Rivalry in the Football Industry and Its Impact on the Stock Prices of Listed Football Clubs Master Thesis Finance

Rivalry in the football industry and its impact on the stock prices of listed football clubs Master Thesis Finance

W.J.Tankink

Supervisor: J.H. von Eije

Groningen, January 11, 2018 University of Groningen Faculty of Economics and Business MSc Finance Words: 9381 Abstract Rivalry in the football industry is examined in this paper as it analyses rivalry effects on stock price performance of football clubs. It does not matter which sport is exercised and at what level, rivalry among clubs is one of the main sources of attractiveness of a league. The rivalry among football clubs leads to a positive “mood” in case the rival loses and leads to a negative “mood” in case the rival has a for them positive match result. It is hypothesised that these emotions due to the performance of the period rival affect the stock price performance of the focal football club. This study shows that there is evidence that the results of the period rival can have an impact on the investment decisions of club supporters.

Keywords: Rivalry, Period Rivalry, Football clubs, European Football, Stock performance

1. Introduction Football, or what they call it in the United States soccer, is a well-known sport exercised all around the world. It all started in 1887. From then on it was allowed by the Fédération Internationale de Football Association (FIFA) to recruit football players as an individual football club. This was the start of, what is familiar to us now as, professional football. In other words, money made its entrance here and the role of money has increased a lot since then (Dobson and Goddard, 1998). One of the largest causes for this significant role of money is the Bosman arrest, also known as the Bosman Ruling. Simply stated, after the expiration of a European football players contract, the player is free to move from his previous club to another club within the European Union. As a consequence of different regulations within national competitions, in combination with the large differences in budgets, Kesenne (2007) states that the gap between the “rich” and “poor” countries has clearly widened, budget-wise as well as performance-wise. In the world of football, four nations are considered to be the “Big four”, in terms of money. These countries are: England, Germany, Italy, and Spain. Other causes for the increasing role of money are the enormous increases in media contracts and sponsoring contracts, which is one of the main sources of income in the football industry. The other sources are merchandising income and income from match receipts (Scholtens and Peenstra, 2009). One clear example for such enormous media contracts is the Skysports contract for the English Premier League. Skysports has the right to broadcast the English Premier League matches for which it paid 7 billion euros, for a period of 3 years (2016 till 2019). As a consequence of these media contracts and sponsoring contracts, the football industry attracted relatively recently also the interest of large investors (Demir and Rigoni, 2017), by which the football industry eventually resulted in a stand-alone sector (Ecer and Boyukaslan, 2014). Eventually, this increase in interest in the football industry, led to football clubs going to the stock exchange. Mitchell and Stewart (2007) mention the enormous competition within the football sector as another reason for individual football clubs to move to the stock exchange. As it was the English Premier League that had the prime to be the first football competition considered to be professional, it was also an British football team that was the first that had a listing on a stock exchange. London based Tottenham Hotspur was the first, in 1983, to get a listing on the London Stock Exchange. Multiple other football clubs followed Tottenham Hotspur and became listed in the following years (Dobson and Goddard, 2001). Due to the uniqueness of the football industry, it is valuable and interesting for investors and share traders

3 to understand what the drivers are behind the fluctuations in share prices for listed football clubs. Several studies have examined how stock prices are affected by the results and performance of football clubs (Demir & Danis, 2011; Scholtens & Peenstra, 2009; Stadtmann, 2006). These studies purely examine what the effect of the match outcome is on the stock prices of that particular football club. Other studies find that stirred up emotions due to football have also an impact on the stock prices of football clubs (Demir and Rigoni, 2017; Edmans, Garcia, and Nørli, 2007; Palomino et al., 2009). Based on the findings in the literature, there are two ways a match outcome affects the stock price of a particular team. The match outcome itself and the mood of the fans resulting from the match outcome. Palomino et al. (2009) is one of the few studies that took both of these effects into consideration. They, however, only examined the effects in isolation of each other. Only one recent study took the two match outcome effects out their isolation and examined their combined effect on the stock price of a particular football club (Demir and Rigoni, 2017). This combined effect is examined by studying the effect of rivalry in the football industry on the stock price of a listed football club. This rivalry is represented by a focal team and its rival. The listed football club for which the presence of rivalry in its stock price is measured, is called the focal team. The combined effect of the performance of the focal team as well as the effect of its rival’s performance represents this rivalry effect. The rival team is the football club which is most feared by the focal team. In this study this combined effect on the stock price of a particular football club is examined here as well. This combined effect is called “Schadenfreude” by Demir and Rigoni (2017). This “Schadenfreude” effect in football can be observed in economic terms. Because all European competitions consist of playing rounds, all teams have one opponent every round. Therefore, all matches and performances can be observed by all investors. In that case, the share price of a team could be the result of both the performance of the team as well as that of the rival team. However, every football club in a particular national competition is somehow a rival of each other. The question is, which one is the most important one for the stock price performance of listed football clubs. Therefore, this study firstly defines a rival based on historical performance. This results in the first contribution to the literature and football industry. Each competitor of the focal team in its national competition is given a rivalry score. The competitor who scores the highest rivalry score is considered to be the rival. No other study defines a rival like this. Demir and Rigoni (2017) consider AS Roma and Lazio Roma to be rivals on basis of politics, for example. The Bosman Ruling causes the composition of football

4 club teams to differ (a lot) from year to year. Therefore is the rivalry score based on only two years of performance, causing the examined rivals to be “period rivals”. Secondly, the study that examined the rivalry effect on stock prices in the football industry (Demir and Rigoni, 2017) focuses on the rivalry between AS Roma and Lazio Roma. Here the focus is on multiple European rivalries. All football clubs have their centuries old “rival”, based on multiple and varied reasons. Some rivalries are based on their final position in the league. For example, both battling for the championship trophy, year in year out. Others are based on political reasons, or from both coming from the same city, or because of religious reasons. Many reasons thus exist for supporters of “their” team to consider a particular other team to be their arch-rival. The question arises, however, if the right football club is considered to be the arch-rival in terms of stock price affection. As a consequence, I will try to answer the following research question:

Is period rivalry in the football industry present in the stock prices of listed football clubs?

The following section will discuss the literature that can be related to this study and it presents hypotheses. In the subsequent section, “Data collection”, the required data to answer this research question are described and it is explained how and where these data are collected. Then, “Methodology” section describes how I test the hypotheses. In the “Results” section, the results following from the hypothesis testing are provided and analysed. Finally, in the “Conclusion” section, the research is concluded, limitations and suggestions for future research are given.

2. Literature Review

Stadtmann (2006) studied how and if subsequent changes in Borussia Dortmund’s stock price could be explained by new information resulting from sportive success. He concludes that investors react to match results resulting in a negative effect on the share price in case the considered team loses and resulting in a positive effect on the share price in case the considered team wins. Panagiotis (2011) finds that the profitability of football clubs in the Greek Football League is positively related to football clubs’ sportive performance. Besides, Samagaio, Couto, and Caiado (2009) find a strong correlation between financial and sporting factor scores. Subsequently, Scholtens and Peenstra (2009) find a significantly positive(negative) stock market response to a victory(defeat). Also Demir and Danis (2011) find that abnormal returns

5 of listed Turkish football clubs are affected by their match results. These studies however purely examine what the effect of the match outcome will be on the stock prices of that particular football club. Edmans et al. (2007) find that after football losses the market significantly declines attribute it to sudden changes in fan investor mood due to football outcomes. In addition, Palomino et al. (2009) find that stock prices are influenced by the mood of investors which resulted from match results, especially a positive abnormal return related to a win. Based on these findings in the literature, there are two ways a match outcome affects the stock price of a particular team. The match outcome itself and the mood of the fan investors resulting from the match outcome. Palomino et al. (2009) is one of the few studies that took both these effects into consideration. These effects are, however, only examined in isolation of each other. Only one recent study took these two match outcome effects on the stock price of a particular football club (Demir and Rigoni, 2017). According to Edmans et al. (2007) it is rationalized to study the link between a mood variable and stock returns, in case the variable satisfies the following three key characteristics:

 A large proportion of the population’s mood should be affected by the variable French and Poterba (1991) studied the relationship between the individuals affected and the investments in the domestic stock market. They found that these individuals are also the ones that invest in a marginal way in the domestic stock market. In other words, the international football matches are perceived to be important to a large fraction of the population in multiple countries, while these matches take place at regular intervals. This characteristic is even more strengthened by Boyle and Walter (2003). They state that in general, affiliation for a football club is assumed to be more important than national identity.  Mood must be driven in a substantial and unambiguous way by the variable Carroll et al. (2002) studied the change to have a heart attack during the World Cup tournament in 1998 between England and Argentina. They found a 25% increase in admissions for heart attacks in the 3-day period after the World Cup final, in which England lost to Argentina in a penalty shoot-out.  The effect of the mood-variable on the stock returns should be correlated across the majority of events within an examined football club. Edmans et al. (2007) conclude that for many countries football is a “national interest”, based on media coverage, TV viewing figures and merchandise sales. For example in

countries such as Greece, Italy, Portugal, and Spain, newspapers that are exclusively dedicated to sports, in particular football, are best-selling newspapers.

