Frequent News and Pure Signals:

Evidence of a Publicly Traded Football Club

Georg Stadtmann∗

November 2003

Abstract

We use stock market data for Borussia Dortmund GmbH & Co.KGaA – one of the leading German football clubs – for an applicationof the news model. Due to the specific characteristics of the newsgenerating process, the case of a publicly traded sport club is a veryappropriate candidate for testing this model. We elaborate whethernew information regarding the sporting success helps to explain sub-sequent changes in the stock price of Borussia Dortmund. In a firststep, we develop an industry model that links the sporting success tothe economic success. In a second step, we quantify the effect of matchoutcomes in the different competitions on the subsequent change inthe stock price.

Keywords: News Model, Football Industry, Betting Odds, Stock Market

JEL classification: G14

∗ Institute for Development Strategies, Indiana University, Bloomington,USA and

Department of Economics, WHU Koblenz, Burgplatz 2, 56179 Vallendar,Germany, Phone: +49-261-6509-273, Fax: +49-261-6509-279E-mail address: stadtman@whu.edu

Frequent News and Pure Signals:

Evidence of a Publicly Traded Football Club

November 2003

Abstract

We use stock market data for Borussia Dortmund GmbH & Co.KGaA – one of the leading German football clubs – for an applicationof the news model. Due to the specific characteristics of the newsgenerating process, the case of a publicly traded sport club is a veryappropriate candidate for testing this model. We elaborate whethernew information regarding the sporting success helps to explain sub-sequent changes in the stock price of Borussia Dortmund. In a firststep, we develop an industry model that links the sporting success tothe economic success. In a second step, we quantify the effect of matchoutcomes in the different competitions on the subsequent change inthe stock price.

Keywords: News Model, Football Industry, Betting Odds, Stock Market

JEL classification: G14

1

1 Introduction

According to the news model of asset price determination, changes in asset

prices are the outcome of the emergence of new, non-expected information

that was not considered in asset prices so far. This information is frequently

labeled as a signal with respect to the fundamental value of an asset. When

testing the news model, it would be ideal to have signals available that

are very frequent, easy to quantify, occur solely when financial markets are

closed, become publicly available to all agents at the same point in time,

and have ex-ante observable expectations.

Regularly, signals such as earnings announcements are used to test the news

model. The disadvantage of using earnings announcements is that such

information occurs only infrequently, since such an analysis is based on

quarterly reports. Furthermore, some agents have access to this information

in advance which can lead to substantial problems due to insider trading.

Additionally, information regarding quarterly reports is already expected to

some extent, and is therefore at least partially reflected in market prices.

All the characteristics mentioned above lead to a scenario, where earnings

announcements cannot be regarded as pure signals so that the news content

is not easy to quantify.1

A stock market segment where signals come close to fulfilling the above men-

tioned criteria is the sports industry with publicly traded sports clubs, like

football teams. Football teams participate in different competitions like the

national championships, national cup competitions, as well as international

competitions such as, on an European level, the Champions League, UEFA,

and UI-Cup. Therefore, financial agents receive information on the strength

of a team regularly and frequently. Furthermore, the outcome of a game is

easy to quantify and the influence of a game’s outcome on league competition

is very transparent due to the overall league ranking. Another important

feature is that matches normally take place on weekends or at night, so that

the outcome of the games materialize when financial markets are closed.

Additionally, the outcome of the games becomes public knowledge at the

1See Brown/Hartzell (2001).

2

very same time and it can thus be excluded that financial agents act on inside

information. A very important aspect is also the fact that betting odds are

available for all matches. These betting odds can be used to extract market

expectations and ex-ante winning/losing probabilities. Therefore, one can

control for the ex-ante expected match outcome. Given these industry

characteristics, it becomes clear that publicly traded sport clubs can be re-

garded as a very appropriate candidate for an application of the news model.2

Until now, empirical evidence of the link between sporting success and

subsequent stock performance of publicly listed football companies is only

limited. Lehmann/Weigand (1998) as well as Renneboog/Vanbrabant

(2000) analyze the performance of the British football clubs. Dahlke/Rott

(2001) who focus on Borussia Dortmund do not use econometric methods

to examine the stock performance but concentrate on corporate governance

issues related to Borussia Dortmund. A study which comes closest to this

study is an empirical work of Feddersen/Maennig (2003). They elaborate

whether the stock market for shares of Borussia Dortmund is efficient in its

weak and semi-strong forms as defined by Fama (1970). The only Borussia

Dortmund specific sport related variable considered in the empirical analysis

is the actual ranking of Borussia Dortmund in the Bundesliga competition.

Hence, this study neglects the fact that Dortmund also competes in the

DFB Cup competition as well as competitions on an European level. As we

show, the European competitions are a very important factor to generate

revenues and should therefore, not be neglected in an empirical analysis of

publicly traded football clubs.

The remainder of the paper is organized as follows. In Section 2, we introduce

the theoretical concept of the news model. We show how new information

2Nevertheless, one may argue that the football industry may also have some disadvan-tages. Football fans are often regarded to act irrationally and one cannot rule out thatfootball fans also engage in stock market trading. However, some institutional investorshold major stakes in Borussia Dortmund GmbH & Co. KGaA. For example, in 2000/2001the Deutsche Bank was holding a stake of 10 % and an international investment fund washolding a 5 % in the company (Borussia Dortmund 2001, p. 9). Largest single shareholderis still the Ballspielverein Borussia 09 e.V. Dortmund which holds a stake of more than25 % (Borussia Dortmund 2002, p. 30). Furthermore, the Deutsche Bank was the mainunderwriter during the IPO process.

3

about future fundamentals influences the current stock price and highlight

the role of the expectation formation process. In Section 3, we focus on the

football industry. We develop a theoretical framework that highlights the

link between the sporting success and the economic success. Equipped with

this theoretical framework, we derive the main hypotheses which are tested

in the empirical part of this paper. In Section 4, we present the results of

our empirical analysis and perform several statistical tests to confirm the

robustness of our main results. In particular, we focus on the role of the

expectation formation process by using betting odds information to control

for the ex-ante expected fundamentals. The last section concludes.

