Int. J. Economics and Accounting, Vol. 3, No. 1, 2012
51
Investor sentiment and market reaction: evidence on 2010 FIFA World Cup Elisabete F. Simões Vieira GOVCOPP Unit Research, ISCA Department, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal Fax: +351-234-380-111 E-mail:
[email protected] Abstract: The purpose of this study is to examine whether investor sentiment influences the stock price reaction to football matches results, giving some contribute to the behaviour finance, or if investors react in a rational way, giving evidence of standard finance. To proxy for investor sentiment, we analyse the 2010 FIFA World Cup of South Africa. Globally, the study provides no evidence of a direct relationship between games results and the subsequent market reaction, not documenting a change in investor mood caused by soccer games outcomes. This paper contributes to the recent literature on the asset pricing impact of behaviour biases. The global results are more in line with standard finance than on behaviour finance, suggesting that stock prices are not influenced by economically-neutral events that can affect the investor sentiment, and, consequently, the stock prices. Keywords: investor sentiment; behavioural finance; standard finance; stock returns; volume trading; VT. Reference to this paper should be made as follows: Vieira, E.F.S. (2012) ‘Investor sentiment and market reaction: evidence on 2010 FIFA World Cup’, Int. J. Economics and Accounting, Vol. 3, No. 1, pp.51–76. Biographical notes: Elisabete F. Simões Vieira is a member of the GOVCOPP Investigation Centre and Portuguese Financial Analysts Association.
1
Introduction
Whether investor sentiment has any influence on asset returns has long been a topic of interest in behaviour finance. According this area of finance, investors make cognitive errors, resulting in asset prices deviating from present values of future cash flows (Statman, 1999). Two possible reasons for such divergence can happen. First, investors may incorporate considerations that are due to emotions or irrational reactions when evaluating assets (Hirshleifer, 2001). Second, they may have biased estimates of firms’ future cash flows distributions (Baker and Wurgler, 2006, 2007).
Copyright © 2012 Inderscience Enterprises Ltd.
52
E.F.S. Vieira
Several studies have documented a significant relationship between mood and human decision-making (Luce et al., 1999, among many others). Some of them particularise the relationship between mood and stock returns. Charoenrook (2005) examines a sentiment measure based on the University of Michigan Consumer Sentiment Index, finding that changes in consumer sentiment are positively related to excess market returns and Nofsinger (2005) concludes that positive (negative) public emotion can cause higher (lower) stock returns. Baker and Wurgler (2006) study how investor sentiment affects the cross-section of stock returns. They find that when beginning-of-period proxies for sentiment are low, subsequent returns are relatively high for small stocks, young stocks, high volatility stocks, unprofitable stocks, non-dividend-paying stocks, extreme growth stocks, and distressed stocks. On the other hand, when sentiment is high, these categories of stock earn relatively low subsequent returns. Schmeling (2009) examine whether consumer confidence affects stock returns internationally (18 countries), finding that sentiment negatively forecasts aggregate stock market returns on average across countries. The conclusions of two German studies, however, are not so robust. Lux (2008) studies the relationship between investors’ mood and the subsequent stock price changes, being the results dependent on the specification of the VAR model. The author found a surprising degree of informational inefficiency in the German stock market, concluding that the news contained in sentiment are not incorporated immediately into prices. Glaser et al. (2009) test whether individual investor sentiment is related to daily stock returns, finding a mutual influence of sentiment and stock market returns, but only in the very short-run. Returns have a negative influence on sentiment, while the influence of sentiment on returns is positive for the next trading day. A significant number of events has been used as a mood proxy, such as the weather (Saunders, 1993; Hirshleifer and Shumway, 2003; Krivelyova and Robotti, 2003; Cao and Wei, 2005; Yuan et al., 2006), the impact of the daylight saving effect (Kamstra et al., 2000; Pinegar, 2002), non-secular holidays (Frieder and Subramanyam, 2004; Croaley et al., 2008; Bialkowski et al., 2010) and sports results (Leeuwen and Kalshoven, 2006; Boido and Fasano, 2007; Edmans et al., 2007; Bernile and Lyandres, 2009). In addition, some authors document an extension of sport results on feelings about life and economic behaviour (Schwarz et al., 1987; Schweitzer et al., 1992; Gonzalez-Bono et al., 1999; Gore et al., 2003) and on health (White, 1989; Carroll et al., 2002; Berthier and Boulay, 2003; Chi and Kloner, 2003). Hirt et al. (1992) find a positive relationship between the Indiana University basketball team performance and their own performance and Wann et al. (1994) find evidence that fans experience positive (negative) reactions to watching their team performing well (poorly). Leeuwen and Kalshoven (2006) conclude that a world cup winner enjoys an average economic bonus of 0.7% additional growth and that market reacts positively. Recent studies show that sports affect investor sentiment and, consequently, the stock prices. When investors team wins (loses), they decrease (increase) stress, rise (decrease) self-confidence and become less (more) risk averse. Consequently, investors are disposed to buy (sell) stocks and prices will increase (decrease). Brown and Hartzell (2001) and Ashton et al. (2003) found evidence of a positive relationship between football teams and the market reaction, respectively for American and England teams.