These two ways of how a match outcome affects the stock price of a football club in combination is defined as rivalry. Rivalry is an important driver behind the fluctuations in share prices for listed football clubs. One message of the study by Mason (1999) is that it does not matter at what level football is exercised, rivalry among clubs is one of the main sources of attractiveness of a league. Still, only a limited number of researchers have examined rivalry in football, or in sports in general. For example Leach et al. (2003) found that the football fans of the Dutch national team were very happy in the World Cup tournament of 1998, at which the German national team lost against Croatia. While Germany and Holland were both placed in another grouping of teams, and the Germans were eliminated earlier than the Dutch. In General, the Dutch football fans regard the Germans as their arch-rival, by which the results of Leach et al. (2003) indicate that a disappointment of a rival team is psychologically more beneficial in case the interest in sports become larger. Stadtmann (2006) found a similar argument. He studied the effect of unexpected positive results of Bayern Munich on the share price of Borussia Dortmund. As these clubs are rivals of each other in terms of the championship trophy in the German Bundeliga. Stadtmann (2006) found that a (unexpected) success of Bayern Munich results in a negative effect on Borussia Dortmund’s share price. As indicated only one recent study examined the rival team effect on the share price (Demir and Rigoni, 2017). The findings by Demir and Rigoni (2017) result in question marks. They find partial evidence for their hypothesis. In case the focal team has lost, the negative effect on the share price due to this loss is strengthened by winning result of the rival team. They, however, find no reaction by investors for the case: focal team wins, rival team loses. This is probably due to the negative effect resulting from a loss of the focal team is counterbalanced by the positive effect resulting from a loss of the rival team. The rival effect does not hold in case the focal team wins. They found that it does not matter what the rival team does in case the focal team wins. The positive effect on the share price due to the focal team winning, is not counterbalanced by a win of the rival team or strengthened by a loss of the rival team. Reasons for their partial evidence is that it could be the case that the results of another Italian football club is considered to be more relevant for the fan investors. Demir and Rigoni (2017) for example state that the term “Schadenfreude” in football is mostly related to the most prominent rivalries. However they also state that their methodology is not only applicable to

7 those rivalries. Schadenfreude is namely described as some kind of mood resulting from the performance of a particular team that is considered to be important for an investor. Demir and Rigoni (2017) examined only one single rivalry. The rivalry between Lazio Roma and AS Roma, two clubs listed on the Italian stock exchange. However, many more rivalries exist between football clubs. A highly ranked rivalry is the rivalry between Fenerbahçe and Galatasaray (www.footballderbies.com). This rivalry is called the “Kitalar Arasi Derbi”, which is Turkish for “Intercontinental Derby”. This rivalry has its name due to Fenerbahçe coming from the Asian part of the capital Istanbul and Galatasaray coming from the European part of the capital Istanbul. Other highly ranked rivalries, according to www.footballderbies.com are; “El SuperClasico” between Boca Juniors and River Plate (both football clubs coming from the Argentina capital Buenos Aires); “The Old Firm” between Celtic and Glasgow Rangers (both football clubs coming from the Scottish capital Glasgow); and “El Clasico” between Barcelona and Real Madrid (both coming from Spain, and battling for the Championship trophy in the Primera Division). The expectation here is that the performance of the rival in terms of sportive success is also considered to be relevant to football investors. Thus, the positive mood impact of the fan investors which resulted from a win should increase in case the period rival lost. Contrary to this, the negative mood impact of the fan investors which resulted from a loss should increase in case the period rival won.

Hypothesis 1: If both the focal team and the period rival team win their match the share price of the focal team is positively influenced.

Hypothesis 2: If the focal team wins their match and the period rival team loses their match the share price of the focal team is strong positively influenced.

Hypothesis 3: If the focal team loses their match and the period rival team win their match the share price of the focal team is strong negatively influenced.

Hypothesis 4: If both the focal team and the period rival team lose their match the share price of the focal team is negatively influenced.

The difference between hypothesis 1 and hypothesis 2 is the word “strong”. This is the same for hypothesis 3 and hypothesis 4 which also differ from each other by the word “strong”. The

8 next two hypotheses, hypothesis 5 and hypothesis 6, are testing this word “strong” more explicitely. More specifically, hypothesis 5 is testing the difference between hypothesis 1 and hypothesis 2, and hypothesis 6 is testing the difference between hypothesis 3 and hypothesis 4.

Hypothesis 5: In case the focal team wins and the period rival loses the share price of the focal team is more positively influenced than when the period rival wins as well.

Hypothesis 6: In case the focal team loses and the period rival wins the share price of the focal team is more negatively influenced than when the period rival loses as well.

3. Data Collection As the subject of this research is the effect of period rivalry in the football industry on stock price performance, it is necessary to identify the football clubs that have a listing on a stock exchange. Such a list is provided by the Stoxx Football Index (Appendix A). According to this Stoxx Football Index list there exist 22 European football clubs that have a listing on a stock exchange. Besides this European listing, the football club Manchester United is listed as well, however, they are listed on the New York Stock Exchange. That is why this football club is not included in the Stoxx Football Index. This research will, therefore, exclude the football club Manchester United from this examination. According to Demir and Rigoni (2017) the Schadenfreude effect is most related to the most prominent rivalries. Based on this I have decided to examine only the football clubs coming from the largest football countries in Europe. Appendix B gives a list of the European football countries that are considered to be the best performing European football countries. Based on this UEFA rankings list (Appendix B) and the football clubs that have a listing on the stock exchange, I came up with the following relevant listed football clubs to include in this research:

Table 1 – Listed football clubs and their listing characteristics Football Club Stock Code Country Stock Exchange AS Roma BIT: ASR Italy Italy Stock Market (FTSE MIB) Besiktas IST: BJKAS Turkey Turkey Stock Market (XU100) – BIST 100 Borussia Dortmund ETR: BVB Germany German Stock Market (SDAX) FC Porto ELI: FCP Portugal Portugal Stock Market (PSI All Share Index) Fenerbahçe IST: FENER Turkey Turkey Stock Market (XU100) – BIST 100 Galatasaray IST: GSRAY Turkey Turkey Stock Market (XU100) – BIST 100 Juventus BIT: JUVE Italy Italy Stock Market (FTSE MIB) Lazio Roma BIT: SSL Italy Italy Stock Market (FTSE MIB) SL Benfica ELI: SLBEN Portugal Portugal Stock Market (PSI20) Sporting CP ELI: SCP Portugal Portugal Stock Market (PSI20) Trabzonspor IST: TSPOR Turkey Turkey Stock Market (XU100) – BIST 100

The clubs presented in table 1 are all listed on their domestic stock exchange. The Italian football clubs: AS Roma, Juventus, and Lazio Roma have a listing on the Italian Stock Exchange, the Turkish football clubs: Besiktas, Fenerbahce, Galatasaray, and Trabzonspor have a listing on the Turkish Stock Exchange, the Portuguese football clubs: FC Porto, SL Benfica, and Sporting CP have a listing on the Portuguese Stock Exchange, and Borussia Dortmund has a listing on the German Stock Exchange. Except for the Portuguese football clubs, the focus of this research, to test the hypotheses, is on the seasons 2011/2012 till 2016/2017, as all football clubs have a listing from 2010 onwards. The focus for the Portuguese football clubs is on the seasons 2013/2014 till 2016/2017 as the market data for the PSI All-Share Index is only available from October 15 2012. To test the hypotheses for those clubs, the period rivals of these clubs have to be defined. The period rivals will be based on the mutual struggle between the different football clubs given in table 1 and their rivals from the past. These rivals all get a rivalry score based on the mutual matches between a particular team from the national competition and the considered football club. The precise way to come to this rivalry score and the underlying thought is explained in the “Methodology” section. These mutual struggles of the considered football club and its rivals is received from nl.soccerway.com. Table 2 gives the period rivalries for all considered clubs based on a two-season period:

Table 2 – Rivals based on rivalry score Football Club Two-season period Period Rival

2011/2012 Juventus 2012/2013 2013/2014 AS Roma (Ita) Juventus 2014/2015 2015/2016 Atalanta 2016/2017 2011/2012 Fenerbahce 2012/2013 2013/2014 Besiktas (Tur) Galatasaray 2014/2015 2015/2016 Istanbul Basaksehir 2016/2017 2011/2012 Schalke 04 2012/2013 2013/2014 Borussia Dortmund (Ger) Borussia Mönchengladbach 2014/2015 2015/2016 Bayern München 2016/2017 2011/2012 SL Benfica 2012/2013 2013/2014 FC Porto (Por) SL Benfica 2014/2015 2015/2016 Sporting Lisbon 2016/2017 2011/2012 Karabukspor 2012/2013 2013/2014 Fenerbahce (Tur) Akhisar Belediyespor 2014/2015 2015/2016 Antalyaspor 2016/2017 2011/2012 Gaziantepspor 2012/2013 2013/2014 Galatasaray (Tur) Kasimpasa 2014/2015 2015/2016 Istanbul Basaksehir 2016/2017 2011/2012 AC Milan 2012/2013 2013/2014 Juventus (Ita) Fiorentina 2014/2015 2015/2016 Palermo 2016/2017 2011/2012 Genoa Lazio Roma (Ita) 2012/2013 2013/2014 Internazionale

2014/2015 2015/2016 Napoli 2016/2017 2011/2012 FC Porto 2012/2013 2013/2014 SL Benfica (Por) Sporting Lisbon 2014/2015 2015/2016 FC Porto 2016/2017 2011/2012 Maritimo Funchal 2012/2013 2013/2014 Sporting Lisbon (Por) FC Porto 2014/2015 2015/2016 SL Benfica 2016/2017 2011/2012 Fenerbahce 2012/2013 2013/2014 Trabzonspor (Tur) Besiktas 2014/2015 2015/2016 Fenerbahce 2016/2017

www.soccerway.com provided all the match information (such as the goals scored, the minutes the goals were scored, and the results) that was needed to test the hypotheses. Other data that is needed here are market indices, share prices, alphas, and betas that are collected from Datastream at the RUG University Library. Due to the available information and the criteria to which this research is subject to, this database consists of 4200 matches that are analysed for the hypothesis testing which are spread over 11 different football clubs.

