may bias the results, but the bias is

to reduce the estimate of the innovators advantage. Since entry of imitators can only happen sooner than

we can attribute it, the dataset would actually have some more zero market share observations associated

with imitating banks. Thus, the estimate we have now, which is already positive and signicant, could even

be larger. Note too that a s we increase the size of the time horizon for t he computation of market shares,

the problem noted above loses its eect on the results. This is because for larger time periods, the imitator

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appears most likely in th e same time p eriod as the innovator, and the p otential for bias is smaller. Precisely,

we observe that the estimated innovators advantage is largest when t he size of the time period decreases

(i.e., at t = 8 the estimate is highest).

Following this discussion, a remaining concern is, why are some securities imitated and others not. I

address this question in the next s ection.

5 Patterns of Imitation and Innovation in Equity-Linked Securi-

ties

The evidence discussed above has shown robustly that imitators are at a disadvantage with respect to

securities innovators: the demand for their underwriting service is, ceteris paribus, smaller. The disadvantage

is smaller though for later generation securities. In this section I use this nding to try to answer why some

securities are imitated and others not. Understanding why and when these innovations are imitated sheds

some more light into the nature of the game that investment banks play when underwriting issues of corporate

derivatives.

5.1 The Decision to Imitate

As I pointed out earlier in the paper, there are 43 securities that could have been imitated, but only 18

of t hese actually were imitated by rival banks. Even if imitation were costly, and underwriting deals as an

imitator rather than an innovator puts the bank at a disadvantage, it is clear that imitation should be less

costly than innovation (e.g., free-riding the costs o f d evelopment and legal advice). Table 13 shows some

preliminary ndings that help to narrow our focus on the possible explanations for why some securities are not

imitated. Panel A of the table shows that imitation seems to be more frequent in the rst generations than

in the later ones: the null hypothesis that no imitation is associated with later generation securities can be

rejected with 91% condence. In Panel B I group generations in three categories: rst generation securities,

securities between generation 2 and 6, and later generation securities. In this case, the statistical association

is stronger (the p-value is 0.047).26 Panel C suggests that banks are more likely to be the innovators of a

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given generation if they had previously imitated or innovated in the same product group: we can reject that

there is no association between having previously underwritten deals in the same product group and being a

next innovator with 96.6% condence. Thus, it seems that doing deals helps banks to develop new products

but that this helps less later on in the product sequence.

Imitation is also increasingly protable, since the demand for an imitators variety increases faster for

later generations. This seems to be in contradiction with the fact that there is less imitation later because,

even if imitation is costly, if it becomes more attractive it should be more frequent. A possible conjecture

is that the benets of imitation spill-over to rival underwriters that dont imitate: if they observe more

imitation, then it is more likely that they will b e successful at attempting next generations innovation. If

spill-overs are large enough, banks will imitate less often. Probably a more obvious conjecture is that banks

have dealt enough by the time they attempt at innovating late generation securities, so there is little gain

from imitating if it cannot increase enough anymore the future success probabilities. But even in this case,

imitation prots are higher due to the higher demand. Moreover, a bank can always deal its innovations and

imitate other banks products simultaneously.

To understand better the decision to imitate, consider t he following very simple decision rule, whose

predictions I will confront later to the data. A new security, which is the g th generation in a sequence

of related innovations has been introduced and Ebanks (entrants) have to decide to imitate the product or

not. Imitating product g increases the chances that the bank m ay develop a next generation product, from

a probability of z(g) to z(g) + z: The other benet of imitation is that, in case of failure at attempting an

innovation, the bank can always enjoy imitators prots, Im(g): The evidence so far shows that Im(g) is

increasing in g: Suppose that Im(:)is dierentiable.

Imitation costs F, and if one bank imitates then this will increase the others probability from z (g)+ hz:

The spill-over of imitation is thus hz; where we assume that 0 h 1 to rule out the case where a bank

benets more from observing imitators rather than imitating itself:An a ttempt to innovate a next generation

costs F >Fand it pays innovators prots In if successful. Assume that all the banks have an interest in

developing a next generation.

Consider an equilibrium where every b ank chooses t he same probability, p; of imitating the current

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generation. For any bank, if they are randomizing between imitating or not, it must be that the probability

of imitation chosen by the remaining E1 banks make the E th indierent. The payos from imitation

are:

(z(g) + z)In+ ( 1 z(g) z)Im(g) F F : (16)

If the remaining banks are imitating with probability p each, then a bank that does not imitate can benet

from a spill-over to the probability of innovating successfully. The probability is 1 (1 p)E1: Thus, the

payos to holding back from imitation are

[z(g) + (1 (1 p)E1)hz]In F : (17)

Note that it is crucial that imitation is costly, because otherwise, imitation would dominate and be

observed always. The equilibrium probability of imitating a given generation g is a value for p in the unit

interval th at equates (16) w ith (16). From this equality, dierentiating with respect to g we obtain:

dp

dg =

[1 z z(g)]0Im (g) z0(g)Im(g)

(E 1)(1 p)E2hzIn:

Thus, dpdg

< 0 if and only if z0(g)Im (g)> [1 z z(g)]0Im(g) for any value ofg :

Since the probability that a generation g is imitated is 1 (1 p)E1; the following predictions can be

made:

1. If imitation does not spill-over to non-imitators then imitation should be independent of the generation

number.