2 The News-Model

The corner stone of the news model is an asset evaluation equation.3 It

is assumed that there exist some fundamental variables (zt) – expressed in

natural logarithms – that explain the log of the asset price (st). Let t index

time and Et(st+1 − st) the expected change in the asset price for the next

period conditional on all information available at time t. We then have

st = α1z1,t + α2z2,t + α3z3,t + ...αnzn,t + γEt(st+1 − st). (1)

It is assumed that all n−coefficients (αi) which express the impact of the

fundamental variables on the asset price are constant over time. Besides the

impact of the fundamental variables, it is also assumed that the expected

change in the asset price also affects the level of the current asset price.

The expected relative change in the asset price influences the current asset

price through the parameter γ which is assumed to be positive and constant

over time. Collecting all fundamental variables in a column vector Zt of

dimension (n× 1) and all coefficients in a row vector A of dimension (1×n),

yields st = AZt + γ[Et(st+1)− st]. Solving this equation for st, we obtain

st =1

1 + γAZt +

γ

1 + γEt(st+1). (2)

3The news model was initially introduced by Frenkel (1981) and Mussa (1982) as amodel to explain exchange rate movements. See also Copeland (1994) for a good overviewand textbook version of the model.

4

The current asset price can, therefore, be expressed as a function of the

fundamental variables and the expected asset price of the next period. The

following transformations are performed to eliminate the expected asset price

parameter from equation (2) so that the current asset price can be written as

a function of current and expected fundamentals. To this end, the expression

containing the expected asset price has to be eliminated from equation (2).

Leading the time index t of this equation by one period, and taking in period

t the expectations of the asset price for period t + 1, one gets

Et(st+1) =1

1 + γAEt(Zt+1) +

γ

1 + γEt(st+2). (3)

Using equation (3) to eliminate the expectations Et(st+1) from equation (2),

we have

st =1

1 + γAZt +

γ

1 + γ

1

1 + γAEt(Zt+1) +

(γ

1 + γ

)2

Et(st+2). (4)

Taking in period t – in analogy to equation (3) – expectations about the asset

price in period t+2 and eliminating the expectation expression Et(st +2) in

equation (4) leads to:

st =1

1 + γAZt +

γ

1 + γ

1

1 + γAEt(Zt+1) + (5)

(γ

1 + γ

)21

(1 + γ)AEt(Zt+2) +

(γ

1 + γ

)3

Et(st+3).

Performing this kind of substitution n times and letting n approach infinity,

the last expression will converge towards zero, since γ > 0.4 We obtain:

st =1

1 + γAZt +

1

(1 + γ)A

∞∑

j=1

(γ

1 + γ

)j

Et(Zt+j). (6)

Equation (6) shows that the asset price of period t is solely determined by

the realized fundamentals in period t and the expected fundamentals for all

4To be more precise, the condition limn→∞[γ/(1 + γ)]nEt(st+n) = 0 only holds ifthe expectation expression does not grow faster towards infinity than the discount fac-tor [γ/(1 + γ)] converges towards zero. This assumption is known as the transversalitycondition (see Mark 2001, p. 68).

5

future periods. Furthermore, it should be emphasized that expected changes

in fundamentals enter the asset price equation only at a discounted value,

with [γ/(1+γ)] serving as the discount factor. This implies, that the further

a change in the fundamentals lies in the future, the lower its impact on the

current asset price. Taking again in period t−1 expectations about the asset

price in period t as given in equation (6) and subtracting the expectations

Et−1(st) from the asset price st, yields:

st − Et−1(st) =1

(1 + γ)A[Zt − Et−1(Zt)] + (7)

1

(1 + γ)A

∞∑

j=1

(γ

1 + γ

)j

[Et(Zt+j)− Et−1(Zt+j)] .

Equation (7) shows that a divergence of the observed asset price in t from

its in t− 1 expected value can be due to two factors:

• On the one hand, it is possible that the fundamentals realized in period

t differ from the expected fundamentals.

• On the other hand, it is possible that new information arrives so that

expectations about future fundamentals are adjusted.

Both factors can lead to a scenario where agents in the financial markets

change their evaluation of the fair value of an asset due to the arrival of new

information.

3 The Professional Football Industry

3.1 The Structure of the German and European Foot-ball Competitions

The top football division in Germany is the Bundesliga, which was founded

in 1963. It has 18 teams that play each other for the German championship

in home and away matches, alternating between their home and their

opponent’s stadiums. Thus, each team must play 34 Bundesliga matches

per season. In addition to the Bundesliga matches, a competition is held

6

each year for the DFB Cup. Under the current rules, 64 teams take part in

the first round of the cup competition. In addition to all teams from the 1st

and 2nd Bundesliga, clubs from the two regional leagues and amateur teams

that may reach the main draw of the DFB Cup competition via qualification

matches, perticipate in the main round. The DFB Cup competition is

played using a knockout system, i.e., only the winning side in a given

match qualifies for the next round. The pairings are drawn by lot (Borussia

Dortmund 2000, pp. 21 – 22).

Since the beginning of the 1999/2000 season, 32 European top teams have

been participating in the Champions League, a championship organized

by the UEFA.5 In addition to the competition’s sporting dimension, the

significant financial value of participation is of great importance to football

clubs. Under the current rules, the first two teams in the Bundesliga

automatically qualify to play in the Champions League. The third ranked

team may also qualify for the Champions League via a qualifying round.6

The competition initially begins with two rounds of matches where the

teams in each group play each other. The best eight teams then qualify for

the quarterfinals. Thereafter, the competition is continued under a knockout

system with home and away matches. The final is held at a neutral stadium.

