The difference in stock price reaction among
investors in Europe: does culture explain the
difference?
Rob de Jong
S2081040
IB&M Master Thesis
Faculty of Economics and Business Rijksuniversiteit Groningen
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Abstract
This study examines the difference in stock price reaction among investors in Europe by using a new dataset, football matches. Furthermore, this study tests if these differences might be explained by cultural differences between countries. Stock price reaction around matches of 12 clubs from 6 European countries are analyzed in order to measure the difference emotion and response among investors. A lot of emotions are released during and around football matches and they provide therefore an excellent test case. The findings suggest that football matches play a significant role in explaining stock price movements of football clubs. Significant differences exist between countries around matches, indicating differences in emotions and response among investors. The results indicate that these differences can be explained by the differences in several cultural dimensions.
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Table of Contents
1. Introduction 4
2. Background and Theory 6
2.1 Culture, emotions and behavior 6
2.2 Emotions and finance 6
2.3 Football, emotions and behavior 7
2.4 Football and financial behavior 8
2.5 Cultural differences and stock prices 10 2.5.1 The effect of individualism/collectivism on stock prices 11 2.5.2 The effect of uncertainty avoidance on stock prices 12 2.5.3 The effect of long-term/ short-term orientation on stock prices 12 2.5.4 The effect of the North-South phenomenon on stock prices 13
3. Data and Method 14
3.1 Data 14 3.1.1 Dependent variable 14 3.2.2 Independent variables 14 3.2.2.1 National Index 14 3.2.2.2 Match results 14 3.2.2.3 Cultural dimensions 17 3.2 Models 19 4. Results 23
4.1 The influence of matches on stock prices 23 4.2 The influence of culture on stock prices 24
4.3 Robustness 25
4.3.1 GLOBE 26
4.3.2 Opening stock prices 27
5. Conclusion 28
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1. Introduction
Emotions of people vary across cultures and therefore grabs the attention of many researchers. Despite the fact that many research has been done in this area (e.g. de Jong and Semenov, 2001; Mesquita, 2001; Choi, Nisbett and Norenzayan, 1999), it still remains unclear why people from different countries react different after or before certain situations. This paper extends the current literature by investigating the stock market response after football matches among investors from six different countries in Europe. Differences in stock price reaction between countries are investigated and if these differences can be explained by differences in culture. These cultural differences leads to differences in emotion and behavior among investors (Chui et al., 2010) and this might explain differences in stock price reaction. Understanding what drives the reasoning of investors on the stock market can be of massive value in explaining differences in reaction among people from different countries. Although, this is a topic with ongoing investigation, the current literature suggests that human emotions have a huge impact on people’s behavior and decisions. For example, Mellers et al. (1999) argue that emotions influence the rational decision making process of people. In contrast to the historically dominant view of emotions as a negative influence on human behavior (e.g. Peters & Slovic, 2000 and Mellers et al, 1999 ), other research has highlighted the positive influence of emotions in human behavior and argue that human beings cannot survive without emotions (e.g. Shiv et al., 2005). While the research towards this topic has increased and improved there are still contradictions in the literature. A gap in the literature remains in explaining the differences in emotions and behavior of people between countries. This paper extends the current literature by testing an unused dataset in this area, namely football matches. A lot of emotions are released during and around football match. Furthermore, football matches are played weekly and therefore perfect fit for analyzing this phenomenon.
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However, these information signals are infrequent and have intervals of a quarter or even a year. Furthermore, sometimes it occurs that some investors have earlier insight in this information, which can lead to insider trading and is not in line with the assumptions of the efficient market model (Fama, 1991). Besides, information from quarterly figures is often already expected and is already processed in the stock prices. According to Brown and Hartzell (2001), earning announcements therefore cannot be considered as pure signals. Therefore, football clubs are perfect object to analyze. Football clubs play weekly and sometimes twice a week a match and so occur on frequent basis and these matches can possess valuable information. Furthermore, this information is for everyone and at the same time accessible and therefore excludes that certain investors act on inside information. Another important aspect of football matches is that betting odds are available for every match. According to the news model, stock price only react to unexpected information (Stadtmann, 2006), so betting odds can control for the expected match outcome. Odds can be seen as a forecast model for the upcoming event and can therefore be seen as guidance for what is the unexpected information after a match.