Investor sentiment and market reaction
53
Edmans et al. (2007) document a change in investor mood caused by sport matches losses. They find that losses in football games have a negative and significant effect on the losing country’s stock market, especially in world cups versus continental cups, elimination stages versus group or qualifying stages, and for countries where football is important. However, they find no evidence of a significant effect after wins. Palomino et al. (2009) and Renneboog and Brabant (2000) concentrate their studies on the British soccer clubs, finding both a significant positive (negative) return after wins (losses). Spais and Filis (2008) conclude that an official football club sponsorship announcement can influence football clubs’ stockholders’ behaviour, thought the stock market reaction. There is large evidence of an asymmetric market reaction to games outcomes: stock price changes following losses are substantially larger in magnitude than those following wins (Kahneman and Tvershy, 1979; Brown and Hartzell, 2001; Edmans et al., 2007; Bernile and Lyandres, 2009; Palomino et al., 2009). According to Brown and Hartzell (2001), Edmans et al. (2007) and Bernile and Lyandres (2009), what may cause the market reaction asymmetry is the investor optimism. However, the results of Boido and Fasano (2007) contradict this asymmetric market reaction. Indeed, they analyse the impact of Italian football results on stock prices, and conclude that the price reaction to wins is higher than the average reaction following losses. Although several studies find evidence that sport matches results influences the investors’ sentiment and the subsequent market reaction, there are other studies which results are consistent with the efficient market expectations. The results of Boyle and Walter (2003) contradict the ones supporting a significant market reaction to football games results. They find that stock return is independent of the success of the premier New Zealand national rugby team, concluding that investor responses to sporting match results are transitory at best because investors became aware of the source of their emotional state. As stated by the authors (p.234), “they appear able to rationally discount shocks to confidence and self-esteem when the source of these shocks is easily recognisable”. Bernile and Lyandres (2009) analyse the stock returns of European soccer clubs, finding evidence not entirely consistent with investors reacting irrationally to match results. They document that the observed market inefficiency is caused, in part, by investors’ inability to form unbiased beliefs about future event outcomes. Investors tend to be overly optimistic about their teams’ prospects, leading, sometimes, to disappointments upon resolution of the uncertainty, causing negative post-game abnormal returns. Indeed, the authors found a close-to-zero positive return following wins and a significant negative return after losses and draws. Gray and Gray (1997) examine the efficiency of the US National Football League betting market, and their results suggest that widely documented inefficiencies in this market tend to dissipate over time. Kaplanski and Levy (2008) test the association between results of soccer matches and the US market returns, exploiting this effect on the aggregate international level. The authors conclude that, unlike the local effect, the aggregate effect does not depend on the game results, but is based on many games. In this context, we analyse the effect of investor sentiment on the market prices reaction, employing the 2010 FIFA World Cup of South Africa (2010 World Cup) results as a mood variable. Football might drive investor sentiment in a substantial way. First, it causes heart attacks (Carroll et al., 2002), homicides (White, 1989) and suicides
54
E.F.S. Vieira
(Trovato, 1998). Second, it effects extend to general life (Wann et al., 1994). Finally, it affects a large proportion of the relevant population (French and Poterba, 1991). Kaplanski and Levy (2008) conclude that the importance of a world cup is reflected in the vast media coverage, the huge TV audience1, and in the great interest that can be seen from related activities such as fan clubs and merchandise sales. Consistent with the behaviour finance assumptions and the relevant role that football plays in people’s lives, games results should affect investor sentiment, and, consequently, the market returns. However, according to the standard finance, there is no reason to believe that investment decisions are influenced by economically-neutral events that can affect the investor sentiment or that sports results have any effect on rational cash flow forecasts or discount rates, so investor behaviour and market prices should be impermeable to such events. Consequently, the market prices should not be influenced by sport results. The relevance of this paper is based on several reasons. First, studying whether investor sentiment influences stock market, contributes to the growing literature on behaviour or standard finance, depending on the obtained results. Second, we try to identify an unexpected change in the investor’s mood. According to Edmans et al. (2007), if investors react irrationally to resolution of uncertainty, sporting events would be an ideal setting for detecting such emotional reactions. Furthermore, our results may have important implications in what concerns investment decisions. Finally, as the best of our knowledge, this is the first study to analyse the market reaction to 2010 World Cup results, and we are convict that it is a good proxy to measure the mood of investors. Indeed, in order to analyse the possibility that investors react to sports results, it is required sporting events where the majority of market participants agree on the desired outcome, which may be the case of the 2010 World Cup. Globally, we find no evidence of a direct relationship between games results and the subsequent market reaction, not documenting a change in investor mood caused by soccer games outcomes, being these results more in line with the standard finance that with the behaviour finance. These results are in accordance with the ones of Boyle and Walter (2003), and, for the wins situations, with the ones of Edmans et al. (2007) and Bernile and Lyandres (2009), among others. Some results seem to indicate that investors of the favourite teams are somewhat influenced by football game results (Edmans et al., 2007), but globally, the aggregate market effect does not depend on the games outcome, which is somewhat in agreement with Kaplanski and Levy (2008) results. The paper proceeds as follows. The next section formulates the hypotheses. Section three describes the data and the methodology. Section four reports the empirical results and the robustness checks. Section five concludes the paper.
2
Hypotheses
The main purpose of this paper is to analyse the relationship between football match results and the subsequent market reaction. According the behaviour finance assumptions, stock prices react positively to wins and negatively to losses. Consequently, we formulate the following hypothesis:
Investor sentiment and market reaction
55
H1 Football match wins (losses) are associated with a positive (negative) market reaction. Under the null hypothesis, the share price movements are unrelated with the outcomes of football games, which is associated with the concept of rational investors and efficient markets. In addition, football outcomes might have influence also on the volume trading (VT). Thus, we formulate the second hypothesis: H2 Football match wins (losses) are associated with higher (lower) VT. We suppose elimination games to have a more significant mood impact than the group stage, so, we formulate the third hypothesis: H3 Elimination games are associated with a higher market reaction and VT than group stage games. Furthermore, we are waiting for a higher (lower) market surprise when favourites lose (win) and underdog wins (loses). In this context, we formulate the fourth hypothesis: H4 Favourite teams losses (wins) are associated with a negative (positive) and larger (lower) market reaction and VT, in magnitude, than underdog teams losses (wins). Finally, we would like to analyse whether the market reaction is stronger for small stocks than for the large ones. The corresponding hypothesis is the following: H5 Small stocks are associated with a higher market reaction and VT than the larger ones. According to Lee et al. (1991) and Edmans et al. (2007), small stocks are essentially held by local investors and their valuations are more likely to be affected by sentiment.
3
Data and methodology
The 2010 World Cup occurred from June 11 to July 11. We collect the 2010 World Cup matches and results from sports newspapers and check the data for errors using the website of the Fédération Internationale de Football Association (FIFA). Based on the qualifying rounds performance, 32 teams were selected as competitors for the 2010 World Cup. The teams were divided into eight groups of four teams each one (groups A to H). Teams in each group play against each other on the ‘group stage’. The top two teams from each group advanced to the stage two, which starts with 16 teams. In the ‘Elimination games’, the loser is eliminated from further play in the championship. At each of the following stage, half of the remaining teams were eliminated. The team that survives to all elimination won the 2010 World Cup, and the winner was Spain. Table 1 shows detail about the final tournament schedule and the respective results and Table 2 provides information about the number of wins, losses and draws in 2010 World Cup matches. There are a total of 64 football games, with 50 wins and losses and 28 draws (in 14 games).