Since the 1999/2000 season and the abolition of the European Cup Winners’

Cup competition, the only competition on an European level other than the

Champions League is the UEFA Cup. The competition is conducted using

a knockout system with home and away matches. In the third round, eight

teams that were knocked out following the first round of group matches

in the Champions League are added to the UEFA Cup competition. The

fourth to sixth placed teams from the Bundesliga qualify for the UEFA Cup,

5The Union des Associations Europeennes de Football (”UEFA”), an association formedunder the law of Switzerland, was founded in 1954. It is the umbrella organization of theEuropean football associations, and today has 51 members. In addition to the EuropeanChampionship, which is held every four years, UEFA organizes club championships suchas the Champions League and the UEFA Cup.

6In the 2000/2001 and 2001/2002 season even the fourth placed team had the chanceto qualify for the Champions League (CL). Those teams that fail to qualify for the CL viaqualification matches are automatically qualified for the UEFA Cup competition (Kicker2000, p. 3 and 2001, p. 3).

7

as well the DFB Cup winner.7 Two other Bundesliga teams may qualify to

participate in the competition via a further qualification round, the so-called

UEFA Intertoto Cup (”UI-Cup”).

– Insert Table 1 here –

Table 1 gives an overview of the sporting success of Borussia Dortmund for

the time span under consideration (11/2000 – 11/2002). In the Bundesliga,

Borussia Dortmund was quite successful with a 3rd place at the end of the

2000/2001 season and winning the national championship at the end of the

following season. Taking all observations for this competition together, one

can analyze the influence of the outcome of 70 matches, 37 of them were

won while 11 games were lost.

The good performance in the national competition allowed Dortmund to

qualify two times for the Champions League. Although they dropped out

after the first round in the Champions League in 2001/2002, they took

the chance to compete in the UEFA Cup competition subsequently and

made it to the final. Therefore, we observe 23 signals of the international

competitiveness of Borussia Dortmund.

While being highly successful in the two competitions mentioned before,

Dortmund failed early in the DFB Cup competition against ’underdogs’ of the

second or third league. Hence, we only have a limited number of observations

for this competition.

3.2 The Link Between the Sporting Success, Revenues,and Profits

Figure 1 highlights the link between the success in the sports league and the

financial success of a football club (see Lehmann/Weigand, 1997). If a team

7Should the winner of the DFB Cup competition be qualified for the CL, the loser of theDFB Cup final is qualified for the UEFA Cup. The number of starting places per nationalfootball association depends upon the clubs’ rating in the UEFA Five Year Evaluation,depending on the results of the respective national club teams during the preceding fiveyears.

8

is successful and wins some games in a row or has a good position in the

overall ranking,

• the club has the chance to qualify for an European competition like the

Champions League, UEFA or UI-Cup,

• the club will attract more spectators and gate attendance will be higher,

• and the club will have a larger presence in the mass media like daily

broadcasts in the TV news or in sport magazines, leading to a stronger

brand name.

– Insert Figure 1 here –

This success will lead to higher revenues due to the following linkages:

1. In Germany the television rights of the Bundesliga and the DFB Cup

are centrally marketed by the Deutsche Fußball Bund (DFB) and the

Deutsche Fußball Liga (DFL).8 Before the breakdown of Kirch Media

the contract foresaw to pay – on average – approximately 383 million

Euro per season. Under the terms of this agreement, the first division

receives 80 % of the total amount. Half of this income is divided such

that each member of the first division receives a fixed amount of about

8.5 million Euro. The other half is paid according to performance-based

criteria, depending upon sporting success during the last three as well

as the current season (Borussia Dortmund 2000, p. 24).

2. With a qualification for a European competition, the club can generate

additional funds from selling the broadcasting rights of this compe-

tition. While the broadcasting rights of the Champions League are

centrally marketed by the UEFA, every participant in the UEFA Cup

competition markets the broadcasting rights of its home games indi-

vidually (see Elter 2002, p. 86).

3. A successful team is able to generate higher advertising and sponsoring

revenues, because most sponsoring agreements provide for graduated

8See Kruse/Quitzau (2002) and Parlasca/Szymanski (2002) for a critical analysis ofthe central marketing of television rights to football matches.

9

revenues based on the team’s performance. Especially, a participa-

tion in European competitions can generate additional funds (Borussia

Dortmund 2000, p. 29 and 2001, p. 15).

4. Furthermore, a higher gate attendance will lead to higher

ticket and merchandising revenues. Gartner/Pommerehne (1978),

Lehmann/Weigand (1997) as well as Czarnitzki/Stadtmann (2002) an-

alyze the link between sporting success and attendance figures for the

German football industry. Both studies find a significant positive link

between the two variables.9

The link between sporting success and higher gate attendance is only valid

if the stadium capacity is not a binding constraint. Of the 35 Bundesliga

home games of Borussia Dortmund under consideration, 11 matches were

sold out. The average usage of the stadium capacity (68,600 spectators)

in the underlying time horizon is 96 %. The lowest number of spectators

during the underlying time horizon was 60,500 (Kicker 2003). Hence, the

stadium capacity is a binding constraint for Borussia Dortmund. Therefore,

the possibility to generate additional revenues by attracting a larger crowd

seems to be restricted – at least in the short run.10 Nevertheless, in times

of lacking sporting success, revenues could go down due to lower gate at-

tendance. Furthermore, the argument that sporting success leads to more

revenues stemming from ticket sales holds at least for the DFB and the UEFA

Cup competition where games are organized in a knock out modus.

– Insert Table 2 here –

Table 2 focuses on the revenue structure of Borussia Dortmund.11 The

balance sheet figures highlight the importance of those revenues that can be

9For example, Czarnitzki/Stadtmann (2002) find that every position gained in theoverall ranking leads to a higher attendance of about 230 spectators, i.e., the team whichis ranked first can expect approximately 3,910 additional spectators than the worst team.These authors explicitly control for the stadium capacity by using a generalized Tobitestimator for individual and known cut of points.

