According to the news model of asset price determination, only the unexpected component of an information should drive the stock price. We use the Danish publicly listed football club Brøndby IF to analyze how match outcome impacts the stock price. To disentangle gross news from net news, betting odd information is used to control for the expected match outcome. --news model,football industry,betting odds,stock market,market efficiency,event study

Jørgensen, Casper W.

Moritzen, Mark R.

Stadtmann, Georg

English

Research Papers in Economics

 
 
 
 
 
 
 
 
The News Model of Asset Price 
Determination - An Empirical Examination  
of the Danish Football Club Brøndby IF 
 
 
 
 
 
 
 
  
Casper W. Jørgensen 
Mark R. Moritzen 
Georg Stadtmann 
 
 
 
 
 
 
 
___________________________________________________________________ 
 
European University Viadrina Frankfurt (Oder) 
Department of Business Administration and Economics 
Discussion Paper No. 313 
February 2012 
ISSN 1860 0921 
___________________________________________________________________ The News Model of Asset Price
Determination { An Empirical Examination
of the Danish Football Club Brndby IF
Casper W. Jrgensena, Mark R. Moritzena, GEORG STADTMANN
February 2012
Abstract
According to the news model of asset price determination, only
the unexpected component of an information should drive the stock
price. We use the Danish publicly listed football club Brndby IF to
analyze how match outcome impacts the stock price. To disentangle
gross news from net news, betting odd information is used to control
for the expected match outcome.
Keywords: News Model, Football Industry, Betting Odds, Stock Market,
Market Eciency, Event Study
JEL classication: G14, L83, G32
a University of Southern Denmark.
 Corresponding Author: GEORG STADTMANN, Europa-Universit at Vi-
adrina, Lehrstuhl f ur Volkswirtschaftslehre, insb. Makro okonomik, Post-
fach 1786, 15207 Frankfurt (Oder), Germany, Tel. +49 335 5534-2700,
stadtmann@europa-uni.de1
1 Introduction
The news model states that nancial agents collect every piece of publicly
available information and consider this information in their asset price
expectations. As a consequence markets are ecient in a semi-strong form
as dened by Fama (1970).
Changes in asset prices are caused by the appearance of new, non-expected
information that was not reected in asset prices so far. We use the Danish
publicly listed football club Brndby IF to analyze, how the match outcome
impacts the stock price. To disentangle gross news from net news, betting
odd information is used to control for the expected match outcome (see
also Dobson/Goddard 2001, Brown/Hartzell 2001, Ashton/Gerrard/Hudson
2003).
The remainder of the paper is organized as follows. Section 2 describes the
data set. We present the regression methodology and results in Section 3.
The last section concludes.
2 The data set
The analysis is perform on the Danish football club Brndby IF, during the
period 2nd of March 2009 { 6th of November 2011. Match results and odds for
calculating expectations were kindly provided by Danske Spil,1 while stock
market data for Brndby IF B and the OMXC smallcap index were collected
from Euroinvestor.2
{ Insert Table 1 here {
Table 1 highlights that Brndby IF performed quite well in the Danish
national league, with three 3rd places in the last three seasons. How-
ever, currently the team is underperforming and ranked 10th. Through
their top three nishes, Brndby IF played qualication matches for
UEFA Europa League, but failed to qualify in each and every season.
1www.Danskespil.dk
2www.Euroinvestor.dk2
Additionally, the team performed relatively poorly in the Danish Cup com-
petition. Thus, we only have a few observations for this competition. All
in all, we have 119 observations, 50 of these are wins, 31 draws, and 38 losses.
The expectations for each match outcome are calculated using the odds given
by Danske Spil. Since Danske Spil continuously adjusts their odds, we choose
those odds given just before game started, since these represent the true
expectations given injuries, change in line-up's etc. For each observation
we calculated the percentage change in the Brndby IF B stock price, on
the following trading day, as well as the percentage change in the OMCX
Smallcap stock index.
3 Hypotheses and empirical results
We test the following hypotheses:
 H1: A won match should inuence stock returns positively.