56 Table 1 Game
E.F.S. Vieira 2010 World Cup: final tournament schedule June
Day
Group
Teams
Wins (W), losses (L), draws (D)
Group stage 1
11
Friday
A
2
11
Friday
A
3
12
Saturday
B
4
12
Saturday
B
5
12
Saturday
C
6
13
Sunday
C
7
13
Sunday
D
8
13
Sunday
D
9
14
Monday
E
10
14
Monday
E
11
14
Monday
F
12
15
Tuesday
F
13
15
Tuesday
G
14
15
Tuesday
G
15
16 Wednesday
H
16
16 Wednesday
H
17
16 Wednesday
A
18
17
Thursday
A
19
17
Thursday
B
20
17
Thursday
B
South Africa
D
Mexico Uruguay France Argentina Nigeria Korea Republic Greece England USA Algeria Slovenia Germany Australia Serbia Ghana Netherlands Denmark Japan Cameroon Italy Paraguay New Zealand Slovakia Côte d’Ivoire Portugal Brazil Korea DPR Honduras Chile Spain Switzerland South Africa Uruguay France Mexico Greece Nigeria Argentina Korea Republic
D D D W L W L D D L W W L L W W L W L D D D D D D W L L W L W L W L W W L W L
Investor sentiment and market reaction Table 1 Game
57
2010 World Cup: final tournament schedule (continued) June
Day
Group
Teams
Wins (W), losses (L), draws (D)
Group stage 21
18
Friday
D
22
18
Friday
C
23
18
Friday
C
24
19
Saturday
D
25
19
Saturday
E
26
19
Saturday
E
27
20
Sunday
F
28
20
Sunday
F
29
20
Sunday
G
30
21
Monday
G
31
21
Monday
H
32
21
Monday
H
33
22
Tuesday
A
34
22
Tuesday
A
35
22
Tuesday
B
36
22
Tuesday
B
37
23 Wednesday
C
38
23 Wednesday
C
39
23 Wednesday
D
40
23 Wednesday
D
Germany
L
Serbia Slovenia USA England Algeria Ghana Australia Netherlands Japan Cameroon Denmark Slovakia Paraguay Italy New Zealand Brazil Côte d’Ivoire Portugal Korea DPR Chile Switzerland Spain Honduras Mexico Uruguay France South Africa Nigeria Korea Republic Greece Argentina Slovenia England USA Algeria Ghana Germany Australia Serbia
W D D D D D D W L L W L W D D W L W L W L W L L W L W D D L W L W W L L W W L
58 Table 1 Game
E.F.S. Vieira 2010 World Cup: final tournament schedule (continued) June
Day
Group
Teams
Wins (W), losses (L), draws (D)
Group stage 41 42 43 44 45 46 47 48
24 24 24 24 25 25 25 25
Thursday Thursday Thursday Thursday Friday Friday Friday Friday
F F E E G G H H
Slovakia
W
Italy
L
Paraguay
D
New Zealand
D
Denmark
L
Japan
W
Cameroon
L
Netherlands
W
Portugal
D
Brazil
D
Korea DPR
L
Côte d’Ivoire
W
Chile
L
Spain
W
Switzerland
D
Honduras
D
Stage 2 Round of 16 49 50 51 52 53 54 55 56
26 26 27 27 28 28 29 29
Saturday Saturday Sunday Sunday Monday Monday Tuesday Tuesday
Uruguay
W
Korea Republic
L
USA
L
Ghana
W
Germany
W
England
L
Argentina
W
Mexico
L
Netherlands
W
Slovakia
L
Brazil
W
Chile
L
Paraguay
W
Japan
L
Spain
W
Portugal
L
Investor sentiment and market reaction Table 1 Game
59
2010 World Cup: final tournament schedule (continued) July
Day
Group
Teams
Wins (W), losses (L), draws (D)
Quarter-finals 57
2
58
2
59
3
60
3
Friday Friday Saturday Saturday
Netherlands
W
Brazil
L
Uruguay
W
Ghana
L
Argentina
L
Germany
W
Paraguay
L
Spain
W
Semi-finals 61
6
62
7
Tuesday Wednesday
Uruguay
L
Netherlands
W
Germany
L
Spain
W
Match for third place 63
10
Saturday
Uruguay
L
Germany
W
Final 64
11
Sunday
Netherlands
L
Spain
W
Winner: Spain Table 2
Number of wins (W), losses (L) and draws (D) in 2010 World Cup Group stage W
Algeria Argentina
3
Australia
1
Brazil
2
Cameroon
Stage 2
L
D
2
1
1
L
W
1
1
4
1
1
1
1
1
1
3
1
1
1
2
2
1
1
1
2
1
1
2
2
1
1 1
Total
W
3
Chile
2
1
Côte d’Ivoire
1
1
Denmark
1
2
England
1
France Germany
2
1
Ghana
1
1
D
2
1
3 1 2
2
L
1
1 1
3
1
5
2
1
1
2
2
1
1
60
E.F.S. Vieira
Table 2
Number of wins (W), losses (L) and draws (D) in 2010 World Cup (continued) Group stage
Greece
W
L
1
2
Stage 2 D
W
L
Total W
L
1
2
D
Honduras
2
1
2
1
Italy
1
2
1
2
Japan
2
Korea DPR
1
1
2
3
2 3
Korea Republic
1
1
1
1
1
2
1
Mexico
1
1
1
1
1
2
1
Netherlands
3
1
6
1
3
New Zealand
3
Nigeria
2
3
1
2
Paraguay
1
2
Portugal
1
2
Serbia
1
2
Slovakia
1
1
1
Slovenia
1
1
1
1
1
1
South Africa
1
1
1
1
1
1
Spain
2
1
6
1
Switzerland
1
1
1
1
Uruguay
2
1
2
4
2
1
USA
1
2
1
1
1
2
All countries
34
16
50
50
28
34
1
1
1
2
1
2
1
1
1
2
1
2
1
2
1
4 1
28
2 16
1
1
To measure the effect of the games results on market stock prices, we calculate the mean returns (computed from the log daily return) of the market index from the stock exchange of each of the countries of our sample, on the first trading day following the game, even for weekday matches, in order to ensure that we obtain the return of a full day after the match result is known. To analyse the impact of match results on share returns, and following Edmans et al. (2007), we estimate the subsequent ordinary least squares (OLS) model:
ε i ,t = β 0 + βW Wi ,t + β L Li ,t + μi ,t
(1)
where Wi,t dummy variable that takes the value one if country i won a football match on a day that makes the first trading day after the match and zero otherwise Li,t
dummy variable that takes the value one if country i loss a football match on a day that makes the first trading day after the match and zero otherwise
Investor sentiment and market reaction
μi,t
61
error term that is allowed to be heteroskedastic and contemporaneously correlated between countries.