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2. Background and Theory
2.1 Culture, emotions and behavior
Emotions vary across cultures and thus on the behavior of people. There is an extensive body of literature in this area. One discovered that people from different culture differ in basis perceptual processes and causal reasoning (Ellsworth & Hong, 2011; Choi, Nisbett and Norenzayan, 1999). For centuries one thought that emotions were negative for the rational reasoning process and that those emotions are basic, invariant states of the body that can be turned on and off (Mesquita, 2001). Nevertheless, recent research has shown that emotions play a fundamental role in the rational thinking and decision making process. Several authors (e.g. Mesquita & Walker, 2002) confirm that these emotions differ between cultures and countries. These differences are based on the fact that living conditions vary across countries, because different cultural models are promoted that elicit culturally desirable emotions (Cohen, 2001). Expressions and behaviors that are consistent with cultural models tend to have a high rate of occurrence, whereas responses that are contrary to cultural models tend to be infrequent (Nisbett and Norenzayan, 1999). For example, Americans promote happiness which is a highly desirable emotion in the American cultural context according to D’Andrade (1987). According to him Americans give awards for many levels of accomplishments. Furthermore, they praise and complement each other a lot, while being critical is avoided which contributes to the higher level of confidence and a more individualistic society. In contrast to the American model, for example the East Asian countries promote that individuals take their proper place, stress relational harmony and low expression of happiness (Mesquita, 2001). These fundamentals create a more collectivistic society and a lower self-esteem for the inhabitants.
2.2 Emotions and finance
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This demonstrates that the emotions of investors influence their pricing of equities at that particular moment. The psychological aspect is an important part of explaining decision making process and Forgas (1995) argues that the emotions and feelings of people is guidance for their decisions at that moment. Forgas argues that these emotions affect the decision making process of investors, depending on how risky and uncertain the decision is. The more complex and uncertain a decision is, the greater the influence of emotions in the decision. Furthermore, Tversky & Kahneman (1991) argue that when people are in a good mood they are better problem solvers, more risk seeking and more optimistic about chances of favorable events. In contrast, if people are in a bad mood they are more likely to recall negative events and overestimate the changes of unfavorable events (Mellers, Schwartz, & Ritov, 1999). An effect of these emotions is called loss. According to Kahneman (2003), this can be described as that people prefer to avoid losses rather than gamble for greater gains, because people expect losses to have greater impact than gains of equal magnitude. Furthermore, research suggests that although negative outcomes can be quite painful, people typically overestimate the intensity and duration of their reactions to them (e.g. Mellers, Schwartz, & Ritov, 1999 and Kahneman, 2003). Football clubs play matches on weekly basis and thus supporters and investors have to deal with a lot of won or lost matches during a season. It is interesting to see how these investors of football clubs react on these situations and if they are vulnerable to loss aversion.
2.3 Football, emotions and behavior
Football is the biggest and most popular sport on the planet. For instance, the World Cup finals in South Africa in 2010 had a cumulative audience of 26 billion viewers from over 209 countries (Fifa report, 2010). Furthermore, the total revenues of the European football clubs grew to €16.9 billion in 2010/2011. Moreover, the combined revenue of the top 20 clubs in Europe has been estimated at €4.9 billion (Deloitte, 2012). Therefore, we can state that football is an important factor in today’s business and plays a subsequent role in people’s life.