2. If imitation spills-over, and ifz (g)is increasing in g ; a nd z0(:)is h igh enough then imitation should be

less frequent in later generations.

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Note that

dp

dIn=

[1 h(1 (1 p)E1)]

(E 1)(1 p)E2hIn>0;

which implies that:

3. Imitation is more likely if the expected prots from innovating a next generation are higher.

Note also that there may be heterogeneity in t he players in terms of their experience imitating and

innovating past generations. The next section describes how these are addressed empirically and how these

predictions are tested.

5.2 The Evidence on Imitation

To test these predictions I t probabilistic regressions of a security being imitated or not on the generation

number of the security. I exclude the securities whose names were not trademarked in the US Patent and

Trademark Oce. This leaves 43 of them. As controls I use:

the total number of underwriting deals accumulated by previous generations in the same product group,

as a proxy for the expected size of the market of the next innovation;

the number of previous generations in the same p roduct group that were actually imitated, a s an

additional source that speeds up the spill-overs and makes imitation less desirable;

dummy variables that indicate if any of the underwriters of the given security were also dealers of the

rst generation, the second, and so on.

Table 14 shows the estimates of the regressions. The covariance matrix has been estimated using Hu-

ber/Whites estimator, which is robust to correlation of the securities within the group and robust to het-

ersokedasticity. Columns 1 and 3 use the generation number as the main regressor, and Columns 2 and

4 u se instead the same categorical variable used in Panel B of Table 13. Columns 1 an d 3 show that a

given generation is imitated with a probability that is smaller by 0.08 or 0.07 than the probability that

previous generation is imitated, respectively. These estimates are signicantly dierent from zero with 99%

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condence. Columns 2 and 4 we see that the probability of imitation is decreases for generations 2 and 3,

and decreases further for the remaining generations.

As expected, in larger product groups, the next product is imitated more frequently, and this eect is

also statistically signicant to the 0.01 level in all cases. Note that the regressions control for the number of

imitatedprevious generations, which is also statistically signicant and, as expected, reduces the probability

that the next products in the sequence are imitated. Also, securities w hose underwriters had also dealt

the groups rst generations are imitated w ith a higher probability, all other things constant. In other

words, securities that are imitated with a higher probability are those for which their innovators or imitators

have had accumulated experience innovating or imitating the rst generations. Having experience in the

second generations has a negative eect on imitation (experience in further generations was rejected of all

specications). This result, though, may just be a reection of the main hypothesis: that banks have less

incentives to imitate later due to the spill-overs of other imitators.

5.3 Further Comments

The results shown in this section and before have characterized to a large extent the nature of the game

played by underwriters of corporate derivatives. In these markets, the largest, most reputed investment

banks compete to underwrite issues which are, on average, much larger than common stock deals. To seize

high prots these banks dierentiate from others using their innovative ability, their engineering skills and

their placement capacity. Success at i nnovation gives them discretion to choose underwriting fees through

a demand advantage. Since t his advantage dissipates with time, it is not surprising that banks typically

dont co-manage deals with others: co-management could disclose too much private information to potential

imitators and speed up the elimination of innovators advantages.

Banks also need to imitate their rivals to stay in the game at the early stages of development of innovations.

Imitating early generations seems to increase the chances that banks will develop innovations in the future.

But even as imitation b ecomes more protable along the innovation sequence, less imitation is observed

later. Imitation becomes less frequent faster if there has been more of it in the earlier generations. This may

be due to the fact that b anks that d ont imitate also learn by observing deals completed by rival imitators,

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or possibly that banks tend to concentrate in their own products at later stages of the innovation sequence.

Here we have seen evidence supporting the former, although not against the latter. In any case, it is quite

apparent that the innovative process evolves to a situation where the top tier banks have all learnt enough

about the type of products in a group, and most of them underwrite deals with their own dierentiated

innovations.

At this stage, a comparison can be made with what we know of corporate underwriting in standard

equity. There, w ith little room for dierentiation in the security design, banks are dierentiated by the

coverage of their analysts or their reputation as underwriters of h igh quality stocks. Also, common stock

underwriting exhibits a great deal of cooperation between banks that need each others clienteles to sell their

issues successfully. Further, underwriting deals are priced almost symmetrically. Thus, it is clear that the

determinants of market shares in both lines of business are quite dierent. But even if this study of corporate

derivatives has shown a more competitive side of investment banks, it is most likely the case that the two

lines of business are related. To what extent the banks success in one of them may aect the success in the

other remains an open question. A fertile ground for future research may consist of a study of the investment

bank as a multiproduct rm, where innovation in corporate products allows the bank to increase its prot

margins by reaching larger deals, but cooperation with other banks in more conventional securities markets

is crucial to t he bank to d evelop the necessary reputation.