εi,t’s are the ‘raw residuals’ εˆi ,t , defined by the following regression: Ri ,t = α + β1 Ri ,t -1 + β 2 Dt + β3Qt + εˆi ,t
(2)
where: Ri,t
daily return based on a share market index for country i on day t
Dt
{D1t, D2t, D3t, D4t} are dummy variables for days of the week, from Monday through Thursday
Qt
{Q1t, Q2t, Q3t, Q4t, Q5t} are dummy variables for days for which the previous one through five days are non-weekend holidays.
A significant number of games are played between Friday and Sunday. Consequently, we measure the daily return on Monday for all these matches, which may introduce the day-of-the-week effect in the relationship between football results and stock returns. With this regression model, we control for the Monday effect, as well as other confounding effects. The lagged index return is included to account for first-order serial correlation. We estimate the model simultaneously for all countries by interacting each independent variable with a set of country dummies. To analyse the relationship between football match results and the subsequent VT, we estimate similar equations, but considering as dependent variable the abnormal VT: VTi ,t = α + β1VTi ,t -1 + β 2 Dt + β3Qt + εˆi ,t
(3)
where VTi,t is the daily abnormal VT based on the share market index for country i on day t, computed from the log daily VT rate. In order to test the third hypothesis, we split the sample according the relevance of the matches. The first sub sample includes the group stage, with 48 games (34 wins and losses and 28 draws) and the second one has the 16 elimination games (16 wins and losses). To test hypothesis four, we use the Elo ratings, developed by Arpad Elo, a well known rating system used for ranking players, in order to have a proxy for matches’ relevance. The rating was obtained from the website ‘http://www.elorating.net’. We will analyse the relationship between football results and stock returns according the ranking of countries. As we can see in Table 3, as of May 11, 2010 (one month before the beginning of 2010 World Cup), the difference in Elo rating between the top ranked country (Brazil) and the bottom one (Korea DPR) was 552 points. According to a survey conducted by the daily sport press ‘L’Equipe’ in nine countries (France, UK, Italy, Spain, Germany, Netherlands, Brazil, USA and China), and released on May 19, 2010, the Brazil, Spain and Argentina were the favourite teams to the title of World Cup 2010, with 37%, 19% and 9% of preferences, respectively. Finally, to test the fifth hypothesis, we estimate equation [1] using pairs of small/large indices, with data on large indices for 16 of the 32 world cup countries2.
62
E.F.S. Vieira
Table 3
Elo rating for the 32 teams of 2010 World Cup Elo rating – May 11, 2010
Team
Rank
Ranking
Brazil
1
2085
Spain
2
2078
Netherlands
3
2005
England
4
1964
Italy
5
1922
Germany
6
1919
Argentina
7
1896
Mexico
8
1875
Chile
10
1855
France
11
1852
Portugal
12
1847
Serbia
13
1833
Uruguay
16
1811
Australia
18
1763
USA
19
1762
Denmark
20
1761
Switzerland
23
1746
Korea Republic
24
1740
Paraguay
26
1732
Greece
27
1731
Côte d’Ivoire
30
1725
Honduras
30
1725
Japan
36
1707
Cameroon
38
1705
Nigeria
40
1697
Ghana
41
1682
Slovenia
47
1648
Slovakia
49
1626
South Africa
75
1538
Algeria
76
1536
New Zealand
77
1534
Korea DPR
79
1533
Notes: The table reports the Elo rating, a rating system used for ranking players, for the 32 teams playing in the 2010 World Cup, at May 11, 2010. Source: The rating was obtained from the website ‘http://www.elorating.net’.
N
Mean
–0.6595
0.3851
Group stage
Elimination stage
(0.700)
(0.510)
(0.876)
16
–0.0015
0.0006
–0.0001
Mean
Wins
Losses
(0.720) (0.086)
0.0223
0.0126
0.0162
Std.
16
34
50
N
–0.0039
0.0032
0.0004
Mean
Losses
0.0103
0.0161
0.0144
Std. 14
N
0.0070
Mean
Draws
0.0104
Std.
Notes: The table reports the number of wins, losses and draws in 2010 World Cup matches, from June, 11 to July 11, 2010. Group stage matches are played during the championship, and qualify teams for the elimination stage. Elimination matches are the ones where the loser is eliminated from further play in the championship. Panel A presents the returns, computed from the log daily return on national stock market indices on the first trading day after wins, losses and draws. Panel B presents the abnormal VT computed from the log daily VT rate based on the share market index for the day following the game. It reports the z-test (p-value) for means differences.
0.3582
–1.7180
Wins
z-value (p-value) for mean differences between group and elimination stages:
–0.1565
All games
z-value (p-value) for mean differences between wins and losses:
0.0160
60
Elimination stage
–0.0023
34
0.0124
N 50
0.0034
Std. 0.0301
384
0.0026
Group stage
444
All games
No games
No games
Table 4
Panel A – Returns
Investor sentiment and market reaction 63
Mean daily return on the first trading day after 2010 World Cup matches
N
Mean
–0.9274
Group stage
Elimination stage
16
0.1312 –0.1604
0.2679
Losses
(0.015) (0.655)
0.4757
0.7187
0.6765
Std.
16
34
50
N
–0.0308
–0.0841
–0.0625
Mean
Losses
0.2584
0.4633
0.3897
Std. 14
N
–0.0387
Mean
Draws
0.3163
Std.