4. Methodology 4.1 Rivalry Score Multiple factors could be considered as important in order to consider a particular football club as the “arch-rival” of your focal team. Such as, city pride (Guschwan, 2007) or political beliefs (Demir and Rigoni, 2017) etc. However, the question that arises here is whether the considered “arch-rival” is actually the rival in economic terms. Therefore, a rivalry score is given to the competitors of the examined teams. The frequency in transfers increases year by year causing recently larger differences between the selection of one year and the year after of a particular football club. Therefore, it would not be justified to base the rivalry score on the mutual struggle of two teams over a period of a long period of time. Therefore, the period rival teams (given in table 2) are based on a 2- year period, which results in three measurements for rival teams over the available period. The

12 total lists of rivalry scores per examined team are given in Appendix C. The rival teams that get a rivalry score are only the teams that played the examined team two years in a row. The rivalry score consists of multiple scores. First of all, the match result could be a win, draw or loss for a particular team. In general this results in three points, one point, or no points respectively. Therefore the score a team is given for match result is thus three points, one point, or no points in case of a win, draw, or loss respectively. Another score that is given to a rival is the goal difference. Because in simple terms, the difference between the goals scored is representative for the ease of the match. However, a 2-0 match result is not the other 2-0 match result. To give an example: it could be that a team scores the 1-0 in the 3’ minute and the 2-0 in the 15’. Based on the 2-0 goal difference early in the match the winning team completes the match at his dead ease. The other case could be that the one team scores the 1-0 in the 68’ minute and the final 2-0 in the 90’+ 2’ minute. This second example indicates that the 2-0 win was much more difficult than the first 2-0 win example. Therefore the percentage of time that still has to be completed after scoring the goal is added as well to the rivalry score. This results in the following equation:

푟𝑖푣푎푙푟푦 푠푐표푟푒 = 푚푎푡푐ℎ 푟푒푠푢푙푡 + 푔표푎푙 푑𝑖푓푓푒푟푒푛푐푒 + 푝푙푎푦𝑖푛푔 푡𝑖푚푒 푠푐표푟𝑖푛푔 (1)

Match result is a score of 0, 1, or 3 points. In case one of the focal teams loses, the “match result” is 3 points, in case of a draw 1 point and in case of a win 0 points. Goal difference is the differences between the goals the focal team scored and the rival team scored. In case one of the focal teams loses the “goal difference” is a positive number, in case of a draw this variable is 0 and in case of a win the “goal difference” is a negative number. Playing time scoring is the percentage of playing time that still have to be completed after the goal is scored. In case one of the examined teams loses, the “playing time scoring” is a positive number, in case of a draw, the sign of the score depends on who scored first, and in case of a win the “playing time scoring” is a negative number. For your understanding, hereafter follows a numerical example. One of the examined football clubs is AS Roma, coming from Italy. This football club is represented as example.

Rivalry Score: Juventus

“Match Result” = 3 “Goal Difference” = 1 “Playing time scoring” = 0.844 ((90-14)/90)

Rivaly Score = 4.844

Rivalry Score: Juventus

“Match Result” = 0 “Goal Difference” = -1 “Playing time scoring” = -0,411 ((90-87)/90) - ((90-61)/90) - ((90-79)/90)

Rivaly Score = -1.411

Rivalry Score: Juventus

“Match Result” = 1 “Goal Difference” = 0 “Playing time scoring” = 0.156 ((90-64)/90) - ((90-78)/90)

Rivaly Score = 1.156

Rivalry Score: Juventus

“Match Result” = 3 “Goal Difference” = 1 “Playing time scoring” = 0.144 ((90-77)/90)

Rivaly Score = 4.144

The period rivals given in table 2 are the result of the calculation given above. These period rivals are included in the hypothesis testing, which is describe in detail hereafter.

4.2 Hypothesis Testing As described in the previous sections the period rivalry effect in the football industry is examined by using the effect of match results of the focal team in combination with the match results of the period rival team on the stock price of the focal team. As these match results are a specific result on a particular point in time, these match results could be considered as events. The literature in Finance, especially about stock prices, is familiar with event studies as a stock price is present for each day and innumerable factors could have an influence on the stock price for a particular company or business. According to Becker and Suls (1983) the performance of a football club increases attendance rate of that football club, and thus the stock price should increase. Sponsoring contracts are also positively influenced by increasing sportive performance (Ngan et al., 2011). Lucifora and Simmons (2001) state that merchandise income is increased due to increasing positive sporting performance in the football industry. In summary, the main sources of income for a professional football club (Scholtens and Peenstra, 2009) are directly positively affected by the performance of that professional football club. However, stock prices are influenced by innumerable factors. Therefore the event study methodology is used as the effect of a specific event on the stock market is tested by which match outcome is the event which could be a win, draw or loss. Besides, the effects that are tested here are the sudden mood changes of investors due to the performance of the focal team as well as the period rival team and according to Edmans et al. (2007) this is clearly identified by an event approach. In order to capture the effect of an event on the stock prices, the return that could not be explained by the market should be filtered out. This part is called the abnormal return in the Finance literature. This abnormal return is the difference between actual return of a particular stock and the part that can be explained by the market, which results in the following equation:

퐴푅푖푡 = 푅푖푡 − 푅̂푖푡 (2)

퐴푅푖푡 is the abnormal return at time t for stock i, by which the normal estimated return for a particular football club i at time t (푅̂푖푡) is given by the formula:

푅̂푖푡 = 푎푖 + 훽푖푅푚푖푡 + 휀푖푡 (3)

This is the market model. 푎푖 is the alpha of stock i, 훽푖 is the stock prices’ sensitivity to the market return, also defined as the beta of stock i, and 푅푚푖푡 is the market return of stock i at time t. The market return (푅푚푖푡) at day t is the market index in which the particular stock is included. The Italian football club, AS Roma, Lazio Roma, and Juventus are all included in the FTSE

Italia All Share Index. Therefore this market index is used for the Italian football clubs. Sporting CP, Porto FC, and SL Benfica are all included in the PSI All Share Index. Therefore this market index is used for the Portuguese football clubs. The Turkish football clubs, Fenerbahçe, Besiktas, Trabzonspor, and Galatasaray are all included in the BIST 100 index. Therefore this market index is used for the Turkish football clubs. Finally, Borussia Dortmund is included in the Small-cap Deutscher Aktieindex. Therefore this market index is used for the German football club. The first trading day after the match was played is used to calculate the abnormal returns. Football clubs play their matches frequently, sometimes with only 2/3 days between the matches, therefore the event period of a one-day window is used in order to prevent the effect of an overlapping game. Due to this frequency in matches played an estimation period of pre- event data cannot be used here to estimate the parameters in the market model which are used in the abnormal return equation. Multiple other studies (Demir & Rigoni, 2017; Palomino et al., 2009; Scholtens & Peenstra, 2009) have tackled this problem by using a whole sample period available as the estimation period. Therefore, an estimation period of a two season sample period is used here. ̂ Based on this two year sample period as estimation period, the alpha (푎̂푖) and beta (훽푖) are estimated, which are then used to calculate the abnormal return (퐴푅푖푡) at day t for each team (i) separately. Subsequently, an ordinary least square (OLS) regression is run to capture the effect of game results on abnormal returns. At first, the literature is verified by analysing what the effect of the match results of the focal team is on its own stock price. This effect is analysed by making use of the following OLS regression:

(4) 퐴푅푖푡 = 푎 + 훽1푊𝑖푛푖푡 + 훽2퐿표푠푠푖푡 + 휀푖푡

The 푊𝑖푛푖푡 and 퐿표푠푠푖푡 regression only measure the end result of the game by which the 푊𝑖푛푖푡 variable takes 1 in case the examined team wins and the 퐿표푠푠푖푡 variable takes 1 in case the examined team loses, in both cases the variable takes 0 otherwise. Furthermore a robustness check is run for this match result effect by using the goal difference score as variable instead of the dummy variables 푊𝑖푛푡 and 퐿표푠푠푡. This results in the following OLS regression:

퐴푅푖푡 = 푎 + 훽1퐺표푎푙 퐷𝑖푓푓푒푟푒푛푐푒푖푡 + 휀푖푡 (5)

The difference between those OLS regressions is the way they analyse the match result effect on the abnormal stock returns. Each abnormal return (퐴푅푖푡) is of the first trading day

16 after the game day. Both regressions tackle the match result effect of the focal team on the stock price of that examined team, however, the second OLS regressions which includes the

퐺표푎푙 퐷𝑖푓푓푒푟푒푛푐푒푖푡 variable includes the magnitude of a victory or defeat. The

퐺표푎푙 퐷𝑖푓푓푒푟푒푛푐푒푖푡 variable represents a win in case the variable has a positive value and represents a loss in case the variable has a negative value. A draw is represented by the number 0, as both teams scored the same amount of goals. Therefore the magnitude of the match result is considered here as well. In case with a higher difference, the 퐺표푎푙 푑𝑖푓푓푒푟푒푛푐푒푖푡 variable takes a higher value. This robustness test is examined because of investors probably reacting more strongly to a higher/lower goal margin. The two equations (4) and (5) are used for confirming or rejecting the literature, as these equations analyse what effect the performance of the focal team has on its own stock price. Thus, this part is a confirmation (rejection) of the literature. The next part makes a contribution to the literature, in particular to the finance research in the football industry with respect to rivalry as it is going to focus on period rivals in the European football industry. Similar to the research by Demir and Rigoni (2017) a dummy interaction variable method is used. Such that the combined performance of both the focal team as well as the period rival team is captured in the analysis. A similar equation is used as in the research by Demir and Rigoni (2017):

퐴푅푖푡 = 푎 + 훽1푊𝑖푛푊𝑖푛푖푡 + 훽2푊𝑖푛퐿표푠푠푖푡 + 훽3퐿표푠푠푊𝑖푛푖푡 + 훽4퐿표푠푠퐿표푠푠푖푡 + 휀푖푡 (6)

The variables WinWin, WinLoss, LossWin, and LossLoss are the dummy interaction variables. The variables are relatively simple to explain. In case both considered teams, focal team as well as the period rival team, wins, the WinWin variable takes a value of 1. Otherwise the value for this variable would be 0. In case both considered teams, focal team as well as the period rival team, loses, the LossLoss variable takes a value of 1. Otherwise the value for this variable would be 0. The other two opposite variables are the WinLoss and LossWin variables. If the focal team wins its match and the period rival team loses its match in the similar playing round, the WinLoss variable takes a value of 1. In case of the opposite event, where the focal team loses and the period rival team wins its match in the similar playing round, LossWin variable takes a value of 1. Furthermore a robustness check is run for this interaction effect by using the goal difference scores as variables instead of the 푊𝑖푛푡 and 퐿표푠푠푡 combination variables. This results in the following OLS regression:

퐴푅푖푡 = 푎 + 훽1푃퐺퐷/푃퐺퐷푖푡 + 훽2푃퐺퐷/푁퐺퐷푖푡 + 훽3푁퐺퐷/푃퐺퐷푖푡 + 훽4푁퐺퐷/푁퐺퐷푖푡 + 휀푖푡 (7)