10To meet excess demand in the future, Borussia Dortmund plans to increase stadiumcapacity until the beginning of the 2003/2004 season. The enlarged arena will have acapacity for 80,000 spectators in Bundesliga matches (Borussia Dortmund 2002, p. 42).

11Since the fiscal year of Borussia Dortmund ends as of June 30 each year, the balancesheet figures for the fiscal year 2002/2003 are not available yet.

10

generated through selling broadcasting rights and advertising/sponsoring.

Furthermore, this table also shows the immense decrease of revenues during

the 2000/2001 season, when Dortmund did not qualify for an European

competition. When controlling for those revenues stemming from transfer

operations, revenues decreased by 41.5 % during fiscal year 2000/2001

compared to the fiscal year 1999/2000.12 Despite the immense sporting

success, overall profits can be regarded as relatively low. So far, Borussia

Dortmund has not paid any dividends even after winning the German

championship.

With a lot of funds at hand, a football club can invest in ”bricks and

bones”. It can increase the capacity or the comfort of the stadium.13 It can

also hire better players to increase the overall strength of the team. These

investments can have feedback effects on game attendance and the success

of the team, indicated by the dotted arrows in Figure 1.14 Furthermore,

the club has the opportunity to invest in sports-related fields, such as TV

stations, travel agencies, hotels and restaurants, or sportswear companies.15

12The high amount of transfer revenues stem mainly from selling the transfer rights ofplayer Evanilson to AC Parma. Nevertheless, Evanilson is still in the team of Dortmunddue to a licensing agreement (see Borussia Dortmund 2001, p. 16).

13See Dietl/Pauli (2002) for a detailed analysis of the economics of the German footballclubs with respect to their stadiums. They argue that Germany’s professional footballteams need modern stadiums in order to increase ticket revenues but that a high investmentrisk can lead to an underinvestment in stadium projects. Furthermore, they focus on theconflict between clubs that invest in their own arenas, like Borussia Dortmund, and clubsthat receive either direct or indirect stadium subsidies.

14Szymanksi/Smith (1997, p. 136) find in an empirical analysis of the English footballindustry that the function between profit and position in the league has a negative slope.This negative slope indicates that increased spending on players is – on average – not selffinancing through higher performance and revenue.

15For example, Borussia Dortmund (2000, p. 43 – 44) argues that it tries to investin lines of business related to football to become less dependent on the sporting successof the professional team. To this end, Dortmund established their own line of sportsclothing which is marketed under the brand name goool.de. Furthermore, Borussia Dort-mund owns part of their home arena ’Westfalenstadion’, holds a 51 % stake in the travelagency B.E.S.T, a strategic alliance with Euro Loyd Reiseburos, and a 33.4 % stake inOrthomed GmbH, a company that is specialized in medical rehabilitation and physicaltherapy services. Dahlke/Rott (2001, p. 10) critically remark, that Borussia Dortmundmay not have core competencies in these related fields. Furthermore, they emphasize thatit should be the shareholder – and not the company itself – who is responsible for portfoliodiversification.

11

Last but not least, higher revenues should also increase the profitability of the

club which should also lead to higher (expected) dividend payments. Higher

expected dividend payments will – according to the standard theory of fi-

nance – lead to higher stock prices. Hence, if all relations hold as supposed

in our framework, the outcome of a game should influence stock prices. How-

ever, the simple model neglects some special industry characteristics that are

reviewed shortly in the next subsection.

3.3 Deriving the Hypotheses

The literature on sport economics provides major arguments that question

the causal relationship between sporting and financial success as outlined

in the industry model. Some factors are company or Bundesliga specific,

while others are related to the overall industry characteristics and the

league structure of team sports.16 In Germany, the 50 % + 1 rule assures

that the voting majority of all clubs remains in the hand of a non-profit

organization (eingetragener Verein, ”e.V.”). 17 Therefore, we cannot rule

out that the members of the non-profit organization try to maximize their

personal welfare which may be directly related to the sporting, but not to

the financial success of the club. Since the members are not entitled to

receive dividends or similar kinds of payments, they may prefer to reinvest

all revenues in players in order to maximize team strength. If this is the case,

sporting success would positively influence revenues, but not necessarily

profits and therefore stock prices.18

16See also the discussion in Dahlke/Rott (2001), p. 6 as well as Lehmann/Weigand(2002).

17In case of Borussia Dortmund, the company is structured as a partnership limited byshares which is labeled as Kommanditgesellschaft auf Aktien (KGaA). The legal form ofa KGaA is uncommon in an international setting. Therefore, Borussia Dortmund informsthe potential shareholders in its Offering Circular Prospectus (2000, pp. 14 – 15): ”Thefact that, in comparison to the shareholders of a stock corporation, the Shareholders ofBorussia Dortmund have only a very limited ability to influence management at the Gen-eral Shareholders’ Meeting or indirectly through the Supervisory Board, as well as the factthat foreign investors in particular are largely unfamiliar with the KGaA as a corporatelegal form, could affect investor interest and lead to a decrease in the value of the shares.”

18The low degree of profit orientation among the clubs of the 1st and 2nd GermanBundesliga is backed by a recent survey among the club managers. Of the 21 managerswhich answered the survey 14 reported that maximizing team strength under the side

12

Franck/Muller (2000, p. 153) argue that a share of Borussia Dortmund may

– due to industry characteristics – represent fewer property rights compared

to a share in a company of an other industry. The football industry like

any other sports league, is characterized by a two stage production process:

On the club level – the first stage – different input factors are bundled to

form a team. On the league level – the second stage – the DFB/DFL set

the overall rules, structure the different competitions and thereby create

a marketable product (Franck/Muller 1998, p. 123). All the rules and

restrictions set by the DFB/DFL – which seem to be necessary to guarantee

league competition – lead to a scenario in which the different rights of the

football club, and, therefore, the rights of the shareholders, too, are limited

compared to other industries.