Notes: The table reports the number of wins, losses and draws in 2010 World Cup matches, from June, 11 to July 11, 2010. Group stage matches are played during the championship, and qualify teams for the elimination stage. Elimination matches are the ones where the loser is eliminated from further play in the championship. Panel A presents the returns, computed from the log daily return on national stock market indices on the first trading day after wins, losses and draws. Panel B presents the abnormal VT computed from the log daily VT rate based on the share market index for the day following the game. It reports the z-test (p-value) for means differences.
2.4242 –0.4467
Wins
Wins Mean
z-value (p-value) for mean differences between group and elimination stages:
1.6462 2.1873
All games
z-value (p-value) for mean differences between wins and losses:
0.7032
60
Elimination stage
–0.0537
50 0.7597
N
34
–0.1344
Std. 0.7510
384
–0.1221
Group stage
444
All games
No games
No games
Table 4
Panel B – Volume trading
64 E.F.S. Vieira
Mean daily return on the first trading day after 2010 World Cup matches (continued)
Investor sentiment and market reaction
4
65
Empirical results
Table 4 reports the number of wins, losses and draws included in the sample as well as mean daily log returns (Panel A) and abnormal VT (Panel B) on the stock markets on days following match days and non-match days. There are 444 trading days that are not associated with a football game for some teams, being the average return of 0.26% (Panel A). Although the average return for all the games is negative on days following a win and positive on days following a loss, the values are almost zero. What concerns the difference between group and elimination stages, the returns are always positive for group stage and negative for elimination stage. In the last stage, the average return is larger in magnitude on days following a loss (–0.39%) than on days following a win (–0.15%), suggesting that the loss effect is most pronounced for losses than for wins, which is consistent with the results of Edmans et al. (2007), Bernile and Lyandres (2009) and Palomino et al. (2009), among others. In addition, and reinforced by the significant mean differences between group and elimination stage returns for losses, it is an indication of more relevance for results on the elimination stage, which gives some evidence for H3, in what concerns losses and returns. Indeed, while a loss in this stage lead to an instant exit of the 2010 World Cup, in the group stage the team can have another chance, in the next round. According to Bernile and Lyandres (2009) findings, it seems that, for elimination stage losses, investors tend to be too optimistic about their teams’ prospects, being sometimes disappointed upon resolution of the uncertainty, causing negative post-game returns. Applying test z for mean differences between wins and losses returns, the null hypothesis of similar return after wins and losses cannot be rejected. Consequently, we find no evidence to support a significantly difference between market reaction to football match wins and losses, as well as to H1. Panel B presents the VT results. With the exception of the elimination stage, where there are no mean differences between wins and losses, we find evidence of a positive (negative) abnormal VT following games wins (losses), giving support to H2. For the group stage, a win increases the VT by about 27 percentage points, showing a large economic impact of wins on trading volume, and suggesting that investors’ trader based on the football game results. Finally, we find no evidence supporting H3, in what concerns the VT. Next, we analyse the relationship between football results and stock returns, conditioned on teams ranking. According the behaviour finance assumptions, it is expect that wins with lower (higher) rating and losses with higher (lower) rating have larger (lower) impact on stock returns. Table 5 shows the estimates of βW and βL from equation (1), for the returns (Panel A) and the abnormal VT (Panel B). Although all the losses coefficients have the expected signal (negative) and some of the win coefficients (positive), none of them are statistically significant, so we cannot reject the null hypothesis associated with H1, finding no support to a direct relationship between games results and the subsequent market reaction. Thus, we do not document a change in investor mood caused by soccer games outcomes.
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E.F.S. Vieira
Table 5
Abnormal daily stock market returns and VT after 2010 World Cup matches N
βW
t-value
βL
t-value
116
–0.0055
–1.477
–0.0059
–1.566
Group stage
84
–0.0042
–1.212
–0.0018
–0.507
Elimination stage
32
0.0014
0.244
–0.006
–0.503
108
0.0297
0.219
–0.1419
–1.016
Group stage
78
0.1345
0.813
–0.1786
–1.019
Elimination stage
30
–0.0598
–0.516
–0.2846
–1.112
Panel A – Returns All games
Panel B – Volume trading All games
Notes: The table reports the OLS estimates of βW and βL from:
ε i ,t = β0 + β W Wi,t + β L Li,t + μi ,t where Wi,t is a dummy variable that takes the value one if country i won a football match on a day that makes the first trading day after the match and zero otherwise; Li,t is a dummy variable that takes the value one if country i loss a football match on a day that makes the first trading day after the match and zero otherwise; μi,t is an error term that is allowed to be heteroskedastic and contemporaneously correlated between countries; the εi,t’s are the ‘raw residuals’ εˆi ,t defined by the following regression: Ri ,t = α + β1 Ri ,t -1 + β 2 Dt + β3 Qt + εˆi ,t (Panel A) and VTi ,t = α + β1 VTi ,t -1 + β 2 Dt + β3 Qt + εˆi ,t (Panel B) where Ri,t denotes the daily return based on a share market index for country i on day t; Dt = {D1t, D2t, D3t, D4t} are dummy variables for days of the week, from Monday through Thursday and Qt = {Q1t, Q2t, Q3t, Q4t, Q5t} are dummy variables for days for which the previous one through five days are non-weekend holidays; VTi,t is the daily abnormal VT based on the share market index for country i on day t, computed from the log daily VT rate. t-statistic is reported in parenthesis.
Our results contradict the ones supporting a significant market reaction to football games results, but are in accordance with the ones of Boyle and Walter (2003), who document that stock return is independent of the success of teams. These results suggest that investors became aware of the source of their emotional state, and tend to react rationally to match results. In addition, for the wins situations, it is also in agreement with the results of Edmans et al. (2007) and Bernile and Lyandres (2009), as both studies find evidence of an insignificant positive effect on returns after the wins. Although not significant, we would like to emphasise the economic importance of VT for the losses situations. Losses decrease the VT by about 18 and 28 percentage points, respectively for the group and elimination stages. However, the results are not statistically significant. Table 6 provides the same type of information as Table 4, but considering the matches results conditioned to the ranking of each team.