PGD stands for positive goal difference and NGD stands negative goal difference. This means that the variable 푃퐺퐷/푃퐺퐷푡 takes a value larger than zero in case both the focal team as well as the period rival team wins and the goal difference for the focal team is larger positive than the goal margin for the period rival team. Actually, the PGD of the period rival team is the opposite of the PGD of the focal team. To give an example, in case the focal team wins 2-0, the PGD score for this team is 2. In case the period rival team wins 1-0, the PGD score for this team is -1. Resulting in an overall 푃퐺퐷/푃퐺퐷푡 variable score of 1 for this example. If one of the two considered teams do not win its match, this 푃퐺퐷/푃퐺퐷푡 variable takes a value of 0. In case the focal team wins and the period rival team loses, the 푃퐺퐷/푁퐺퐷푡 variable takes a positive value due to a win of the focal team, resulting in a PGD score which is positive (+) and the period rival loses, resulting in a NGD score which is positive (+) as well. In all other cases, this variable

푃퐺퐷/푁퐺퐷푡 is 0. In case the focal team loses and the period rival team wins, the 푁퐺퐷/푃퐺퐷푡 variable takes a negative value due to a loss of the focal team, resulting in a NGD score which is negative (-) and the period rival team wins, resulting in a PGD score which is negative (-) as well. Again, in all other cases, this variable 푁퐺퐷/푃퐺퐷푡 is 0. The variable 푁퐺퐷/푁퐺퐷푡 takes a value higher than zero in case both the focal team as well as the period rival team loses and the goal difference for the focal team is less negative than the goal margin of the period rival team. Finally, to test hypotheses 5 and 6 a t-test is run between the averages of the four variables; WinWin, WinLoss, LossWin, and LossLoss. First regression (6) is analysed for all eleven football clubs. This regression presents a coefficient for the four aforementioned variables for all eleven football clubs. Such that there are eleven WinWin beta’s, eleven WinLoss beta’s etc. Based on these eleven beta’s for each variable, an average beta is given, resulting in an average beta for the WinWin variable, an average beta for the WinLoss variable, an average beta for the LossWin variable, and an average beta for the LossLoss variable. To test hypothesis 5, the average beta of the WinWin variable and the average beta of the WinLoss variable are compared by making use of a t-test. On basis of this t-test result, hypothesis 5 is rejected or not. The same method is used for testing hypothesis 6, however, the t-test representing this hypothesis is comparing the average beta’s of the LossWin variable and the LossLoss variable.

5. Results Before the results of the hypothesis testing are explained, to strengthen this hypothesis testing, the literature is confirmed or contradicted. The match result’s effect of the focal team

18 only is examined, to analyse this effect on the stock price of the focal team. Then the hypothesis testing is analysed. To have a clear and easy to read chapter, an example of how the regression results should be interpreted is given in this section. The remaining regression results and explanations are presented in Appendix D. The regression results are presented in statistical tables. These statistical tables contain several data. The alpha and the beta for each variable are given in these tables together with the significance of this variable. This significance level is indicated with *’s, by which * represents a statistical level of 10%, ** represents a 5% significance level and *** represents a 1% significance level.

The variables that are given in these statistical tables are the 푊𝑖푛푡, 퐿표푠푠푡 and

퐺표푎푙 퐷𝑖푓푓푒푟푒푛푐푒푡 to reject or not reject previous literature. The variables WinWin, WinLoss,

LossWin, and LossLoss are the dummy variables and the 푁퐺퐷/푃퐺퐷푡, 푁퐺퐷/푃퐺퐷푡, 푁퐺퐷/푃퐺퐷푡 and 푁퐺퐷/푃퐺퐷푡 variables are the other dummy interaction variables in order to test my hypothesis. Furthermore the standard errors are given in parentheses. The number of observations are given by “N”. Finally, the R² is given for each regression, which is an explanation of the particular regression. 5.1 AS Roma Testing the literature finds evidence for the way the market reacts to the performance of the considered football club. These results are given in table 3. The Win variable is statistically significant at a 1% significance level, which means that in case the focal team wins its stock return increases by 0.0256. The Loss variable is insignificant and thus in case of AS Roma, their stock price is not affected due to a loss of AS Roma. The Goal-Difference variable is statistically significant at a 1% level as well. This variable means that in case the goal difference for the focal team increases by 1, the return of this football club increases by 0.0060. The other variables are all insignificant at all significance levels.

Table 3 – Market Reaction (AS Roma) AR (1) AR (2) Constant -0.0158*** -0.0049** (0.000) (0.022) Win 0.0256*** (0.000) Loss 0.0055 (0.331) Goal- 0.0060*** Difference (0.000) N 228 228 R² 0.14 0.11

The way the market reacts to the performance of the period rival is also found evidence for. The WinWin variable as well as the WinLoss variable both are statistically significant at a 1% significance level. In case of the WinWin variable, if both the considered team as well as its period rival team wins the return of the considered team increases by 0.0167, thus hypothesis 1 is not rejected. In case the supported team wins and the period rival loses, the return of the supported team increases even more (0.0225). This information causes hypothesis 2 also not to be rejected. Both the LossWin and the LossLoss variable are insignificant at all significance levels, whereby hypothesis 3 as well as hypothesis 4 are rejected. In case of the robustness check, only the PGD/PGD variable is statistically significant at a 1% significance level, while the other variables are statistically insignificant at all significance levels. This variable states that in case the focal team as well as the period rival team wins, and thus the goal difference increases by 1, the return of the focal team increases by 0.0031. Therefore, hypothesis 1 is considered to be correct. The other three variables, PGD/NGD, NGD/PGD, and NGD/NGD, are not significant.

Table 4 – Rival Influence (AS Roma)

AR (1) AR (2) Constant -0.0077** -0.0042* (0.013) (0.085) WinWin 0.0167*** (0.000) WinLoss 0.0225*** (0.001) LossWin -0.0027 (0.649) LossLoss 0.0015 (0.921) PGD/PGD 0.0031*** (0.002) PGD/NGD 0.0024 (0.562) NGD/PGD -0.0014 (0.762) NGD/NGD 0.0012 (0.776) N 228 228 R² 0.10 0.05

The results for the other examined listed football clubs, which are presented in statistical tables, and the explanation of these results are presented in Appendix D. Table 5, hereunder, presents the hypotheses results for the eleven examined football clubs:

Table 5: Hypothesis Rejection/No Rejection

Rivalry Hypothesis 1 Hypothesis 2 Hypothesis 3 Hypothesis 4 AS Roma Not Rejected Not Rejected Rejected Rejected Besiktas Rejected Rejected Not Rejected Rejected Borussia Dortmund Not Rejected Rejected Not Rejected Not Rejected FC Porto Rejected Rejected Rejected Rejected Fenerbahce Rejected Not Rejected Rejected Rejected Galatasaray Rejected Rejected Rejected Rejected Juventus Rejected Rejected Not Rejected Rejected Lazio Roma Not Rejected Not Rejected Not Rejected Not Rejected SL Benfica Rejected Rejected Rejected Rejected Sporting Lisbon N/A N/A N/A N/A Trabzonspor Rejected Rejected Not Rejected Rejected Total Not Rejected: 3 3 5 2

Unfortunately the analysis for Sporting Lisbon gives a near singular matrix. The other football club analysis give the hypothesis conclusions which are presented in table 5. Hypothesis 1 and hypothesis 2 are not rejected three times out of ten. With regard to hypothesis 3, this hypothesis is not rejected 5 times out of 10. Finally hypothesis 4 is only not rejected two

21 times for these 10 football clubs. Resulting in little evidence found for the presence of the period rival’s performance in the stock market. 5.2 T-test In order to test hypothesis 5 a t-test is run between the average of the WinWin variable and the average of the WinLoss variable. Subsequently, a t-test is run between the average of the LossWin variable and the average of the LossLoss variable in order to test hypothesis 6. The following table presents the results for the t-tests: Table 6 – T-testing WinWin Probability WinLoss LossWin Probability LossLoss AS Roma 0.0167*** 0.0000 0.0225*** -0.0027 0.0000 0.0015 (0.000) (0.001) (0.649) (0.921) Besiktas 0.0002 0.0000 0.0059 -0.0196** 0.0023 0.0031 (0.974) (0.534) (0.034) (0.821) Borussia Dortmund 0.0096*** 0.0000 0.0062 -0.0092** 0.0002 -0.0295*** (0.002) (0.126) (0.032) (0.000) FC Porto 0.0185 0.0000 -0.0220 -0.0137 0.0031 -0.0124 (0.127) (0.350) (0.493) (0.778) Fenerbahce 0.0052 0.0038 0.0095* -0.0098 0.0003 -0.0052 (0.340) (0.051) (0.157) (0.687) Galatasaray 0.0049 0.0001 0.0038 0.0053 0.0123 -0.1390 (0.397) (0.582) (0.554) (0.296) Juventus -0.0187** 0.4607 -0.0104 -0.0327* 0.3340 -0.0096 (0.055) (0.293) (0.098) (0.678) Lazio Roma 0.0117** 0.0010 0.0136** -0.0183*** 0.0326 -0.0178** (0.045) (0.049) (0.008) (0.032) SL Benfica -0.0004 0.0000 0.0234 -0.0021 0.0000 -0.0304 (0.978) (0.311) (0.868) (0.219) Sporting CP ------Trabzonspor 0.0047 0.0000 0.0058 -0.0182*** 0.0000 -0.0042 (0.330) (0.374) (0.000) (0.612) Full Sample: 0.0026 0.0000 0.0066** -0.0100*** 0.0000 -0.0121** (0.262) (0.024) (0.001) (0.020)

Hypothesis 5 is testing whether the WinLoss variable is significantly different from the WinWin variable, and whether the Winloss variable is more positive than the WinWin variable. Hypothesis 6 is testing whether the LossWin variable is significantly different from the LossLoss variable, and whether the LossWin variable is more negative than the LossLoss variable. The probability resulting from the AS Roma t-test regarding hypothesis 5 is 0.0000, which means that the WinLoss and WinWin variable are significantly different from each other for all significance levels. The WinLoss variable as well as the WinWin variable are significant at a 1% significance level. Besides, the WinLoss variable is more positive than the WinWin variable. Therefore hypothesis 5 is not rejected for AS Roma. The same holds for Fenerbahce,