Franck/Muller (1997) compare league competition with a rat race where

the chance of getting the cheese (higher revenues for the individual club)

increases with the speed of the rat (investments in better players), although

no additional cheese is produced (revenues for the whole league are constant).

They argue that the main problem is the rank interdependence within the

sports league. A team that gains a higher rank and receives higher revenues

creates automatically a negative external effect for the displaced club which

is not taken into consideration by the successor. As a consequence the

displaced club may also increase its investments in better players to restore

the former ranking.19

Furthermore, these theoretical arguments are backed by empirical evidence

with respect to the most mature market for publicly listed football clubs:

The majority of all British clubs that went public showed a dramati-

cal decline in stock prices and under performed the overall market (see

condition of the budget constraint is the objective of the club. Only three managersindicated that their objective is profit maximization (Swieter 2002, p. 63).

19In a setting of rational agents, relatively weak clubs should accept the relative strengthof the other clubs and anticipate the counter of the opponents. For the sport industry,Frank/Muller (1997) find several theoretical arguments why club managers do not actrationally, but overinvest and become part of a rat race.

13

Lehmann/Weigand 1998 as well as Renneboog/Vanbrabant 2000).20 These

industry characteristics lead to a scenario where the hypothesis that the

sporting success does automatically also lead to additional profits is not

backed by existing empirical findings. Nevertheless, a few British clubs won

the race, are profitable, and even outperformed the overall stock market.

Furthermore, Lehman/Weigand (1998) show that Borussia Dortmund is

managed with a high degree of professionalism and financial soundness and is,

therefore, considered as one of the two candidates of the First German Bun-

desliga that seems to be prepared to perform as a publicly traded company.21

Since Borussia Dortmund is the first German club that went public, it could

be that their move to the stock market may turn out to be successful –

despite the overall finding that an investment in football clubs is, on average,

unprofitable. Due to a first-mover advantage Borussia Dortmund may be

even able to materialize an unsuitable part of the goodwill which was build

up by the German Bundesliga since its foundation in 1963 (Franck/Muller

2000, p. 156).

The discussion of the different arguments highlights a very important ques-

tion: Does the link between higher revenues – generated through sporting

success – and higher profits holds for the industry under consideration? As

pointed out in this paragraph, theoretical and empirical findings do not ex-

clusively support the one or the other hypothesis. Therefore, it is very in-

teresting to analyze how the agents at the financial markets are judging this

issue. Hence, we test the following hypotheses as suggested by the industry

model (Figure 1):

20Currently, the shares of 36 European football clubs are listed at different stock ex-changes. England is the most matured market with 20 clubs listed, followed by Denmark(7), Italy (3), Portugal (2), Turkey (2), The Netherlands (2), and Germany (1). Only fourclubs (Manchester United, Tottenham Hotspur, Brondbyernes I.F. Fodbold, and AGFKontraktsfodbold A/S) were traded at prices which were above the public offering priceas of August 2002 (WGZ-Bank 2002, p. 123).

21Lehman/Weigand (1998) use their insights from the stock market performance ofthe British football clubs to evaluate the chances and readiness for all clubs of the FirstGerman Bundesliga to go public. Using a four rank scale (IPO possible without problems,possible, no chance right now, no chance even in the medium term) only two clubs wereranked in the highest group: Bayern Munich and Borussia Dortmund.

14

• H1: A won match should influence stock returns positively.

• H2: A lost match should influence stock returns negatively.

As stressed above when analyzing the revenue structure of Borussia Dort-

mund, the high amounts of money that can be earned in the European com-

petitions should be considered. Hence, the third hypothesis is:

• H3: A won/lost game in a European competition will influence stock

returns to a larger extent than a win/defeat in the national competition.

4 The Empirical Analysis

4.1 Controlling for Expectations

The main idea of the news model is that only the difference between the

realized fundamentals and the expected fundamentals has to be regarded

as the news component. Putting it differently, only the expectation error

should influence stock prices. This argument can easily be formalized by

using the news model introduced in Section 2. Referring to equation (7),

the first component of the right-hand side reflects that the asset price will

only be influenced if the realized fundamentals differ from the expected ones.

Therefore, if a home win was already anticipated to a large extent (there

always remains some kind of uncertainty), stock prices should not react that

much. One method to control for the expectations of relative strength of

the two teams involved in a match and, therefore, in the expected match

outcome is to use betting odds information. As many studies show,22 the

football betting market seems to be pretty efficient in the sense that it seems

to be impossible to beat the market by using information such as rankings

of the teams involved, home-away performance, and injuries of important

players. Hence, it is interesting to employ these implicit expectations in

the current analysis. The betting odds used in this analysis were kindly

provided by gamebookers.com, an online bookmaker. Using the conventional

22See for example Pope/Thomas (1989), Williams (1999), and Forrest/Simmons (2000)for a literature survey. The finding of an efficient betting market is not common for allsports. See Thaler (1992) for an extensive literature survey of the horse race bettingmarket, which shows some degree of market inefficiencies.

15

abbreviation for a home win (1), a draw (0), and an away win (2), Table 3

highlights the background of the ’news proxy’ used in the following analysis.

– Insert Table 3 here –

For example, for the match Dortmund versus Koeln quotes prior to the game

were 1.35 for a home win, 4.25 for a draw, and 6.90 for an away win. This

means a bettor who put 1 Euro on a draw received 4.25 Euro. Comparing

the different quotes for this match already highlights that Dortmund was

regarded as the favorite in this match. Summing up the inverse of the quotes

(1/1.35 + 1/4.25 + 1/6.90), yields the mark-up of the betting company.

The higher this mark-up, the higher the price for the bet. As becomes

evident in Table 3, the mark-up is around 12 % for this betting company.