10
14
Draws with lower rating
0.0085
0.0057
0.0070
–0.0031
0.0015
0.0004
–0.0075
0.0015
–0.0001
–0.0023
0.0034
0.0026
Returns
0.0109
0.0103
0.0104
0.0084
0.0158
0.0144
0.0070
0.0180
0.0162
0.0160
0.0124
0.0301
Std.
–0.6686
1.1754
2.4799
z-value
(0.504)
(0.240)
(0.013)
p-value
14
14
28
10
40
50
10
40
50
60
384
444
N
–0.0202
–0.0519
–0.0387
–0.1976
–0.0124
–0.0625
–0.0001
0.1581
0.1312
–0.0537
–0.1344
–0.1221
Mean
0.2514
0.3645
0.3163
0.6047
0.2721
0.3897
0.2792
0.7316
0.6765
0.7032
0.7597
0.7510
Std.
–0.2526
0.9337
1.0328
z-value
Volume trading
(0.801)
(0.351)
(0.302)
p-value
Notes: The table reports the number of wins, losses and draws in 2010 World Cup matches, from June, 11 to July 11, 2010. The mean returns are computed from the log daily return on national stock market indices on the first trading day after wins, losses and draws. The abnormal VT is computed from the log daily VT rate based on the share market index for the day following the game. The results are presented considering the games results conditioned to the ranking of each team. It reports the z-test (p-value) for means differences.
14
Draws with higher rating
28
10
Losses with higher rating
Draws
40
Losses with lower rating
50
Wins with lower rating
Losses
40
Wins with higher rating
50
60
Elimination stage
Wins
384
444
Group stage
No games
Mean
Table 6
N
Investor sentiment and market reaction 67
Mean daily return and VT on the first trading day after 2010 World Cup, conditioned to the ranking of the teams
68
E.F.S. Vieira
Table 7
Abnormal daily stock market returns and VT after 2010 World Cup matches for the three top football teams N
βW
t-value
βL
t-value
Panel A – Returns Top three football teams All games
19
0.0104
0.287
0.0003
0.007
Group stage
9
0.0131**
5.439
–0.0253**
–5.068
Elimination stage
10
–0.0007
–0.027
–0.0018
–0.084
Other football teams All games
97
–0.0088***
–2.82
–0.0063**
–2.106
Group stage
75
–0.0077**
–2.252
–0.0024
–0.708
Elimination stage
22
0.0017
0.454
–0.0008
–0.099
19
–0.4164
–0.614
0.0818
0.116
Panel B – Volume trading Top three football teams All games Group stage
9
–0.323
–0.51
0.4443
0.58
Elimination stage
10
–0.3471*
–2.512
–0.1272
–1.12
All games
89
–0.0616
–0.492
–0.1553
–1.279
Group stage
69
0.0394
0.276
–0.1737
–1.232
Elimination stage
20
–0.3172*
–1.897
0.0608
0.155
Other football teams
Notes: *Significantly different from zero at the 10% level **Significantly different from zero at the 5% level ***Significantly different from zero at the 1% level The table reports the OLS estimates of βW and βL from:
ε i ,t = β 0 + βW Wi ,t + β L Li ,t + μi ,t where Wi,t is a dummy variable that takes the value one if country i won a football match on a day that makes the first trading day after the match and zero otherwise; Li,t is a dummy variable that takes the value one if country i loss a football match on a day that makes the first trading day after the match and zero otherwise; μi,t is an error term that is allowed to be heteroskedastic and contemporaneously correlated between countries; The εi,t’s are the ‘raw residuals’ εˆi ,t defined by the following regression: Ri ,t = α + β1 Ri ,t -1 + β 2 Dt + β3 Qt + εˆi ,t (Panel A) and VTi ,t = α + β1 VTi ,t -1 + β 2 Dt + β3 Qt + εˆi ,t (Panel B) where Ri,t denotes the daily return based on a share market index for country i on day t; Dt = {D1t, D2t, D3t, D4t} are dummy variables for days of the week, from Monday through Thursday and Qt = {Q1t, Q2t, Q3t, Q4t, Q5t} are dummy variables for days for which the previous one through five days are non-weekend holidays; VTi,t is the daily abnormal VT based on the share market index for country i on day t, computed from the log daily VT rate. The ‘top three football teams’ are Brazil, Netherlands and Spain. t-statistic is reported in parenthesis.
Investor sentiment and market reaction
69
The results for wins suggest that the market does not penalise the teams that have higher performance, measured by the Elo rating. The results for losses and draws show that favourite teams losses and draws are associated with a lower market reaction and VT than underdog teams, suggesting that the market reacts strongly when there is a ‘loser or drawer surprise’. Thus, we find evidence supporting H4, with the exception of the win cases. However, the difference in means is not statistically significant. Furthermore, we investigate whether the loss effect is stronger in countries where football teams have higher ratings, using also the Elo rating. We split the sample into ‘top three football teams’ – Brazil, Netherlands and Spain and ‘Other football teams’. Table 7 shows the results for abnormal daily stock market returns (Panel A) and VT (Panel B) after 2010 World Cup games for the three top football teams and for the other 29 teams. In what concerns returns results, we can see that, for the ‘Top three football teams’, the null hypothesis of βW = 0 and βL = 0 is rejected for the group stage regression, having the coefficients the expected signal, which gives evidence for H1, and is in accordance with the results of Edmans et al. (2007). In addition, for these coefficients, we have evidence of an asymmetric market reaction, manifested by larger negative returns after losses than positive returns after wins, which is consistent with the findings of Brown and Hartzell (2001), Edmans et al. (2007), Palomino et al. (2009) and Bernile and Lyandres (2009). For ‘Other football teams’, the coefficients are all negative, both for wins and losses. These results suggest that investors have some inability to form unbiased beliefs about future games outcomes, being excessively optimistic about their teams’ prospects, and leading to disappointments upon resolutions of the uncertainty, which is in agreement with Bernile and Lyandres (2009) opinion. The results suggest that market reacts more significantly to losses for the teams that have higher ratings than for the other teams, for both the group and the elimination stages, giving some evidence for H4, but only for losses and return situations. The VT results are only significant for the wins on the elimination stage, but the signal is negative, which contradicts the expected results formulated in H2 and H3, for the VT. However, it is in agreement with the negative coefficient of wins in Panel B of Table 5. Table 8 reports the results obtained when we apply the methodology to test whether the market reaction is stronger for small stocks than for the large ones. We can see that the only coefficients that are statistically significant are the ones of small-cap indices, in the return results (Panel A), which is somewhat consistent with the hypothesis that the market reaction is stronger for small stocks than for the large ones (Lee et al., 1991 and Edmans et al., 2007). Thus, we find some evidence to support H5 in what concerns the stock prices reaction.