22 for which the probability of the t-test regarding hypothesis 5 is 0.0038, whereby the WinLoss variable is significant at a 10% level, while the WinWin variable is insignificant. Therefore hypothesis 5 is not rejected for Fenerbahce as well. Regarding Lazio Roma, the t-test representing hypothesis 5 gives a probability of 0.0010. Thus the WinWin variable and the WinLoss variable are significantly different from each other, whereby both variables are significant at a 5% significance level and the WinLoss variable is more positive than the WinWin variable. Therefore hypothesis 5 is not rejected for Lazio Roma. The probability resulting from the Besiktas t-test regarding hypothesis 6 is 0.0023, which means that the LossWin and LossLoss variable are significantly different from each other for all significance levels. The LossWin variable is significant at a 5% significance level and more negative than the LossLoss variable, while the LossLoss variable is insignificant at all significance levels. Therefore hypothesis 6 is not rejected for Besiktas. The same holds for Trabzonspor, for which the probability of the t-test regarding hypothesis 6 is 0.0000, whereby the LossWin variable is significant at a 1% significance level, while the LossLoss variable is insignificant. Therefore hypothesis 6 is not rejected for Trabzonspor as well. Regarding Lazio Roma, the t-test representing hypothesis 6 gives a probability of 0.0326. Thus the LossWin variable and the LossLoss variable are significantly different from each other, whereby the LossWin variable is significant at a 1% significance level and the LossLoss variable is significant at a 5% significance level. Furthermore, the LossWin variable is more negative than the LossLoss variable. Therefore hypothesis 6 is not rejected for Lazio Roma. Finally, the LossWin variable for Juventus is significant at a 10% significance level, while the LossLoss variable is insignificant at all significance levels whereby the LossWin variable is more negative than the LossLoss variable. However, the probability of Juventus’ t-test regarding hypothesis 6 is 0.3340, and thus hypothesis 6 is rejected for Juventus. Comparing the Full Sample WinLoss variable and the Full Sample WinWin variable by running a t-test results in a probability of 0.0000, which means that the averages of the WinLoss and WinWin variable are significantly different from each other for all significance levels. Furthermore, the WinLoss variable is more positive than the WinWin variable. Therefore hypothesis 5 is not rejected. Comparing the Full Sample LossWin variable and the Full Sample LossLoss variable by running a t-test results in a probability of 0.0000, which means that the averages of the LossWin and LossLoss variable are significantly different from each other for all significance levels. However, the LossLoss variable is more negative than the LossWin variable, which is actually the opposite of what is hypothesised. Therefore hypothesis 6 is rejected. Table 7 presents the above described hypothesis outcomes:

Table 7: Hypothesis Rejection/No Rejection

Rivalry Hypothesis 5 Hypothesis 6 AS Roma Not Rejected Rejected Besiktas Rejected Not Rejected Borussia Dortmund Rejected Rejected FC Porto Rejected Rejected Fenerbahce Not Rejected Rejected Galatasaray Rejected Rejected Juventus Rejected Rejected Lazio Roma Not Rejected Not Rejected SL Benfica Rejected Rejected Sporting Lisbon N/A N/A Trabzonspor Rejected Not Rejected Full Sample Not Rejected Rejected Total Not Rejected: 4 3

Regarding hypothesis 1, there is found evidence for multiple football clubs that their stock price increases by a win of the focal team combined with a win of the period rival. However, in case of Juventus, this variable gives a remarkable result. For this football club a win of Juventus combined with a win of their period rival results in a decrease in the share price of Juventus. A possible conclusion for this finding could be that investors consider a win of Juventus’ period rival as more important than a win of Juventus. With respect to hypothesis 2, there is found evidence for multiple football clubs that their stock price increases by a win of the focal team combined with a loss of the period rival. In case of hypothesis 3, there is found evidence for multiple football clubs that their stock price decreases by a loss of the focal team combined with a win of the period rival. Finally, in case of hypothesis 4, there is found evidence for multiple football clubs that their stock price decreases by a loss of the focal team combined with a loss of the period rival. Furthermore, the results for the four different variables are compared by using a t-test. It is hypothesised that the stock price of the considered team increases even more in case the focal team wins and the period rival loses, compared to the stock price effect that is the result of both the focal team and their period rival win their match, for which there is evidence. Therefore hypothesis 5 should not be rejected. Finally, there is found evidence for hypothesis 6, which hypothesises that the stock price of the considered team decreases even more in case the focal team loses and the period rival wins, compared to the stock price effect that is the result of when both the focal team and their period rival lose their match.

6. Conclusion This study examines whether rivalry is present in the stock market regarding the football industry. Specifically, whether the performance of the period rival is present in the stock market. The expectation is that the performance of the supported team and the performance of the period rival(s) could both have an impact on the mood of the fan investor, and therefore could have an impact on the stock price of the focal club. Gross of the 11 examined football clubs confirm the literature that a win of the supportive team has a positive effect on their stock price and in case a loss of the supported team, their stock price decreases. These findings are even more strengthened by the goal- difference robustness check, that the more positive the goal margin is the more positive the supported team’s stock price is affected. All four dummy variables, WinWin, WinLoss, LossWin, and LossLoss, is found evidence for. Furthermore, comparing the WinWin variable with the WinLoss variable and comparing the LossWin variable with the LossLoss and testing whether they are significantly different from each other is also found evidence for. Based on these result, I can conclude that investors are driven by mood swings due to rivalry in football. Thus, Is period rivalry in the football industry present in the stock prices of listed football clubs? Yes! Presumably could these results and conclusions be strengthened if more data were available. This data availability is one of the limitations for this research. Only one database program was freely accessible for RUG students by which the data for the Portuguese stock exchange was, for example, only available from 15 October 2012 onwards. Demir and Rigoni (2017) argue that fan investors could weigh the performance of their rival more heavily if the ranking difference between the rival and the considered team decreases. This possibility is, however, not controlled for as the information was not available. Furthermore, the focus of this research is on football clubs coming from the top ten (appendix B) best performing European football competitions. Of course the research could be extended by including more European football competitions and/or football clubs coming from other continents. Which leads to suggestions for future research. 6.1 Future Research The way rivalry affects the stock price could also be applied to other sports. In case a basketball club is listed, the effect of rivalry on the stock price of this basketball club could also be analysed by making use of this analysis. Furthermore, as is already mentioned in the previous part, other football clubs coming from less performing European football competitions or coming from other parts of the world could be included and analysed here.

This research is therefore valuable and additive to multiple and different kind of people. Professors and students in the field of finance, sports economics and/or psychology could use this research as source or springboard for their own research. Moreover, fan investors could use this study as information source and tool to better understand the industry they are interested in and possibly also investing in. Finally, this research gives managers or shareholders and stakeholders of a particular football club insight in the way several sportive outcomes could influence the stock price of their football club.

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Websites: http://www.soccerway.com/ http://www.stoxx.com/ http://www.uefa.com/

Appendix A

All European football clubs that have a listing on a European stock exchange.

Football Club Country Listing date Aalborg Denmark 14-09-1998 Arhus Denmark 20-05-1988 AIK Sweden 31-07-2006 Ajax Netherlands 11-05-1998 AS Roma Italy 22-05-2000 Borussia Dortmund Germany 30-10-2000 Besiktas Turkey 19-02-2002 Brøndby Denmark 05-04-1988 Celtic United Kingdom 28-09-1995 FC Porto Portugal 01-06-1998 Fenerbahçe Turkey 17-09-2004 Galatasaray Turkey 19-02-2002 Juventus Italy 19-12-2001 Lazio Italy 06-05-1998 Lyon France 08-02-2007 Parkensport1 Denmark 13-11-1997 Ruch Chorzow Poland 31-12-2009 Silkeborg Denmark 07-10-1991 SL Benfica Portugal 21-05-2007 Sporting Portugal 02-06-1998 Teteks Macedonia 24-08-2009 Trabzonsport Turkey 15-04-2005 https://www.stoxx.com/index-details?symbol=FCTP

1 Parkensport is the name of the company that is owner of the club F.C. Copenhagen 29

Appendix B

UEFA rankings for club competitions

Rank Country Points* Spain 91.855 1 England 64.748 2 Germany 63.998 3 Italy 63.082 4 5 France 48.415 Russia 45.382 6 Portugal 41.582 7 8 Belgium 37.100 Ukraine 35.733 9 Turkey 31.200 10 * Based on the previous five seasons https://www.uefa.com/memberassociations/uefarankings/country/

Appendix C

AS Roma

2011/2012 2013/2014 2015/2016 2012/2013 2014/2015 2016/2017 Juventus 17.2444 Juventus 16.6333 Atalanta 11.8778 Lazio Roma 13.9889 Napoli 5.7333 Sampdoria 5.7556 Cagliari 13.1556 AC Milan 3.4444 Juventus 4.9667 Fiorentina 8.2556 Sampdoria -0.1111 Napoli 2.5111 Catania 7.9778 Lazio Roma -0.8000 Torino 1.6222 Udinese 6.8444 Internazionale -1.9222 Internazionale 0.9000 Chievo 6.2778 Atalanta -2.6111 Lazio Roma -3.8333 AC Milan 5.3556 Torino -4.5556 Genoa -6.1000 Napoli 3.5889 Fiorentina -4.5667 Sassuolo -6.1000 Bologna 1.3667 Sassuolo -4.9222 Bologna -6.2778 Atalanta 0.5000 Parma -5.9333 Empoli -8.1000 Parma -1.9444 Udinese -6.0000 Fiorentina -8.5778 Genoa -2.0333 Genoa -6.4222 AC Milan -8.6111 Palermo -2.3222 Cagliari -7.5889 Chievo -8.8889 Siena -3.3556 Hellas Verona -7.7889 Udinese -12.4444 Internazionale -6.8333 Chievo -8.9111 Palermo -19.1444