By controlling for this mark-up, one can compute the probability implicit in

the betting odds for a home win which amounts to 66 % [1/(1.35 ∗ 1.12)],

for a draw which is equal to 21 % [1/(4.25 ∗ 1.12)], and the away win of 13

% [1/(6.10 ∗ 1.12)], respectively.23 These implicit probabilities show that

Dortmund was indeed regarded as the favorite.

Having these probabilities at hand, it is possible to control for the expecta-

tions of the financial agents. Because the winning team receives 3 points and

a draw will lead to 1 point, the expected number of points for Dortmund in

the match against Koln is equal to 2.19 (3 ∗ 0.66+1 ∗ 0.21). Since Dortmund

drew in this match, the outcome has to be interpreted as a negative informa-

tion and the expectation error amounts to a negative 1.19 (1 – 2.19). Only

this expectation error has to be regarded as the new information which has

to be priced in on Monday morning. Due to the fact that Dortmund under-

performed in this match, stock prices should decrease. In a second example,

the expected number of points in the away match where Dortmund plays in

Munich is equal to 1.24 Hence, the outcome of the match was in line with

the prior expectations and should therefore have no impact on stock prices.

23See Bosch (2000), pp. 91 – 110 for an analysis how to extract probabilities frombetting odd information.

24The team which plays at home always has a home field advantage. SeeSchwartz/Barsky (1977) and Vergin/Sosik (1999). See also Skarupke (2000) for an empir-ical analysis of home advantage in the German Bundesliga.

16

4.2 Description of the Data Set

In addition to the match outcome variables described in the previous para-

graph, we also consider ad-hoc announcements of Borussia Dortmund as a

signal which could influence stock prices. Ad-hoc news are taken from the

homepage of Borussia Dortmund and are classified according the following

news groups:

• A player renews his contract (LONG),

• a new player is hired (HIRE),

• a player is sold to another club (SOLD), and

• on 12/20/2001 the contract of the coach Matthias Sammer was ex-

tended until 2006 (COACH).

It is possible that some (private) information regarding the incidences men-

tioned above was already in the market before being publicly released. Thus,

the dummy variables take on the value of 1 on the day of the announcement

as well as +/– 2 days before and after the announcement day of the ad-hoc

news.

– Insert Figure 2 here –

Daily stock data of closing prices as well as the development of the SDAX

are taken from Datastream. Figure 2 highlights the development of the stock

price of Borussia Dortmund after going public at the end of October 2000.

With an exception of a short period in the summer of 2001, the stock under

performed the SDAX and lost about 60 % of its value. Although this seems

to be much for the two-year horizon under consideration, the loss is (more

or less) in line with the overall market performance, since the SDAX also

decreased by about 50 %.

4.3 Regression Results

In a first step, we estimate the following Model 1:25

25Since financial time series like stock prices or stock indices are often non-stationary,we tested for stationarity by using the Augmented Dickey Fuller Test. It turned out that

17

∆DORTt = β0 + β1∆SDAXt + β2BUND EX ERt + (8)

β3EU EX ERt + β7DFB EX ERt +

β8SOLD + β9HIRE + β10LONG + β11COACH + ε.

Where ∆DORT denotes the change in stock prices and ∆SDAX the change

in the relevant stock market index.26 The regression results are presented

in Table 4. The estimated beta parameter takes the value of β1 = 0.6274,

meaning that a 1 % change of the SDAX only leads to an under-proportional

change in the stock price.27 The expectation error variable for the Bundesliga,

the EU competition and the DFB Cup competition are highly significant

and positive, meaning that points that were not expected (due to a win

of a game) had a positive impact and points that were already expected

but not generated (due to a loss of a game) had a negative impact on the

stock value. These effects are statistically highly significant. Hence, taking

the estimated coefficients into consideration, we cannot reject hypotheses H1

and H2. As the estimated coefficients of the expectation error variable for the

Bundesliga and the European competitions are nearly the same, the results

do not support hypothesis H3 that European matches are more important

than Bundesliga matches. The coefficients of all dummy variables, created

to measure the impact of the ad-hoc news, are not statistically different

from zero. On the one hand, this could be interpreted in a way that this

information does not influence the fundamental value of the asset. On the

other hand, since it is possible that these events were anticipated by the

public, we cannot rule out that the signal was already reflected in market

prices when it became public knowledge.

the log of the time series of the share prices and the index series are not stationary. Hencewe tested whether the first difference of the logged time series are stationary, which turnedout to be the case. The test results are available from the authors upon request.

26We estimate Models 1 – 3 over the full sample of trading days. Hence, our data setcontains trading days where a match took place the day before and trading days where nomatch was played the day before. This methodology allows to incorporate ad-hoc newsvariables as well as match specific variables. We drop this methodology in Model 4 wherewe condition on the fact that a match was played the day before.

27As a sensitivity analysis, we also regress the change in the asset price an a constantand the change in the SDAX. The estimated coefficient took the value of β = 0.591 andtherefore, does not vary significantly from the coefficient determined in Model 1. The R2

of this specification takes the value of 0.025.

18

– Insert Table 4 here –

As a sensitivity analysis we estimate Model 2

∆DORTt = β0 + β1∆SDAXt + β∗2BUND POINTSt (9)

+β∗3EU POINTSt + β∗7DFB OUTt +

β8SOLD + β9HIRE + β10LONG + β11COACH + ε,

where the variable BUND POINTSt (EU POINTSt) measures the

number of points gained in a Bundesliga (European) match. The variable

DFB OUTt denotes a dummy variable that assumes the value one on

trading days, following a day when Dortmund dropped out of the DFB-Cup

competition. Hence, in Model 2, we regress the stock price return on the

points gained instead of the unexpected number of points gained as an

additional specification. The estimation results for Model 2 show that the

estimated β∗2 coefficient is positive and significantly different from zero.