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Table 8
Abnormal daily stock market returns and VT after 2010 World Cup matches for size-sorted portfolios N
βW
t-value
βL
t-value
Small caps
46
–0.0126**
–2.694
–0.0091*
–1.964
Large caps
70
0.0005
0.094
–0.0043
–0.734
1.0664
(0.286)
–1.3582
(0.174)
Panel A – Returns All games
Test of difference Group stage Small caps
36
–0.0109**
–2.106
–0.0078
–1.421
Large caps
48
0.0024
0.565
–0.002
–0.437
1.0664
(0.286)
–1.3582
(0.174)
Test of difference Elimination stage Small caps
10
–0.0027
–1.021
–0.0008
–0.389
Large caps
22
0.0092
1.145
–0.0135
–0.759
0.0916
(0.927)
–0.5738
(0.566)
Test of difference
Notes: *Significantly different from zero at the 10% level **Significantly different from zero at the 5% level The table reports the OLS estimates of βW and βL from:
ε i ,t = β0 + βW Wi ,t + β L Li ,t + μi ,t where Wi,t is a dummy variable that takes the value one if country i won a football match on a day that makes the first trading day after the match and zero otherwise; Li,t is a dummy variable that takes the value one if country i loss a football match on a day that makes the first trading day after the match and zero otherwise; μi,t is an error term that is allowed to be heteroskedastic and contemporaneously correlated between countries; The εi,t’s are the ‘raw residuals’ εˆi ,t defined by the following regression: Ri ,t = α + β1 Ri ,t -1 + β 2 Dt + β3 Qt + εˆi ,t (Panel A) and VTi ,t = α + β1 VTi ,t -1 + β 2 Dt + β3 Qt + εˆi ,t (Panel B) where Ri,t denotes the daily return based on a share market index for country i on day t; Dt = {D1t, D2t, D3t, D4t} are dummy variables for days of the week, from Monday through Thursday and Qt = {Q1t, Q2t, Q3t, Q4t, Q5t} are dummy variables for days for which the previous one through five days are non-weekend holidays; VTi,t is the daily abnormal VT based on the share market index for country i on day t, computed from the log daily VT rate. The large-cap indices are the Argentina Merval, Australia ASX, Brasil Bovespa, Denmark OMX, England FTSE-100, France CAC-40, Germany DAX, Italy MIB, Japan Nikkei-225, Korea Republic KOSPI, Mexico IPC, Netherlands AEX, Portugal PSI-20, Spain IBEX-35, Switzerland SMI and USA S&P-500. Panel A presents the regression results considering the returns and Panel B presents the regression results considering the abnormal VT. It reports the z-test (p-value) for means differences.
Investor sentiment and market reaction Table 8
71
Abnormal daily stock market returns and VT after 2010 World Cup matches for size-sorted portfolios (continued) N
βW
t-value
βL
t-value
Panel B – Volume trading All games Small Caps
38
0.1593
0.787
–0.1226
–0.571
Large Caps
70
–0.0568
–0.313
–0.1947
–1.032
–0.3699
(0.711)
–0.4938
(0.621)
Test of difference Group stage Small Caps
30
0.2547
1.16
–0.3208
–1.279
Large Caps
48
0.0414
0.169
–0.2162
–0.818
–0.3699
(0.711)
–0.4938
(0.621)
Test of difference Elimination stage Small Caps
8
–0.0712
–0.13
0.0582
0.237
Large Caps
22
–0.1104
–0.922
–0.2724
–1.053
1.7272
(0.084)
–1.4089
(0.159)
Test of difference
Notes: *Significantly different from zero at the 10% level **Significantly different from zero at the 5% level The table reports the OLS estimates of βW and βL from:
ε i ,t = β0 + βW Wi ,t + β L Li ,t + μi ,t where Wi,t is a dummy variable that takes the value one if country i won a football match on a day that makes the first trading day after the match and zero otherwise; Li,t is a dummy variable that takes the value one if country i loss a football match on a day that makes the first trading day after the match and zero otherwise; μi,t is an error term that is allowed to be heteroskedastic and contemporaneously correlated between countries; The εi,t’s are the ‘raw residuals’ εˆi ,t defined by the following regression: Ri ,t = α + β1 Ri ,t -1 + β 2 Dt + β3 Qt + εˆi ,t (Panel A) and VTi ,t = α + β1 VTi ,t -1 + β 2 Dt + β3 Qt + εˆi ,t (Panel B) where Ri,t denotes the daily return based on a share market index for country i on day t; Dt = {D1t, D2t, D3t, D4t} are dummy variables for days of the week, from Monday through Thursday and Qt = {Q1t, Q2t, Q3t, Q4t, Q5t} are dummy variables for days for which the previous one through five days are non-weekend holidays; VTi,t is the daily abnormal VT based on the share market index for country i on day t, computed from the log daily VT rate. The large-cap indices are the Argentina Merval, Australia ASX, Brasil Bovespa, Denmark OMX, England FTSE-100, France CAC-40, Germany DAX, Italy MIB, Japan Nikkei-225, Korea Republic KOSPI, Mexico IPC, Netherlands AEX, Portugal PSI-20, Spain IBEX-35, Switzerland SMI and USA S&P-500. Panel A presents the regression results considering the returns and Panel B presents the regression results considering the abnormal VT. It reports the z-test (p-value) for means differences.