Besiktas

2011/2012 2013/2014 2015/2016 2012/2013 2014/2015 2016/2017 Istanbul Fenerbahce 13.0333 Galatasaray 22.0111 Basaksehir 7.6556 Kayserispor 12.4778 Fenerbahce 11.8556 Fenerbahce 5.8000 Galatasaray 11.0667 Sivasspor 4.4222 Kasimpasa 5.7444 Bursaspor 5.7444 Eskisehirspor 3.0000 Belediyespor 0.1444 Trabzonspor 4.9889 Rizespor 2.4778 Trabzonspor -1.5000 Istanbul Basaksehir 4.1667 Karabukspor 1.6778 Galatasaray -1.6889 Gaziantepspor 4.0556 Genclerbirligi 1.5222 Genclerbirligi -2.3556 Sivasspor 1.1111 Konyaspor -0.6667 Konyaspor -5.5222 Eskisehirspor 0.7444 Belediyespor -5.3111 Rizespor -6.2889 Genclerbirligi -0.1667 Gaziantepspor -5.5778 Bursaspor -6.6222 Mersin Idmanyurdu -3.8667 Kayseri Erciyesspor -5.6111 Antalyaspor -9.6889 Karabukspor -5.5444 Trabzonspor -7.6111 Osmanlispor -13.0444 Antalyaspor -7.4333 Bursaspor -7.8778 Gaziantepspor -17.3889 Kasimpasa -16.4111

Borussia Dortmund

2011/2012 2013/2014 2015/2016 2012/2013 2014/2015 2016/2017 Schalke 04 5.7000 Mönchengladbach 15.6000 Bayern München 15.7333 Hamburger SV 2.1222 Bayer Leverkusen 13.0778 Köln 7.1556 Hoffenheim 1.9111 Bayern München 9.9444 Hertha Berlin 4.0222 Stuttgart 1.0667 Wolfsburg 8.4111 Eintracht Frankfurt 2.8222 Bayern München -0.4778 Hamburger 7.3778 Schalke 04 0.7778 Hannover 96 -2.0111 Hertha Berlin 0.2556 Hoffenheim -0.9000 Wolfsburg -2.3000 Schalke 04 -0.5222 Bayer Leverkusen -5.0333 Mainz 05 -6.9444 Hoffenheim -0.9000 Darmstadt -5.1889 Bayer Leverkusen -7.3889 Mainz 05 -1.5333 Augsburg -5.6778 Mönchengladbach -8.4111 Augsburg -3.1111 Ingolstadt -6.4000 Nürnberg -8.6000 Hannover 96 -3.3111 Werder Bremen -6.7111 Augsburg -10.3667 Werder Bremen -4.8333 Hamburger -6.9444 Werder Bremen -13.7778 Eintracht Frankfurt -5.3444 Mainz 05 -7.4556 Freiburg -19.0556 Stuttgart -7.2444 Mönchengladbach -15.1111 Freiburg -15.9222 Wolfsburg -18.8556

FC Porto

2011/2012 2013/2014 2015/2016 2012/2013 2014/2015 2016/2017 SL Benfica -0.6667 SL Benfica 11.4444 Sporting Lissabon 13.9444 Olhanense -1.7444 Nacional 3.1667 SC Braga 6.7778 Gil Vicente -2.9667 Maritimo Funchal 1.9111 Moreirense 1.9444 Sporting Lissabon -3.0778 Estoril-Praia -1.0222 Pacos Ferreira 0.0444 SC Braga -7.3556 Sporting Lissabon -1.1111 SL Benfica -0.1889 Rio Ave -7.6333 Vitoria Guimaraes -1.6111 Tondela -1.5667 Pacos Ferreira -8.9778 Belenenses -3.9778 Maritimo Funchal -1.5889 Academica Coimbra -9.0000 Academica Coimbra -6.2556 Vitoria Setubal -1.9000 Maritimo Funchal -11.3222 SC Braga -7.6222 Vitoria Guimaraes -6.4556 Vitoria Setubal -13.5000 Gil Vicente -13.5556 Rio Ave -6.5222 Beira-Mar -14.4778 Pacos Ferreira -13.5667 Estoril-Praia -7.9556 Nacional -14.7667 Vitoria Setubal -14.8333 Arouca -8.3444 Vitoria Guimaraes -15.9778 Rio Ave -14.9333 Belenenses -10.2889 Arouca -16.0556 Boavista -16.1778 Nacional -23.8778

Fenerbahçe

2011/2012 2013/2014 2015/2016 2012/2013 2014/2015 2016/2017 Karabukspor 13.3778 Belediyespor 11.7000 Antalyaspor 13.7222 Galatasaray 9.6222 Galatasaray 4.8222 Istanbul Basaksehir 6.7111 Sivasspor 8.9556 Eskisehirspor 4.8111 Konyaspor 6.6111 Istanbul Basaksehir 6.5333 Konyaspor 2.0111 Bursaspor 4.3000 Mersin 3.6111 Trabzonspor 0.0000 Kayserispor 4.2667 Eskisehirspor 2.7444 Genclerbirligi -0.8778 Besiktas 4.2000 Antalyaspor 2.4222 Besiktas -1.8556 Osmanlispor 0.0889 Besiktas -2.0333 Kayseri -2.8556 Galatasaray -2.1444 Genclerbirligi -3.8444 Bursaspor -3.9889 Gaziantepspor -3.2222 Trabzonspor -5.1000 Karabukspor -5.0444 Genclerbirligi -7.7667 Bursaspor -6.5667 Sivasspor -5.0889 Rizespor -7.7667 Kayserispor -6.6000 Rizespor -7.4222 Belediyespor -9.4222 Gaziantepspor -8.5333 Kasimpasa -8.1222 Kasimpasa -9.7778 Gaziantepspor -16.1000 Trabzonspor -12.8667

Galatasaray

2011/2012 2013/2014 2015/2016 2012/2013 2014/2015 2016/2017 Gaziantepspor 3.8889 Kasimpasa 8.7222 Istanbul Basaksehir 12.7889 Bursaspor 2.3444 Fenerbahce 7.1778 Besiktas 12.6889 Genclerbirligi 1.7333 Trabzonspor 6.0778 Fenerbahce 12.1444 Fenerbahce 1.3778 Genclerbirligi 0.9000 Genclerbirligi 11.7000 Eskisehirspor -0.4889 Rizespor -0.7556 Osmanlispor 6.4333 Karabukspor -0.5333 Sivasspor -1.3111 Antalyaspor 2.7444 Besiktas -1.0667 Gaziantepspor -3.2556 Rizespor 2.3444 Mersin -2.4667 Eskisehirspor -3.6778 Kasimpasa 1.0556 Istanbul Basaksehir -4.7667 Karabukspor -5.2778 Gaziantepspor 0.6778 Trabzonspor -4.8556 Belediyespor -8.2444 Kayserispor -1.6778 Antalyaspor -6.9222 Konyaspor -8.7889 Konyaspor -5.7778 Sivasspor -13.7444 Kayseri -9.8778 Trabzonspor -6.6889 Kayserispor -13.8667 Bursaspor -9.9111 Bursaspor -11.5667 Besiktas -10.0111 Belediyespor -14.7111

Juventus

2011/2012 2013/2014 2015/2016 2012/2013 2014/2015 2016/2017 AC Milan 3.0111 Fiorentina 2.3000 Palermo 7.0333 Genoa 0.7000 Udinese 0.1111 AC Milan 2.8444 Bologna -1.4667 Genoa -2.0778 Genoa 1.0667 Internazionale -1.7667 Hellas Verona -2.8222 Lazio Roma 0.6778 Cagliari -2.3778 AC Milan -3.7556 Empoli -0.5222 Catania -3.1444 Parma -5.6333 AS Roma -2.7778 Chievo -3.1778 Sassuolo -5.8444 Fiorentina -3.6222 Napoli -3.4667 AS Roma -6.6111 Sassuolo -4.3556 Parma -5.4667 Sampdoria -7.4889 Torino -6.0444 Lazio Roma -5.4778 Lazio Roma -8.3000 Bologna -7.0778 Siena -5.6778 Torino -8.5222 Chievo -8.3222 AS Roma -6.2444 Napoli -8.8667 Atalanta -8.7556 Palermo -9.9222 Chievo -8.9333 Udinese -10.4667 Udinese -10.4111 Atalanta -10.4556 Internazionale -12.8778 Fiorentina -11.4667 Internazionale -10.9889 Napoli -14.8778 Atalanta -13.3111 Cagliari -12.0556 Sampdoria -16.3000

Lazio Roma

2011/2012 2013/2014 2015/2016 2012/2013 2014/2015 2016/2017 Genoa 18.6778 Internazionale 21.9889 Napoli 25.0111 Juventus 16.4778 Genoa 19.6000 Juventus 24.8778 Siena 16.0444 AC Milan 13.3333 AS Roma 15.8333 Catania 13.3556 Parma 10.8000 AC Milan 13.4556 Fiorentina 9.2667 Hellas Verona 9.8556 Chievo 11.6556 Palermo 7.5000 Juventus 6.6778 Torino 8.4333 Napoli 6.4667 Atalanta 6.0889 Internazionale 8.0444 Udinese 6.1333 Sampdoria 5.3556 Fiorentina 4.4889 Parma 4.2778 Udinese 1.5222 Bologna -0.8111 AC Milan 3.6000 AS Roma 0.3778 Empoli -2.2778 Cagliari -1.8222 Chievo -6.5111 Atalanta -2.4667 Internazionale -1.8333 Torino -6.8000 Udinese -2.7556 Chievo -2.1111 Sassuolo -7.2556 Genoa -3.4778 AS Roma -2.9889 Napoli -8.4111 Sassuolo -4.3444 Bologna -5.0222 Fiorentina -8.9889 Sampdoria -7.0889 Atalanta -9.6000 Cagliari -12.1889 Palermo -12.1444

SL Benfica

2011/2012 2013/2014 2015/2016 2012/2013 2014/2015 2016/2017 FC Porto 10.6667 Sporting Lissabon 0.7000 FC Porto 10.1889 SC Braga -1.0889 FC Porto -0.4444 Sporting Lissabon 6.1889 Sporting Lissabon -1.3333 SC Braga -2.1667 Vitoria Setubal 1.0778 Academica -2.3222 Pacos Ferreira -5.2333 Boavista 0.5778 Vitoria Guimaraes -6.5444 Vitoria Guimaraes -6.0222 Arouca -5.6556 Olhanense -7.0222 Rio Ave -6.3778 Rio Ave -8.0222 Beira-Mar -7.7889 Estoril-Praia -7.7000 Estoril-Praia -8.1444 Nacional -10.5333 Arouca -7.9556 Pacos Ferreira -10.0556 Maritimo Funchal -10.7889 Belenenses -8.4222 Moreirense -10.9444 Pacos Ferreira -10.9333 Gil Vicente -9.2333 Maritimo Funchal -11.7556 Rio Ave -13.4778 Maritimo Funchal -9.3333 Vitoria Guimaraes -14.0333 Gil Vicente -15.1444 Nacional -11.4778 SC Braga -14.3111 Vitoria Setubal -18.3778 Vitoria Setubal -14.0000 Nacional -15.8111 Academica -19.5111 Tondela -18.4667 Belenenses -24.6444