The ndings here also motivate future research on how the market structure aects the speed of nancial

innovation. The b enets of innovation are decreasing, as imitators catch up faster along the sequence.

Yet more innovation f eeds back into the demand for future generations. Are the incentives t o innovate

increasing or decreasing along the sequence? Future research may look into the determinants of the speed

and duration of innovation across dierent product groups, including certainly other classes of securities, not

only equity-linked derivatives.

6 Summary and Discussion

This paper has provided new evidence of the sources of rst-mover advantages in innovations in nance. The

existing empirical literature of nancial innovation identied the following stylized fact: that investment

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banks are able to prot f rom innovation despite being imitated almost immediately. Whatever advantage

they had over competitors, the clue to the protability of unpatentable innovation in nance was that

innovators were able to underwrite the largest market shares of corporate initial oerings.

This paper has tried to provide an answer to the question of what is the source of the advantage. For

this purpose I used data of all the New Issues using Equity-Linked and derivative corporate securities. This

paper has tested empirically the hypothesis that rms have stronger preferences for underwriters that are

innovators, not imitators. The theoretical motivation for this conjecture was the following: rms that need

to r aise capital h ave to use a security which is engineered by investment banks that act as underwriters. If

the underwriter is the innovator of the security, this signals he is better informed about the choices that will

be best for the rm. On average, the value to the rm f rom doing the issue with the innovator will be larger.

To nd an appropriate method to test this hypothesis I started by analyzing preliminary evidence that

suggested that innovations in corporate products such a s equity-linked securities are frequently improvements

or generations of previous designs, so that families of securities could be identied. I also noted that banks

oered dierentiated underwriting services. Thus, I used the discrete choice theory of product dierentiation

as the framework to model the decisions of rms to choose security structures and underwriters. The evidence

also suggested that innovators had advantages t hat presumably dissipated over time. Thus, I decided not

only to study the overall advantage of innovators, but its dynamics.

For that purpose I specied the value to a rm for choosing a particular s ecurity and a particular

underwriter whose parameters were estimable. I claimed that the advantage t hat the innovator had over

its competitors in the market to underwrite new issues can be summarized in an index that included his

identity, the time elapsed after the innovator was imitated, and the generation of a security. Moreover, this

index appeared directly in the value function o f a rm because banks make dierent engineering choices

contingent on their private information.

Using data of all the new issues of corporate securities from the Securities Data Company Database I

estimated the parameters of the dynamics of the innovators advantage for multinomial logit and nested logit

demand models. I a lso used nancial data from COMPUSTAT about t he rms t hat issued the securities in

the s ample to enrich the specication. A result consistent to all the specications was that preferences for

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innovators are, on average, stronger than for imitators. Interestingly, these p references were initially stronger

the later generation of an innovation, possibly reecting the fact that late generations get more complex and

are therefore harder to understand to imitators. The preference for an i nnovator over an i mitator diminishes

in time, possibly as a result of imitators catching up with innovators. Further, the speed o f the reduction

in the preference for innovators over imitators was larger for later generations. I interpreted this as the

fact that late g enerations appear n aturally when more information has been aggregated about the family o f

securities they belong to, making learning about the innovators private information easier.

This evidence h ere has also shown a dierent face of the investment banking industry. As opposed to

common stock underwriting, there is a large potential for product dierentiation, and little incentives to

cooperate with other banks so as to keep the demand advantages. Imitation, rather, is crucial for banks to

catch up and achieve a h igh level of comp etitiveness. However, it is possible too that the banks success

in more traditional lines of business may aect the success in markets with innovation. Asking how this is

so may award research in the future. Also, as the benets of innovation are decreasing b ut as innovation

feeds back into the demand for future generations through reputation, it is worth asking if the incentives to

innovate increase or decrease along the sequences of innovations.

The scope o f t he paper has been limited by the availability of d ata. Cost data was unavailable for most

of t he observations, making it unworthy to estimate the model jointly with a pricing equation. This would

have also allowed to test if innovators and imitators have dierent marginal costs for underwriting oers,

another potential source of rst-mover advantages.

This paper has also taken innovation as exogenous. The s et of choices available to rms was taken as

given at each time. Certainly, one interesting way to continue this line of research would be to identify the

preferences of rms for new securities at each time they make their choices. If the choices of the rm were

to choose a security of a set of already existing securities or to rather choose to be the rst issuer of a new

security, then the data in each deal could reveal what determines when an innovation is to be introduced.

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Figure 1

This Figure shows the plots of th