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from the Stanley Cup playoffs. Furthermore, Carroll et al. (2002) found an increased number of heart attacks of 25% after the day England lost to Argentina in a World Cup match after penalty shoot-outs in 1998. Football results also tend to have an impact on economic behavior and investment mood of people. Bell et al. (2011) argues that even in the absence of any direct effects on company cash flows sport results, in particular football results, can affect stock prices by changing the mood, confidence and emotional state of investors. For example, Edmans et al. (2007) found that losses in football matches in the World Cup have an economically and statistically significant negative effect on the losing country’s stock market. On the other hand, Berument et al. (2003) claims that sportive success of Fenerbahce was related with the morale of laborers supporting Fenerbahce. He reveals that the monthly industrial growth rate increases with 0.26% with the number of games won by Fenerbahce. As described above, the psychology literature documents a significant difference in the behavior of fans following wins and losses. 2.4 Football and financial behavior
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including other variables in explaining the stock price movements of clubs. They found that several other factors such as the numbers of goals scored and playing against a rival have influence on stock price reactions. Few authors have investigated the influence of matches on stock price movements from other European countries. Berument et al. (2006) investigated the relationship between stock return and sporting performance and only verified the existence for one of the three major Turkish clubs. In Italy Boide and Fasano (2007) detected a similar result only finding a significant correlation between football matches and stock price reaction for one of the three investigated Italian clubs. Interestingly, he found that when AS Roma became the champion of Italy, the stock price went down because the club had to pay out high bonuses to the players. Other authors only investigated the stock price reactions of one single club in a country. Stadtmann (2006) studies the stock price reaction in the German context investigation the stock market data of Borussia Dortmund. He found a strong relationship between sport performance and stock price returns, in particular in European matches. Wu (2011) found similar results for the Italian club Juventus. Scholtens and Peenstra (2009) and Benkraiem et al. (2009) are the only authors who included clubs from several countries in one dataset and found that the stock market response is significant and positive for victories and negative for defeats. However, they did not investigated for differences between countries which would considerably distinguish them from the other papers. Table 1 shows the results of the influence of football matches on the stock price of clubs using the dataset from this paper. The findings show a significant relationship between matches and stock price reaction. These results are similar and thus confirm the findings of papers including a dataset from several European countries (Scholtens and Peenstra, 2009 and Benkraiem et al. 2009).
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might happen. She also states that people get a sense of satisfaction only thinking about a future event and become more optimistic. Furthermore, it is common known that supporters have subjective expectations about their own team and overestimate the qualities of their team. The second column of table 1 shows indeed that expectations have significant positive influence on stock prices before matches. Yet, this paper focuses on the differences between countries and therefore a more extensive analysis is shown later is this study.
2.5 Cultural differences and stock prices
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2.5.1 The effect of individualism/collectivism on stock prices
The individualism/collectivism (IDV) dimension reflects whether people look only after themselves or belong to in-groups which look after them in exchange for loyalty (Hofstede, 2001). Individualistic communities have a more ‘I- consciousness’ were collectivistic ones have a ‘We- consciousness’. According to de Jong and Semenov (2002) investors from individualistic societies are more confident in their ability to acquire and analyze information and attach less value to the different opinions from others. Meanwhile, investors who are raised in collectivistic cultures rely more on information obtained from their social groups and are less like to trade based on opposite information and opinions. Therefore, in collectivistic countries more herding behavior is expected compared to individualistic countries. Since investors in individualistic country rely more on their own interpretations and are less sensitive to herding behavior, a lower movement in stock prices is expected.
H1: Higher stock price movement is expected after an unexpected match result in countries with higher levels of IDV
Furthermore, literature suggests a link between individualism, over optimism and overconfidence (Chui et al., 2010). This suggests that higher levels of individuality are correlated with higher positive pre-match stock price movements.
H2: Higher stock price movement is expected before matches in countries with higher levels of IDV
2.5.2 The effect of uncertainty avoidance on stock prices
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full of unpredictable events. Not only football matches have an uncertain outcome, also players can be sold unexpected or a sudden injury of a key player can influence future matches. All together, lower stock price movements are expected in countries with higher UA around football matches.