Sporting Lisbon

2011/2012 2013/2014 2015/2016 2012/2013 2014/2015 2016/2017 Maritimo Funchal 15.6667 FC Porto 12.1111 SL Benfica 4.8111 SL Benfica 13.3333 SL Benfica 8.3000 Rio Ave 3.7111 FC Porto 13.0778 Estoril-Praia 3.0778 FC Porto -1.9444 Pacos Ferreira 7.0444 Vitoria Guimaraes -0.0444 Belenenses -2.1444 Rio Ave 4.3667 Nacional -2.2222 SC Braga -2.7778 Academica 3.1222 Belenenses -3.2778 Tondela -3.2778 Vitoria Setubal 1.5000 Rio Ave -4.2444 Vitoria Guimaraes -4.8778 SC Braga -0.2889 Academica -6.1444 Maritimo Funchal -6.0333 Olhanense -2.0333 SC Braga -6.3444 Pacos Ferreira -7.4222 Gil Vicente -2.8778 Pacos Ferreira -7.1111 Nacional -9.2556 Nacional -4.0444 Maritimo Funchal -8.9778 Estoril-Praia -9.9556 Vitoria Guimaraes -6.4333 Arouca -9.4444 Boavista -10.0333 Beira-Mar -8.7222 Vitoria Setubal -9.7333 Moreirense -10.4889 Gil Vicente -14.6444 Arouca -13.7111 Vitoria Setubal -23.4444

Trabzonspor

2011/2012 2013/2014 2015/2016 2012/2013 2014/2015 2016/2017 Fenerbahce 15.1000 Besiktas 18.6111 Fenerbahce 23.8667 Galatasaray 14.8556 Eskisehirspor 12.5667 Galatasaray 18.6889 Bursaspor 8.4556 Belediyespor 11.4444 Osmanlispor 14.2889 Karabukspor 8.0333 Gaziantepspor 11.2444 Besiktas 13.5000 Kayserispor 6.8556 Fenerbahce 9.0000 Istanbul Basaksehir 11.5889 Antalyaspor 6.6667 Kasimpasa 6.4444 Antalyaspor 10.3778 Mersin 5.6222 Galatasaray 5.9222 Konyaspor 10.0667 Sivasspor 5.2556 Karabukspor 5.8556 Kasimpasa 7.9667 Besiktas 5.0111 Bursaspor 2.9556 Genclerbirligi 7.7444 Istanbul Basaksehir 3.9778 Konyaspor 1.3667 Bursaspor 7.5333 Eskisehirspor -1.4667 Genclerbirligi -4.6667 Belediyespor 4.4000 Genclerbirligi -3.4111 Rizespor -5.0556 Kayserispor -0.1667 Gaziantepspor -6.7333 Sivasspor -7.2778 Gaziantepspor -1.8889 Kayseri -11.3333 Rizespor -3.3111

Appendix D

D.1 Besiktas Testing the literature again finds evidence for the way the market reacts to the performance of the considered football club. The Win variable as well as the Goal-Difference is statistically significant at a 1% significance level. In case of the Win variable, the return increases by 0.0186 in case the considered team wins. In case of the Goal-Difference variable, the return increases by 0.0048 in case the considered team scores one goal more. The Loss variable is insignificant and thus in case of Besiktas, their stock price is not affected due to a loss of Besiktas.

Table D.1 – Market Reaction (Besiktas) AR (1) AR (2) Constant -0.0110** -0.0036 (0.047) (0.234) Win 0.0186*** (0.005) Loss 0.0017 (0.843) Goal- 0.0048*** Difference (0.006) N 204 204 R² 0.05 0.04

Only the LossWin variable is statistically significant at a 5% significance level. The other variables are insignificant at all significance levels. This LossWin variable states that the market return of the supported team decreases by 0.0179 in case the considered team loses and the rival wins which is according to the literature. Therefore hypothesis 3 is not rejected here. For the robustness check, the variable that is statistically significant at a 10% significance level is the NGD/PGD variable. In case the supported team loses and the rival wins by which the goal difference increases by 1, the market return of the supported team increases by 0,0143. This is actually the opposite of what is expected, therefore hypothesis 3 is considered to be incorrect. The other three variables, PGD/PGD, PGD/NGD, and NGD/NGD, are insignificant.

Table D.2 – Rival Influence (Besiktas) AR (1) AR (2) Constant 0.0014 -0.0013 (0.745) (0.705) WinWin 0.0002 (0.974) WinLoss 0.0059 (0.534) LossWin -0.0196** (0.034) LossLoss 0.0031 (0.821) PGD/PGD 0.0006 (0.671) PGD/NGD 0.0031 (0.614) NGD/PGD 0.0143* (0.055) NGD/NGD -0.0022 (0.548) N 204 204 R² 0.03 0.02

D.2 Borussia Dortmund The literature is proven to be present for the Win variable, the Loss variable, as well as the Goal-Difference variable. In case Borussia Dortmund wins their market return increases by 0.0094. In case Borussia Dortmund loses their market return decreases by even more, as their market return then decreases by 0.0122. In case Borussia Dortmund scores one goal more, their market return increases by 0.0034. These results are in line with the literature.

Table D.3 – Market Reaction (Borussia Dortmund) AR (1) AR (2) Constant -0.0032 -0.0038** (0.251) (0.015) Win 0.0094*** (0.004) Loss -0.0122*** (0.003) Goal- 0.0034*** Difference (0.000) N 204 204 R² 0.18 0.10

Multiple evidence is found here for the relevance of rivalry in football. The WinWin variable is statistically significant at a 1% significance level and results in an increased market of 0.0096 if both, Borussia Dortmund and its rival, win. Therefore, hypothesis 1 is not rejected. However, the WinLoss variable is insignificant at all confidence levels. Thus hypothesis 2 is rejected. The LossWin variable is significant at a 5% significance level and represents a decrease of the market return of Borussia Dortmund by 0.0092 if Borussia Dortmund loses its match and their rival wins that playing round, thus hypothesis 3 is not rejected. The LossLoss variable is statistically significant as well, even at a 1% significance level, and represents a decrease of 0.0295 if both football clubs lose their match. Therefore hypothesis 4 is not rejected as well. The PGD/PGD variable is statistically significant at a 1% significance level and results in an increased market of 0.0015 if both, Borussia Dortmund and its rival, wins and the goal difference increases by 1, and thus hypothesis 1 is correct. However, the PGD/NGD variable is insignificant at all confidence levels. Making hypothesis 2 incorrect. The NGD/PGD variable is significant at a 5% significance level and represents a decrease of the market return of Borussia Dortmund by 0.0089 if Borussia Dortmund loses its match and their rival wins that playing round and the goal difference increases by 1, and thus hypothesis 3 is correct. The NGD/NGD variable is statistically significant as well, even at a 1% significance level, and

44 represents an increase of 0.0062 if both football clubs lose their match and the goal difference increases by 1.This is actually the opposite of what is expected, therefore hypothesis 4 is incorrect.

Table D.4 – Rival Influence (Borussia Dortmund) AR (1) AR (2) Constant -0.0020 -0.0014 (0.347) (0.403) WinWin 0.0096*** (0.002) WinLoss 0.0062 (0.126) LossWin -0.0092** (0.032) LossLoss -0.0295*** (0.000) PGD/PGD 0.0015*** (0.005) PGD/NGD 0.0038 (0.151) NGD/PGD -0.0089** (0.018) NGD/NGD 0.0064*** (0.002) N 204 204 R² 0.18 0.13

D.3 FC Porto Unfortunately, in case of FC Porto, the literature is contradicted, as it is expected that fan investors react positively (negatively) to a win (loss) of the supportive team, but all the variables, Win, Loss, and Goal-Difference, are insignificant at all significance levels. Table D.5 – Market Reaction (FC Porto) AR (1) AR (2) Constant 0.0013 -0.0016 (0.915) (0.816) Win 0.0024 (0.865) Loss -0.0212 (0.263) Goal- 0.0011 Difference (0.705) N 132 132 R² 0.02 0.00

Both the WinWin as well as the WinLoss variable are insignificant and thus hypothesis 1 and 2 are rejected. Both the LossWin as well as the LossLoss variable are insignificant and thus hypothesis 3 and 4 are rejected as well. Thereby, the PGD/PGD, PGD/NGD, NGD/PGD, and NGD/NGD are statistically insignificant as well at all confidence levels, making hypothesis 1,2,3, and 4 to be incorrect. Table D.6 – Rival Influence (FC Porto) AR (1) AR (2) Constant -0.0070 -0.0017 (0.465) (0.829) WinWin 0.0185 (0.127) WinLoss -0.0220 (0.350) LossWin -0.0137 (0.493) LossLoss -0.0124 (0.778) PGD/PGD 0.0018 (0.394) PGD/NGD -0.0176 (0.121) NGD/PGD -0.0269 (0.229) NGD/NGD -0.0009 (0.961) N 132 132 R² 0.05 0.04

D.4 Fenerbahce Again, the literature is confirmed here. The Win variable is statistically significant at a 1% significance level. If Fenerbahce wins its match, their return increases by 0.0205. The Goal- Difference variable is statistically significant at a 1% significance level as well. Meaning that the market return of Fenerbahce increases by 0.0061 in case Fenerbahce scores one goal more. The Loss variable is insignificant and thus in case of Fenerbahce, their stock price is not affected due to a loss of Fenerbahce.