Furthermore, the β∗7 coefficient is significantly negative, implying that a

knock out in the DFB-Cup competition leads to a decrease in stock prices

of about 4.1 %. All other sport specific coefficients are not significantly

different from zero. As compared to Model 1, the goodness-of-fit decreases,

so that we conclude that Model 2 is not superior to Model 1. Therefore, we

continue to use Model 1 as our basis specification.

One may argue that the variable that measures the outcome of the European

matches is a combination of games that are played in the Champions League

and games that are played in the UEFA Cup competition. Hence, it may

bring further insights to separate the outcomes of the two competitions.

While all UEFA Cup rounds – with an exception of the final – are played

under a knockout system with home and away matches, we consider all UEFA

Cup games up to the final in a variable labeled UEFA EX ER and isolate

the effect of the lost UEFA Cup final in a separate dummy variable.28 Hence,

the regression equation of Model 3 is as follows:

28Since the betting odds prior to the game were nearly pari, it would not lead to furtherinsights to condition on the expectations.

19

∆DORTt = β0 + β1∆SDAXt + β2BUND EX ERt + (10)

β4UEFA EX ERt + β5UEFA FINALt +

β6CL EX ERt + β7DFB EX ERt +

β8SOLD + β9HIRE + β10LONG + β11COACH + ε.

The estimated coefficients reveal, that the outcome of regular UEFA

Cup matches did not influence the stock market in the expected pattern.

Nevertheless, the lost final led to a decrease in stock prices by about 9 %.

Furthermore, the estimated coefficient for the Champions League variable is

positive and larger than the coefficient estimated for Bundesliga matches.

However, the gap is not statistically different from zero. All other coefficients

lie in the same value range as in Model 1.

Another interesting question is, whether the stock market adjusts to new

information during the trading day or whether there is a measurable impact

of match outcome for more than one trading day. To address this point,

we also include the lagged independent variables of the match outcome

variables as additional variables. The estimated coefficients are presented as

Model 4 in Table 4. The lagged variables for Bundesliga, UEFA Cup as well

as DFB Cup matches are not significantly different from zero. This means,

that with respect to these signals, information is priced in within the trading

day following the day of the match. However, the lagged dummy variable for

the lost UEFA Cup final is significantly negative, meaning that this event

influenced stock prices for more than one day. Furthermore, the lagged CL

variable is significant, but has a negative sign meaning that there exists some

kind of overshooting behavior on the first trading day. Since the number

of observations for this competition is very limited, one should not drive

this interpretation too far. The results of Model 4 show that information

is regularly priced in on the subsequent trading day. Nevertheless, some

extraordinary events may also have an impact on the subsequent trading

days.

The regression results presented so far are based on daily observations. This

means that the data set incorporates trading days following days for which

20

we were able to observe the outcome of a match as well as trading days on

which no match was played the day before. Only this methodology allows us

to consider the effect of the ad-hoc news variables in the regressions analysis.

Since the estimated coefficients for the ad-hoc news variables turn out to be

not significantly different from zero, it seems to be interesting to drop the ad-

hoc variables and run the regression again for those trading days following

playing days. Hence we dropped the ad-hoc variables from Model 1 and

estimated the same specification for the smaller sub sample (97 observations).

All coefficients presented in Model 5 turn out to be in the same range as

compared to Model 1.29 Since the R2 is 0.4021, the goodness-of-fit can be

regarded as exceptionally high for this kind of stock market study. Hence, we

can conclude that new information contained in the match outcome variables

is the main factor influencing the stock market on trading days, following a

match.

5 Conclusion

We have applied the news model to the football industry to analyze, whether

new information regarding the sporting success can explain subsequent

changes in the stock price of Borussia Dortmund. The football industry

proves a very appropriate candidate for applying this model due to specific

characteristics: Signals are very frequent and easy to quantify, occur solely

when the markets are closed, become publicly available to all agents at the

very same time, and have observable expectations due to the existence of

betting odds.

According to the news model, only the expectation error should influence

stock prices. Hence, we use betting odds information to control for the

ex-ante expected match outcome. We show that there exists a close link

between the sporting success and subsequent changes in the stock market.

Therefore, the main hypotheses H1 and H2 derived from a theoretical model

cannot be rejected. However, hypothesis H3, predicting the outcome of

European matches should have a higher impact on the stock market than

29One may also choose Model 2 as a candidate for this kind of sensitivity analysis. Theresults did not change significantly.

21

the outcome of Bundesliga matches, is not supported by our regression

results. Although the estimated coefficient for Champions League matches

is larger, the difference between this coefficient and the one estimated for

Bundesliga matches is not statistically different at conventional error levels.

Future research could extend this study in several interesting directions: One

direction could be to test the several links of the industry model – outlined

in Figure 1 – empirically. A very important link in the model is whether

higher revenues, stemming from sporting success, also lead to higher profits.

One crucial factor is, therefore, how the cost structure responds to sporting

success. Hence, future research could focus on how player contracts are

negotiated and how sporting success leads to cost increases via contractual

fixed bonuses for the players.

Another direction for future research could be to extend this study for the

only German publicly traded football club is broadened by using stock

market information of international clubs. In doing this, one would be

able to check whether the results of this study are supported by a broader

international data set.

In the future, there is also a potential to update the existing study by us-

ing new observations. This would open up the possibility to test whether

estimated coefficients are stable over time. Specifically, because Borussia

Dortmund has been the first German football club that went public, it could

be that traders needed some time to become familiar with the football busi-

ness. In this case it could be that market efficiency was not constant over

time, but improved as traders became more familiar with the specific factors

important in pricing the stocks of the first publicly traded German football

club.