 H2: A won game in the European competition will inuence stock
returns to a larger extend than a game won in the national competition.
 H3: An unexpected win should aect stock returns stronger than an
expected win.
To test these hypotheses we set up four dierent models. Model 1 is given
by:
BIFt = 0 + 1Scapt + "t; (1)
where BIFt denotes the percentage change in the stock price of Brndby
IF and Scapt denotes the percentage change in the OMCX Smallcap
index. Scap is used to control for changes in the stock price due to market
wide trends.
The news model states that only the unexpected part of an information
drives stock market prices. We used betting odd information to disentangle3
the expected from the unexpected part (see Stadtmann 2006 for a detailed
description of the methodology applied). Our testing procedure is in line
with Dobson/Goddard (2001, p. 388): In a rst step, we include variables
that measure the actual match outcome (numbers of points gained) in each
match for every competition. In a second step, we include additionally
a variable that measures the expected match outcome. In case that only
the unexpected part of the match outcome has an impact on share prices,
the coecient on the actual performance should be the negative of the
coecient on expected performance. If this condition is met, it is justied to
combine the information of actual performance and expected performance in
a single measure 'unexpected performance' or as we call it 'expectation error'.
Model 2 tests whether the actual match outcome has a signicant eect on
the stock price. Superpointt and Europointt is given as the actual number
of points acquired in a match, hence a win gives a value of 3, a draw gives a
value of 1 and a loss is equivalent of the value of 0. Pokalwint is a dummy,
which obtains the value 1, whenever the match played was in the Danish
Cup competition and the outcome was a Brndby win.
BIFt = 0 + 1Scapt + 2Superpointt (2)
+5Europointt + 8Pokalwint + "t
Model 3 introduces the variables Superexpectedt and Euroexpectedt which
represent the expected number of points acquired in a match. Hence, this
model tests how unexpected information drives stock prices.
BIFt = 0 + 1Scapt + 2Superpointt
+3Superexpectedt + 5Europointt (3)
+6Euroexpectedt + 8Pokalwint + "t
In Model 4 we introduce the expectation error variables.
BIFt = 0 + 1Scapt + 4Supererrort (4)
+7Euroerrort + 8Pokalwint + "t4
The results from the four regressions are summarized in Table 2.
{ Insert Table 2 here {
Model 1 shows that there exists a positive relation between the percentage
change in the stock index and the percentage change in the stock price
of Brndby IF. A one percentage increase in the OMCX Smallcap index
increases the Brndby IF stock price by 1.15 percent.
Model 2 reveals positive coecients on all of the independent variables
which imply that hypothesis H1 can not be rejected. However, Model
2 also shows that there is no signicant dierence between national and
European matches, which contradicts hypothesis H2.3 The coecient
related to the Danish cup competition is insignicant, which might be
due to the small number of tournament observations. Model 2 explains
16.69% of the variation in the stock price of Brndby IF B. The strong
increase of the adjusted R2 compared to Model 1 reveals, that company
specic information is the main driver of the stock price. The goodness-of-
t is in line with such kind of stock market studies (Stadtmann, 2006, p. 496).
Model 3 is used to examine whether the variables actual number of points
scored (point) and the expected number of points scored (expected) can be
aggregated in a variable that measures the expectation error. The estimated
2- and 5-coecients remain positive. The coecients (3 and 6) of the
variables, Superexpected and Euroexpected in contrast are negative. A
test on the hypothesis that ^ 2 =  ^ 3 reveals that there is no signicant
dierence between these two coecients. A similar result is obtained when
3Test of beta coecients:
H0 : ^ 2 = ^ 5
Ha : ^ 2 6= ^ 5
Probability F-test : (1;114) = 0:67105
testing the hypothesis that ^ 5 =  ^ 6.4
As a consequence, it is justied to construct expectation error variables as
the dierence between the actual number of points scored and the expected
number of points. Model 4 supports the hypotheses H2 and H3. Both coef-
cients of the error variables are positive and signicant. This implies that