Table D.7 – Market Reaction (Fenerbahce) AR (1) AR (2) Constant -0.0123*** -0.0052** (0.002) (0.016) Win 0.0205*** (0.000) Loss -0.0001 (0.989) Goal- 0.0061*** Difference (0.000) N 204 204 R² 0.13 0.11

The WinLoss variable is statistically significant at a 10% significance level by which the market return of Fenerbahce increases by 0.0095 if Fenerbahce wins and their rival loses in a particular playing round. Thus hypothesis 2 is not rejected. The LossWin and LossLoss variables are both insignificant at all significance levels, therefore hypothesis 3 as well as hypothesis 4 are rejected.

Table D.8 – Rival Influence (Fenerbahce) AR (1) AR (2) Constant -0.0026 0.0003 (0.405) (0.876) WinWin 0.0052 (0.340) WinLoss 0.0095* (0.051) LossWin -0.0098 (0.157) LossLoss -0.0052 (0.687) PGD/PGD 0.0003 (0.815) PGD/NGD 0.0035 (0.175) NGD/PGD -0.0057 (0.411) NGD/NGD 0.0036 (0.413) N 204 204 R² 0.04 0.02

D.5 Galatasaray Unfortunately, in case of Galatasaray, the literature is contradicted, as it is expected that fan investors react positively (negatively) to a win (loss) of the supportive team, but all the variables, Win, Loss, and Goal-Difference, are insignificant at all significance levels. Table D.9 – Market Reaction (Galatasaray) AR (1) AR (2) Constant -0.0026 -0.0012 (0.589) (0.637) Win 0.0051 (0.371) Loss -0.0020 (0.786) Goal- 0.0015 Difference (0.283) N 204 204 R² 0.01 0.01

Both the WinWin as well as the WinLoss variable are insignificant and thus hypothesis 1 and hypothesis 2 are rejected. Both the LossWin as well as the LossLoss variable are insignificant and thus hypothesis 3 and hypothesis 4 are rejected as well. Thereby, the PGD/PGD, PGD/NGD, NGD/PGD, and NGD/NGD are statistically insignificant as well at all confidence levels. Thus hypothesis 1,2,3, and 4 are likely to be incorrect. Table D.10 – Rival Influence (Galatasaray) AR (1) AR (2) Constant -0.0019 0.0002 (0.594) (0.928) WinWin 0.0049 (0.397) WinLoss 0.0038 (0.582) LossWin 0.0053 (0.554) LossLoss -0.139 (0.296) PGD/PGD 0.0004 (0.720) PGD/NGD 0.0061 (0.105) NGD/PGD 0.0059 (0.431) NGD/NGD 0.0045 (0.203) N 204 204 R² 0.01 0.03

D.6 Juventus Unfortunately is the literature not confirmed here as the Win variable, the Loss variable, and the Goal-Difference are all three statistically insignificant at all significance levels. Table D.11 – Market Reaction (Juventus) AR (1) AR (2) Constant 0.0135 -0.0004 (0.142) (0.942) Win -0.0158 (0.125) Loss -0.0224 (0.163) Goal- 0.0003 Difference (0.915) N 228 228 R² 0.01 0.00

Regarding the market reactions to rivalry are the WinWin variable and the LossWin variable statistically significant at a 10% significance level. The WinWin variable represents a decrease of 0.0187 in market return if both Juventus and its rival win their match. Therefore hypothesis 1 is rejected. The LossWin variable represents a decrease of 0.0327 in market return if Juventus loses their match and its rival win their match. The PGD/PGD, PGD/NGD, NGD/PGD, and NGD/NGD are all insignificant variables which causes actually causes hypothesis 1,2,3, and 4 to be incorrect. Table D.12 – Rival Influence (Juventus)

AR (1) AR (2) Constant 0.0102 0.0028 (0.122) (0.554) WinWin -0.0187* (0.055) WinLoss -0.0104 (0.293) LossWin -0.0327* (0.098) LossLoss -0.0096 (0.678) PGD/PGD -0.0024 (0.261) PGD/NGD -0.0007 (0.900) NGD/PGD -0.0064 (0.746) NGD/NGD 0.0002 (0.973) N 228 228 R² 0.02 0.01

D.7 Lazio Roma Testing the literature again finds evidence for the way the market reacts to the performance of Lazio Roma. The Win variable as well as the Goal-Difference is statistically significant at a 1% significance level. In case of the Win variable, the return increases by 0.0234 in case Lazio Roma wins. In case of the Goal-Difference variable, the return increases by 0.0063 in case the considered team scores one goal more. The Loss variable is insignificant and thus in case of Lazio Roma, their stock price is not affected due to a loss of Lazio Roma.

Table D.13 – Market Reaction (Lazio Roma) AR (1) AR (2) Constant -0.0089* -0.0021 (0.054) (0.341) Win 0.0234*** (0.000) Loss -0.0070 (0.237) Goal- 0.0063*** Difference (0.000) N 228 228 R² 0.17 0.12

Regarding the way the market reacts to the performance of the considered rival is found evidence for. The WinWin variable is statistically significant at a 5% significance. In case Lazio Roma as well as their rival wins their match the market return of Lazio Roma increases by 0.0117. Therefore hypothesis 1 is not rejected. The WinLoss variable is statistically significant at a 5% significance level. In case Lazio Roma wins and their rival loses the market return of Lazio Roma increases even more, by 0.0136 and thus hypothesis 2 is not rejected. The LossWin variable is statistically significant at a 1% significance level. In case Lazio Roma loses and their rival wins the market return of Lazio Roma decreases by 0.0183. Therefore hypothesis 3 is not rejected. In case both Lazio Roma and their rival loses their match, the market return of Lazio Roma decreases by 0.0178, as the LossLoss variable is statistically significant at a 5% confidence level causing hypothesis 4 not to be rejected. For the robustness check, the variables that are statistically significant are PGD/PGD and NGD/NGD. PGD/PGD is statistically significant at a 10% significance level and the NGD/NGD variable is statistically significant at a 1% significance level. In case Lazio Roma and their rival team wins by which the goal difference increases by 1, the market return of Lazio Roma increases by 0.0022. In case Lazio Roma and their rival team loses both their match by which the goal difference increases by 1, the market return of Lazio Roma increases by 0.0064.

This data causes hypothesis 1 to be correct, while hypothesis 3 and 4 are considered to be incorrect.

Table D.14 – Rival Influence (Lazio Roma)

AR (1) AR (2) Constant -0.0003 0.0005 (0.921) (0.838) WinWin 0.0117** (0.045) WinLoss 0.0136** (0.049) LossWin -0.0183*** (0.008) LossLoss -0.0178** (0.032) PGD/PGD 0.0020* (0.085) PGD/NGD -0.0019 (0.748) NGD/PGD -0.0049 (0.199) NGD/NGD 0.0064*** (0.003) N 228 228 R² 0.10 0.06

D.8 SL Benfica Unfortunately, in case of SL Benfica, the literature is contradicted, as it is expected that fan investors react positively (negatively) to a win (loss) of the supportive team, but all the variables, Win, Loss, and Goal-Difference, are insignificant at all significance levels. Table D.15 – Market Reaction (SL Benfica) AR (1) AR (2) Constant -0.0150** 0.0005 (0.273) (0.915) Win 0.0189 (0.230) Loss 0.0158 (0.303) Goal- 0.0011 Difference (0.590) N 132 132 R² 0.01 0.00

AR (1) AR (2) Constant 0.0010 0.0017 (0.918) (0.771) WinWin -0.0004 (0.978) WinLoss 0.0234 (0.311) LossWin -0.0021 (0.868) LossLoss -0.0304 (0.219) PGD/PGD -0.0003 (0.893) PGD/NGD -0.0016 (0.902) NGD/PGD -0.0002 (0.975) NGD/NGD 0.0059 (0.193) N 132 132 R² 0.02 0.01

D.9 Sporting Lisbon Unfortunately, in case of Sporting Lisbon, the literature is contradicted, as it is expected that fan investors react positively (negatively) to a win (loss) of the supportive team, but all the variables, Win, Loss, and Goal-Difference, are insignificant at all significance levels.

Table D.17 – Market Reaction (Sporting Lisbon)

AR (1) AR (2) Constant 0.0038 0.0020 (0.802) (0.822) Win -0.0083 (0.637) Loss 0.0185 (0.499) Goal- -0.0016 Difference (0.713) N 132 132 R² 0.01 0.00

Besides, the hypothesis testing is missing here in total as the influence of the rival performance on the way fan investors react to the performance of their favoured team cannot be tested here again, as the regression is very highly collinear or even exact collinear. Therefore, the hypotheses cannot be tested here.

D.10 Trabzonspor Again, the literature is confirmed here. The Loss variable is statistically significant at a 1% significance level. If Trabzonspor loses its match, their return decreases by 0.0140. The Goal-Difference variable is statistically significant at a 1% significance level as well. Meaning that the market return of Trabzonspor increases by 0.0042 in case Trabzonspor scores one goal more. The Win variable is insignificant and thus in case of Trabzonspor, their stock price is not affected due to a win of Trabzonspor.

Table D.18 – Market Reaction (Trabzonspor) AR (1) AR (2) Constant 0.0022 -0.0006 (0.523) (0.740) Win 0.0059 (0.191) Loss -0.0140*** (0.003) Goal- 0.0042*** Difference (0.000) N 204 204 R² 0.10 0.08

Multiple evidence is found here for the relevance of rivalry in football. The LossWin variable is significant at a 1% significance level and represents a decrease of the market return of Trabzonspor by 0.0182 if Trabzonspor loses its match and their rival wins that playing round. Thus hypothesis 3 is not rejected. The PGD/PGD variable is statistically significant at a 1% significance level and results in an increased market return of 0.0035 if both, Trabzonspor and its rival, wins and the goal difference increases by 1. Therefore hypothesis 1 is considered to be correct. The other three variables, PGD/NGD, NGD/PGD, and NGD/NGD, are insignificant.

Table D.19 – Rival Influence (Trabzonspor)

AR (1) AR (2) Constant 0.0026 -0.0027 (0.343) (0.212) WinWin 0.0047 (0.330) WinLoss 0.0058 (0.374) LossWin -0.0182*** (0.000) LossLoss -0.0042 (0.612) PGD/PGD 0.0035*** (0.008) PGD/NGD 0.0044 (0.214) NGD/PGD 0.0000 (0.995) NGD/NGD -0.0007 (0.812) N 204 204 R² 0.10 0.04