H3: Lower stock price movement is expected after an unexpected match result in countries with higher levels of UA
H4: Lower stock price movement is expected before matches in countries with higher levels of UA
2.5.3 The effect of long-term/ short-term orientation on stock prices
According to Hofstede (2001) long-term and short-term orientation can be interpreted as dealing with society’s search for virtue. Short-term culture has a relative small propensity to save for the future and have a focus on achieving quick results. In contrast, long-term society’s people believe that truth depends very much on the context and time and have a strong propensity to save and invest. Therefore, higher stock price movements are expected in countries with lower levels of LTO. Also lower pre-match differences are expected since a single match should not influence a long term vision.
H5: Lower stock price movement is expected after an unexpected match result in countries with higher levels of LTO
H6: Lower stock price movement is before matches in countries with higher levels of LTO
2.5.4 The effect of the North- South phenomenon on stock prices
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environmental factors may lead to different decision making of people. For example, the weather has a significance influence on the mood of investors and so on their decision making. Eagles (1994) found that sunshine is one of the most positive significant influences on mood and behavior. Saunders (1993) investigated whether there was a relationship between equity returns and the level of cloud in New York. He found that more cloudy days resulted in lower equity prices, whereas more sunshine resulted in higher equity prices. There also seems to be a link between the temperature and equity pricing. Cao and Wei (2002) found that higher temperatures lead to aggression. The authors argue that aggression leads to higher risk-taking and so higher equity returns. In line with this research, Nisbett (1993) found that in the US southerners are more violent and aggressive than northerners. One of the explanations he calls is the temperature difference between North and South. Pennebaker et al. (1996) investigated the difference in emotional expressiveness between in Europe. He found very strong evidence that citizens from warmer climates are more emotional expressive then from cooler ones. Based on the literature we can argue that people from Northern Europe tend to be more aggressive and emotional then people from Northern Europe. Therefore, it is expected that investors from South European clubs react more heavily after and before a match then investors from North European clubs.
H7: Higher stock price movement is expected after an unexpected match result in countries from Southern Europe
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3. Data and Method
3.1 Data
In this section the dependent and independent variables included in the models that are build to explain the stock price differences of football clubs are described. The stock prices of football clubs are used as a dependent variable. National indexes, football results and cultural dimensions are used as explanatory variables. Descriptive statistics of the football results and the cultural dimensions are presented in table 2.
3.1.1 Dependent variable
Stock prices movements of football clubs are used to measure the differences between countries in emotions before and after matches. The data of the stock price of clubs was contained in the following way. Daily financial information (closing price, opening price) on every club examined was collected from DataStream. For some observations during the event window stock market data did not exist and were therefore deleted from the sample. Testing for of the influence of matches on stock prices reaction, two types of stock prices are used. Mainly, the closing price before the match to the closing price a day after the match (PCPC) is used and is presented in column A in the tables. As a robustness, the closing price before the match to the opening price the day after a match (PCPO) is used and is presented in column B in the tables. The robustness check will be discussed later in this study.
3.1.2 Independent variables 3.1.2.1 National index
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3.1.2.2 Match results
Football match results are used in order to measure the performance of the clubs. The data covers a total number of 1852 matches of twelve different clubs from six different countries. Appendix 1 shows the clubs that are or were stock listed in Europe. This paper analyzes the data of Ajax (Netherlands), Borussia Dortmund (Germany), Aalborg BK (Denmark), Galatasaray, Fenerbahce, Besiktas (Turkey) AS Roma, Juventus, SS Lazio (Italy), FC Porto, Sporting Portugal and Benfica (Portugal) from the season 2008/2009 to 2011/2012. The descriptive statistics of these matches are presented in table 2. Data of clubs from the UK are not included, since the majority of these clubs are delisted, often due takeovers. Previous research has shown that cup matches have no significant influence on the stock prices (Stadtmann, 2006; Duque and Ferreira, 2005) and are therefore not included in the dataset. Match results and betting odds were obtained from www.football-data.co.uk. Results and odds from European matches were sourced from www.oddsportal.com. The corresponding betting odds for each game were obtained for 11 bookmakers: Bet 365, Sporting Odds, Bet and Win, Gamebookers, Interwetten, Ladbrokes, Sporting Bet, Stan James, Stanley Bet, VC Bet and William Hill. The mid-week match odds were collected on Tuesdays and the weekend match odds were collected on the preceding Friday. The average of the available odds from these bookmakers was used to give the home win, away win and draw odds for each game.