an unexpected point gained will result in a positive percentage change in the
stock price of Brndby, which is in line with hypothesis H3. In addition, we
nd that an unexpected point gained in a UEFA Europa League cup game
increases the stock price of Brndby IF approximately twice as much as an
unexpected point gained in the national league, which supports hypothesis
H2. However, this dierence is not signicant in statistical terms.
4 Conclusion
We test the news model of asset price determination and nd strong evidence,
that new { company specic { information is the main driver of the stock
price. By using bedding odd information, we are able to disentangle the
expected from the unexpected part of an information. We are able to show,
that only the unexpected part drives the stock price. The overall results
support the hypothesis of market eciency in its semi-strong form.
4Results of hypothesis tests:
H0 : ^ 2 =  ^ 3 H0 : ^ 5 =  ^ 6
Ha : ^ 2 6=  ^ 3 Ha : ^ 5 6=  ^ 6
Probability F-test : (1;112) = 0:2605 Probability F-test : (1;112) = 0:52386
References
Ashton, J.K., B. Gerrard, R. Hudson (2003): Economic impact of national
sporting success: evidence from the London stock exchange, Applied
Economics Letters, Vol. 10, pp. 783 { 785.
Brown, Gregory W. and Hartzell, Jay C. (2001): Market reaction to public
information: The atypical case of the Boston Celtics, in: Journal of
Financial Economics, Vol. 60, pp. 333 { 370.
Dobson, Stephen and Goddard, John (2001): The Economics of Football,
Cambridge University Press.
Fama, E.F. (1970): Ecient Capital Markets: A Review of Theory and
Empirical Work, in: The Journal of Finance, Vol. 25, pp. 383 { 417.
Stadtmann, Georg (2006): Frequent News and Pure Signals { The Case of a
Publicly Traded Football Club, Scottish Journal of Political Economy,
Vol. 53(4), pp. 485 { 504.7
Tables
Table 1: Overview of Brndby IF's performance during the period 2009 {
2011
Season Danish national league UEFA Europa league Danish Cup competition
"Superligaen" "DBU-Pokalen"
2008/2009* Ranked 3rd at the end of
season
Knocked out in the 4th
round of qualication.
Knocked out in the semi-
nal against AaB.
2009/2010 Ranked 3rd at the end of
season
Knocked out in the 3rd
round of qualication
against Hertha Berlin.
Knocked out in the 1/8
nal against Vejle BK.
2010/2011 Ranked 3rd at the end of
season
Knocked out in the 3rd
round of qualication
against Sporting CP.
Knocked out in their rst
game against Varde IF
(Third round).
2011/2012 Currently ranked 10th Knocked out in the 1st
round of qualication
against SV Reid.
Knocked out in the
1/8 nal against F.C.
Kbenhavn.
Summary 98 Matches: 41 Wins, 26
Draws, 31 Losses
14 Matches: 7 Wins, 3
Draws, 4 Losses
7 Matches: 2 Wins, 2
Draws, 3 Losses
*The data only includes matches from 2nd of March 2009 { This excludes all UEFA Europa League matches
in this season, and the rst three matches in DBU-Pokalen. These matches are therefore not considered in
the analysis.8
Table 2: Regression results
Model 1 Model 2 Model 3 Model 4
0 Constant -0.004
(-1.19)
-0.0181***
(-3.79)
-0.0134
(-1.33)
-0.0029
(-0.90)
1 Scap 1.1521**
(2.51)
1.1534***
(2.60)
1.1645***
(2.64)
1.1612***
(2.65)
2 Superpoint { 0.0091***
(3.67)
0.0087***
(3.11)
{
3 Superexpected { { -0.0019***
(-0.29)
{
4 Supererror { { { 0.0086***
(3.10)
5 Europoint { 0.0111***
(2.42)
0.0198***
(2.80)
{
6 Euroexpected { { -0.0152
(-1.60)
{
7 Euroerror { { { 0.0194***
(2.78)
8 Pokalwin { 0.0251
(1.00)
0.0205
(0.77)
0.0099
(0.40)
Observations 119 119 119 119
R2 0.0511 0.1669 0.1870 0.1775
Adjusted R2 0.0430 0.1377 0.1435 0.1484
Probability F-test F(1,117)
=0.0134
F(4,114)
=0.0003
F(6,112)
=0.0006
F(4,114)
=0.0002
*Signicant at 10% level, **Signicant at 5% level, ***Signicant at 1% level. t-values in parantheses.

Ecient Capital Markets: A Review of Theory and Empirical Work, in:

Economic impact of national sporting success: evidence from the London stock exchange,

Frequent News and Pure Signals { The Case of a Publicly Traded Football Club,

Market reaction to public information: The atypical case of the Boston Celtics, in:

The Economics of Football,

The news model of asset price determination: An empirical examination of the Danish football club Brøndby IF

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The news model of asset price determination: An empirical examination of the Danish football club Brøndby IF

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