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E.F.S. Vieira
4.1 Robustness For robustness reasons, we do additional tests. First, we scrutinise the influence of the outliers on the previous results, identifying the observations that have the greatest influence on the estimates of βW or βL, considering the outliers as the observations with large negative or positive returns on a win or loss-day. Table 9 reports the trimmed means, where 10% extreme negative and positive observations were removed. Comparing the results with the ones of Table 5, we can see that, although some of the coefficients change their signal, all the coefficients are statistically insignificant. Consequently, the main conclusions are unaffected by the outliers adjustment. The evidence gives no support for a relationship between football games outcomes and the subsequent market reaction, which contradict the behaviour finance expectations, but is in agreement with Boyle and Walter (2003) results. Table 9
Abnormal daily stock market returns and VT after 2010 World Cup matches using samples trimmed of outliers N
βW
t-value
βL
t-value
All games
90
–0.0016
–0.791
–0.003
–1.385
Group stage
65
0.0004
0.167
–0.0006
–0.253
Elimination stage
25
–0.0008
–0.326
–0.0071
–1.455
All games
82
–0.0099
–0.193
–0.0019
–0.036
Group stage
60
0.0095
0.172
0.0171
0.298
Elimination stage
22
–0.0736
–1.024
–0.1205
–0.755
Panel A – Returns
Panel B – Volume trading
Notes: The table reports 10% – trimmed means of the residuals εˆi ,t . Observations where variable Li,t equals one and the residual is smaller than the 10th percentile or larger than the 90th percentile are removed from the sample. Observations where Wi,t equals one are removed in a similar way. Panel A presents the returns results. Panel B presents the abnormal VT results. The t-statistics for the trimmed means are based on standard asymptotic approximations to the distribution of trimmed means (Huber, 1996).
Second, we model stock return volatility using a GARCH model as developed by Engle (1982) and generalised by Bollerslev (1986), using equation (2). The results are quite similar to the ones of Table 53, so, the main conclusions are unaffected by the GARCH (1, 1) volatility adjustment. Third, we consider two additional dummy variables. The first dummy variable equals 1 if team was favourite, according the Elo rating, and zero otherwise. The other identify if the team is presented in an advanced stage of the 2010 World Cup, equalling 1 if team has reached an advanced stage in the world cup (we consider as advanced stage the quarter-finals), and zero otherwise. None of the coefficients was statistically significant, consequently, the main conclusions are unaffected by the introduction of these two dummies. Finally, we compare the returns on the period during the 2010 World Cup to the returns on the same period of the previous year for the major market indices, in order to test if the results are due to a seasonal effect occurring these months or to the
Investor sentiment and market reaction
73
2010 World Cup, which may induce spurious correlation. The returns are not significantly different, so, we maintain the same conclusions.
5
Conclusions
Motivated to psychology and behaviour finance research showing that individual mood is affected by sports results, we analyse the market reaction to 2010 World Cup results, in order to examine whether emotional reactions to sporting outcomes are reflected in investor behaviour, as assumed by behaviour finance, or whether investors are able of treating these events in a rational manner, as argued by the standard finance. Previous research had mainly focused on the stock price reaction after the games. We try to go further, measuring also the effect on the VT. The main conclusion of our paper is that we find no evidence of any relationship between football results and the subsequent stock market return or VT. Consequently, we do not document a change in investor mood caused by soccer games outcomes. These conclusions are in agreement with what efficient market proponents would expect, as well as with the results of Boyle and Walter (2003), who document that stock return is independent of the success of teams. Moreover, and for the wins situations, the results are in accordance with the ones of Edmans et al. (2007) and Bernile and Lyandres (2009), because both studies find no evidence of a significant return effect after wins. Our results suggest that investors became aware of the source of their emotional state, and tend to react rationally to match results. This greater self-awareness of the cause of their emotional state potentially provides greater scope for investors to resist to irrational impulses. Combining our results with the ones of Boyle and Walter (2003), it seems that investors appear able to rationally discount the confidence distress when the source of these shocks is easily recognisable. This is an indication that game outcomes are not considered as valuable information for investors, being economically-neutral events, which supports the standard finance assumptions. However, it contrasts with the results of Saunders (1993) and Kamstra et al. (2000), who show that some events are economically-neutral, but are psychologically important, having a systematic effect on stock prices. We find some evidence of an asymmetric market reaction, manifested by larger negative returns after losses than positive returns after wins, which is consistent with the findings of Brown and Hartzell (2001), Edmans et al. (2007) and Bernile and Lyandres (2009). However, we find no significant difference between market reaction to football match wins and losses. In addition, we find weak evidence of investors trading based on football game results. When we split the sample according the three top football teams and the other teams, the results for the ‘other football teams’ suggest that investors have some inability to form unbiased beliefs about future games outcomes, being excessively optimistic about their teams’ prospects, and leading to disappointments upon resolutions of the uncertainty, which is in agreement with Bernile and Lyandres (2009) opinion. Furthermore, it seems that investor of the favourite teams are somewhat influenced by football game results, but, globally, the aggregate market effect does not depend on the games outcomes, which is somewhat in agreement with Kaplanski and Levy (2008) results.
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According to the results of Lee et al. (1991) and Edmans et al. (2007), we find some evidence that market reaction is stronger for small stocks than for the large ones, which suggests that small stocks valuations are more likely to be affected by sentiment. Overall, we find no evidence of any effect of 2010 World Cup on the stock market. It seems that investors do not incorporate considerations that are due to football emotions when they are deciding about their investments. The robustness tests reinforce the conclusions obtained so far. However, these results must be carefully interpreted for several reasons. First, some information related to the eventual game outcome may become available to the market before the game itself, and investors act accordingly. Second, the presence of foreign investors in the different markets, for whom the World Cup results might be a matter of indifference, might offset the tendency to domestic investors to react to the 2010 World Cup results. Finally, investors can invest in different countries, causing cross effects (a looser can invest in a winner country and the contrary). These phenomenons can be explored in further research.
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Notes 1
2
3
The 2010 World Cup had the highest TV audience of all the cups, of more than 700 million viewers. The final game registered a TV audience of 15.6 million in Spain (85.9% audience share) and 8.5 million in Holland (90.6% share). See http://tvbythenumbers.com/2010/07/12/world-cup-final-sets-spanish-dutch-tv-records/56772. We include as large-cap indices the Argentina Merval, Australia ASX, Brasil Bovespa, Denmark OMX, England FTSE-100, France CAC-40, Germany DAX, Italy MIB, Japan Nikkei-225, Korea Republic KOSPI, Mexico IPC, Netherlands AEX, Portugal PSI-20, Spain IBEX-35, Switzerland SMI and USA S&P-500. For simplicity reasons, the subsequent results are not reported in the study but available from authors upon request.