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2006). The higher the mark-up, the higher the price for the bet. The mark-up for this match is 9%. One can now calculate the probabilities for a home win 76% (1/1.21 *1.09), a draw (16%) and an away win (8%). Based on these probabilities it is possible to compute the expected and unexpected points for Ajax in this match and control for the expectation. Since a win leads to 3 points and for a draw to 1 point the expected number of points for Ajax in this match is equal to 2.45 (3 * 0.76 + 1 * 0.16). Since Ajax lost the match and received 0 points, the outcome of the match can be interpreted as negative unexpected information. The expectation error amounts -2.45 points (0 – 2.45) and this expectation error can be regarded as new information and based on the news model a negative stock price reaction is expected.
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3.1.2.3 Cultural Dimensions
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3.2 Models
Different models are set up to test the hypothesis. Using consistent models as Stadtmann (2006) and Wu (2011), this section describes the empirical regression models. The mean-centered versions of the variables are used.
ΔTEAM t = β0 + β1 ΔS INDEX t (1)
Where ΔTEAM indicates the percentage change in stock prices of the football clubs and ΔINDEX the percentage change in the national stock market index. Using this regression, it is possible to separate which part of the variance in the change in stock prices is explained by changes in the overall market conditions and match outcome variables. The regressions results per country are proposed in appendix 2. For example, for Germany the estimated slope coefficient take the value β1 = 0.304. This means a 1% change of the DAX leads to an under-proportional change in the club stock price of 0.30%. Including the condition that a match took place the day before, model 2 shows that the slope coefficient is not significant different from zero anymore. This is a sign that match related variables may be more important for investors compared to the overall market conditions.
As mentioned before the fundamental idea of the news model is that only the unexpected part influence stock prices and that we use betting odds for controlling this part. Following Dobson and Goddard (2001) and Stadtmann (2006) several steps in the testing procedure are required. In the first step, variables that measure the actual match outcome (number of points gained) in national and European matches are included. In a second step the expected points are included. Since only the unexpected part of the match outcome has impact on stock prices, the coefficient on the actual performance should be the negative of the coefficient on the expected performance (Stadtmann, 2006). Several other authors (e.g. Wu, 2011and Stadtman, 2006) already found this connection, so we are not going to test this again. We continue directly by using the unexpected part as independent variable together with the national index.
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Good results in European matches might lead to higher price money, greater sponsorship income or increasing merchandize sales and therefore it seems reasonable to argue that European matches have greater influence on a clubs stock price then national matches. Model 3 shows the specification to test this.
ΔTEAM t = β0 + β1 Δ INDEX t + β2 TEAM_unexp + β3 EL_matches + β4 CL_matches (3) To find out whether there are differences between countries, all the 12 clubs from the 6 different countries are pooled together. This leads to a sample of 1851 matches. First, model 4 investigates whether the European matches have influence on the pooled sample
ΔTEAM_pooled t = β0 + β1 Δ INDEX_pooled t + β2 TEAM_unexp + (4) β3 EL_matches + β4 CL_matches
Model 5 test for the differences in stock price reaction between the Netherlands and the other countries after an unexpected won match. Model 6 test for differences after an unexpected lost match.