22

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26

Table 1: Overview of the Different Competitions

German Cup CompetitionSeason German Bundesliga European Competitions

’DFB Pokal’

· Not qualified, since Dortmund2000 – 2001 · 3rd rank at the end of season was only on 11th rank at the end

· Out in 3rd round against

of 1999 – 2000 seasonSchalke 04 on 11/29/2000

· Winner of the qualification games tothe Champions League against Donezk(8/7/2001 and 8/22/2001)

· 1st rank at the end of season · Out after 1st round in the CL · Out in 1st round against2001 – 2002 · Winning of the National · Switch to 3rd round of the UEFA Cup Wolfsburg Amateure

Championship (5/4/2001) competition on 8/25/2001· Loosing of the UEFA Cupfinal against Feyenoord Rotterdamon (5/8/2002)

· 2nd rank in the first phase of CL,· currently 2nd rank, after half qualification to 2nd phase · Out in 2nd round against

2002 – 2003of the season · until now: 1 match won and 1 SC Freiburg on 5/11/2002

lost in the 2nd phase

70 Matches: 23 Matches: 4 Matches:a)Summary

37 Win, 22 Draw, 11 Lost 12 Win, 6 Draw, 5 Lost 1 Win, 3 Lost

Note: a) The first two matches of the DFB Cup in the 2000 – 2001 season took placebefore November 2000 – the starting point of our empirical study – and are therefore, notconsidered in this analysis.

Table 2: Revenues and Profits of Borussia Dortmund GmbH & Co. KGaA

1999/2000 2000/2001 2001/2002Match Operations 17,624.7 13,829.9 17,841.7Advertising 26,048.8 13,233.4 27,951.9Radio, TV 35,001.1 19,339.1 45,975.7Transfer Revenues 3,047.4 25,259.6 1,235.5Merchandising 9,701.7 5,212.9 8,821.0Others 12.3 109.3 532.3Total Revenues 91,436.0 76,984.2 102,358.1Net Income 1.0 -9.0 1.4

Source: Borussia Dortmund (2001 and 2002). Figures are in thousand Euro.

27

Table 3: Betting Odds and Implicit Probabilities for the Outcome of a Game

Date Teams Result Betting Odds Mark-up Implicit Probability

05/19/2001 Dortmund vs. 1. FC Koeln 3:3 1.35 4.25 6.90 1.12 66 % 21 % 13 %02/09/2002 Bayern Muenchen vs. Dortmund 1:1 1.85 3.25 3.72 1.12 48 % 28 % 24 %

28

Table 4: Regression Results

Model 1 Model2 Model 3 Model 4 Model 5

β0 Constant-0.0016 -0.0018 -0.0013 -0.0013 -0.0135***

(-1.33) (-1.39) (-1.10) (-1.08) (-4.31)

β1 S DAX(t)0.6274*** 0.5751*** 0.6111*** 0.6433*** 0.7503*

(4.11) (3.57) (4.07) (4.18) (1.90)

β′1 SDAX(t-1) – – –-0.1392 –

(-0.89)

β2 BUND EXP ERROR(t)0.0187***

–0.0187*** 0.0192*** 0.0208***

(7.08) (7.20) (7.36) (6.49)

β′2 BUND EXP ERROR(t-1) – – –0.0025 –

(0.98)

β∗2 BUND POINTS(t) –0.0032**

– ––

(2.20)

β3 EU EXP ERROR(t)0.0181***

– – –0.0207***

(3.91) (3.71)

β∗3 EU POINTS(t) –-0.0004

– ––

(-0.16)

β4 UEFA EXP ERROR(t) – –-0.0051 -0.0049 –

(-0.69) (-0.67)

β′4 UEFA EXP ERROR(t-1) – – –-0.0068 –

(-0.93)

β5 UEFA FINAL(t) – –-0.0906*** -0.0900*** –

(-3.72) (-3.73)

β′5 UEFA FINAL(t-1) – – –-0.0571*** –

(-2.37)

β6 CL EXP ERROR(t) – –0.0283*** 0.0285*** –

(4.65) (4.71)

β′6 CL EXP ERROR(t-1) – – –-0.0140*** –

(-2.32)

β7 DFB EXP ERROR(t)0.0622***

–0.0626*** 0.0620*** 0.0460*

(3.05) (3.13) (3.12) (1.86)

β′7 DFB EXP ERROR(t-1) – – –-0.0130 –

(-0.65)

β∗7 DFB OUT(t) –-0.0413***

– ––

(-2.73)

β8 AD HOC SOLD0.0024 0.0048 0.0010 0.0020 –

(0.46) (0.88) (0.21) (0.40)

β9 AD HOC HIRE-0.0022 -0.0025 -0.0010 -0.0007 –

(-0.53) (-0.56) (-0.24) (-0.18)

β10 AD HOC LONG0.0032 0.0042 0.0023 0.0012 –

(0.56) (0.70) (0.40) (0.21)

β11 AD HOC COACH-0.0071 -0.0110 -0.0074 -0.0072 –

(-0.64) (-0.93) (-0.68) (-0.66)

R2 0.1521 0.0532 0.1839 0.2078 0.4021Adjusted R2 0.1390 0.0386 0.1682 0.1830 0.3761Durbin Watson Stat. 2.32 2.30 2.35 2.39 2.06Number of Observations 529 529 529 528 97

Figure 1: Stylized Model for the Football Industry

Higher Gate Attendance

Higher Profit =>

Higher Dividend =>

Higher Stock Price

Investment in Related Fields – TV-Stations – Travel Agencies – Hotels/Restaurants – Sportswear

Investment in the Size or Quality of the Facilities – Stadium Capacity – VIP-Lounges

Investment in Better Players

Higher Advertising / Sponsoring Revenues

Higher Merchandising

Revenues

Higher Ticket Revenues

Higher Broadcasting

Revenues

Qualification Champions League

UEFA/UI-Cup

Larger Attention in Mass Media

Success in the German Bundesliga

Figure 2: Performance of Borussia Dortmund AG Compared to the Benchmark SDAX

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

110.0%

Oct.00

Dec.00

Feb.01

Apr.01

Jun.01

Aug.01

Oct.01

Dec.01

Feb.02

Apr.02

Jun.02

Aug.02

Oct.02

SDAX

Borussia Dortmund AG