ΔTEAM t = β0 + β2 TEAM_unexwon + (β2 TEAM_unexpwon x β4 ITALY) + (5) (β2 TEAM_unexpwon x β5 GERMANY) + (β2 TEAM_unexpwon x β6 DENMARK )+
(β2 TEAM_unexpwon x β7 PORTUGAL) + (β2 TEAM_unexpwon x β8 TURKEY)
ΔTEAM t = β0 + β2 TEAM_unexplost + (β2 TEAM_unexplost x β4 ITALY) + (6) (β2 TEAM_unexplost x β5 GERMANY) + (β2 TEAM_unexplost x β6 DENMARK ) +
(β2 TEAM_unexplost x β7 PORTUGAL) + (β2 TEAM_unexplost x β8 TURKEY)
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cultural dimensions. The models test also for the interaction effect between the unexpected points and the cultural dimension. These models test for hypothesis 1, 3 and 5. Also the phenomenon North/South Europe found by Pennebaker (1996) is tested in model 10. This model test for hypothesis 7.
ΔTEAM t = β0 + β2 TEAM_unexp + β10 IDV + ( β2 TEAM_unexp x β10 IDV ) (7)
ΔTEAM t = β0 + β2 TEAM_unexp β11UA + β16 (β2 TEAM_unexp x β11UA) (8)
ΔTEAM t = β0 + β2 TEAM_unexp + β12 LTO + β17 (β2 TEAM_unexp x β12 LTO) (9)
ΔTEAM t = β0 + β2 TEAM_unexp + β13 North_South + (10) (β2 TEAM_unexp x β13 North_South)
As mentioned before, stock prices can also be affected by pre-match expectations. Therefore, we include the team expected points based on betting odds. We first test what the correlation is between the index and the club’s stock price before we can assume that investors expectations have influence the stock price.
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ΔTEAM_matchday t = β0 + β14 TEAM_expected + (β14 TEAM_expected x β4 ITALY) + (12) (β14 TEAM_expected x β4 ITALY) + (β14 TEAM_expected x β5 GERMANY) +
(β14 TEAM_expected x β6 DENMARK) + (β14 TEAM_expected x β7 PORTUGAL) + (β14 TEAM_expected x β8 TURKEY)
The models below tests if the cultural dimensions and the North-South phenomenon can explain the differences between countries. This model test for the hypothesis 2, 4, 6 and 8.
ΔTEAM_matchday t = β0 + β14 TEAM_expected + β10 IDV + (13) β19 (β2 TEAM_exp x β10 IDV )
ΔTEAM_matchday t = β0 + β14 TEAM_expected + β11 UA + (14) β19 (β2 TEAM_exp x β11 UA )
ΔTEAM_matchday t = β0 + β14 TEAM_expected + β12 LTO + (15) Β20 (β2 TEAM_exp x β12 LTO )
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4. Results
4.1 The influence of matches on stock prices
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4.2 The influence of culture on stock prices
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4.3 Robustness 4.3.1 GLOBE
The results of this robustness check of the cultural dimension can be found in table 6. Consistent with the previous findings, the results show that several of the dimensions have a significant impact in explaining the cross country differences after matches. Also the results for the pre-match stock prices differences are nearly similar when using the dimensions of Hofstede.
4.3.2 Opening stock prices
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6. Conclusion
The analysis of this paper shows that differences in emotion exist among people in Europe, using football matches as a new dataset in this area of literature. A lot of emotions are released during and around football matches and provide therefore an excellent test case. Testing two types of data (PCPC and PCPO) from 12 clubs in 6 European countries, the findings suggest significant differences in stock price reaction around matches. Together with the unexpected points, the cultural dimensions IDV and LTO of Hofstede tend to have highly significant value in explaining country differences in stock price movement after matches. In contrast, only LTO appears to be a good predictor in explaining pre-match differences in stock price development. The phenomenon that habitants from Southern Europe are more emotional then Northerners is not confirmed for stock price movement before and after matches. However, the results indicate that North-Europeans are less positive and confident about future events, which is an interesting notification for the European economy. Confidence levels and positivism among people are important predictors of economic forecasting models and might therefore explain the differences. Furthermore, the determination that certain cultural dimensions seem to have higher explanatory value in explaining stock price movements but others not, is a contribution to the ongoing investigation in explaining cultural differences between countries.
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