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Does success of the england national team affect the stock market?

Introduction

In this paper, the possible effects of investor mood on stock prices is investigated. The mood variable being the results of the England national football team. First of all, security prices should reflect all publicly available information (Fama, 1991) and therefore comply with the efficient market hypothesis. If the evidence suggests that the stock market reacts to investor mood via football results, then the stock market is clearly not efficient. This theory assumes that investors are rational meaning that they make decisions with reason and logic, but behavioural finance would state that investors are “normal” people (Statman, 1995). Therefore, this has led to researchers studying whether investor sentiment can influence the stock market. What most of these studies suggest is that market prices respond to events other than those indicated by economic fundamentals (Boyle and Walter, 2003). One factor that can be seen to influence the mood of investors is sporting events, in particular, football matches. In sports economics, football matches are largely focused on because football is by far the most popular sport covered by media (Dobson and Goddard, 2011; Bell et al., 2012).

A key research paper that this literature is based upon is the work of Ashton et al (2003). They studied the effects of the results of the England national team on the London Stock Exchange and found that a statistically significant relationship exists. In a more recent study, Ashton et al (2010) reported that when the dataset was extended until 2009, they found that the effect on stock market returns had declined in importance. Therefore, I want to investigate whether including more recent data, including the recent success of England in the 2018 World Cup might influence stock market returns.

Similarly, Edmans et al. (2007) found a strong negative stock market reaction to losses by national football teams in a study which considered 49 nations and four sports.

Literature Review

There has been a substantial number of studies conducted on the effect of sporting results on the stock market. The literature about the mood of an investor due to sporting results can be tracked back to Wann et al. (1994). They argued that fans experience a potent positive reaction when their team is successful and a corresponding negative reaction when their team is unsuccessful. Therefore, these reactions can either lead to increased or decreased self-esteem which in turn can result in positive or negative feelings about life in general. For instance, Carroll et al. (2002) established that admissions for heart attacks increased by 25% the day in which England lost to Argentina in a World Cup penalty shoot-out. Considering the fact that football is by far the most popular sport covered by media (Dobson and Goddard, 2011), it can be seen as a worldwide phenomenon that affects the lives of many. Also, since the performance of teams may cause a strong impact on the optimism or pessimism of individual investors (Boido and Fasano, 2007), it will be of interest to see if investors emotional sentiment can affect stock market returns.

This paper is based around a mood variable which is the results of the England national team to investigate the effect of investor mood on the stock market. According to Edmans et al. (2007), a mood variable must satisfy three characteristics in order to justify studying its proposed link with stock market returns. First of all, the given variable must impact mood in such a way, that its effect is powerful enough to show up in asset prices. Secondly, the variable must impact the mood of a large proportion of the population to be considered substantial. Thirdly, this effect must be correlated across the majority of individuals within a country.

The main study that of which this dissertation is inspired upon is the work of Ashton et al (2003) who published an article called “Economic impact of national sporting success: evidence from the London stock exchange”. This paper applied event study analysis to share index movements on the FTSE 100 index following the results of the England national football team. They collected daily data from the FTSE 100 from the period between January 6, 1984 to July 3, 2002. Also included were the results of the England’s national football from the same period. The results obtained exhibited a statistically significant relationship between the success of the England national team and the change in share prices traded on the London Stock Exchange.

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On the contrary, this paper was heavily scrutinised by Klein et al. (2009a). They published an article called “Reconsidering the impact of national soccer results on the FTSE 100” where they questioned certain aspects of the original article. They rebuilt the study of Ashton et al. (2003) and argued that the paper contained several inconsistencies in Ashton et al. (2003)’s event study set up. After correcting for these inconsistencies, they concluded that there is no evidence of a significant relationship between national sporting success of the English football team and the London stock exchange return index.

Acknowledging the work of Klein et al. (2009a), Ashton et al. (2010) revaluated the link between international football results and the stock market with their publication “Do national soccer results really impact on the stock market?”. They did so by using a larger set of data and applying an extended range of tests. Under these new specifications, contrary to the findings of Klein et al.(2009a), they reported that the link between international football results and stock market prices does still provide a significant relationship. However, when they the dataset was extended until 2009, the importance of sporting results on stock market returns had declined considerably.

There has been a vast number of other studies that have been conducted, seeking a link between international sporting success and stock market returns. A key study that was seeking to find a relationship between the success of a national team and the stock market was conducted by Edmans et al. (2007). They argued that losses in sports matches has an economically and statistically significant negative effect on the losing country’s stock market. Through evidence that sports results can affect an individual’s optimism or pessimism about their own abilities and life in general, the paper hypothesised that sporting results impact an investors’ views on future stock prices. Since international football matches take place at regular intervals and its importance is held in regard by a large proportion of the population in many countries, it is of interest to study. Edmans et al. (2007) also found a statistically significant loss effect for other sports including international rugby, cricket and basketball matches. However, there was no evidence of a reaction in the stock market to wins in any of the sports including football.

Another study by Dilek Demirhan (2013) analysed whether the sporting success of Turkish national football team affects the Borsa Istanbul (BIST) stock index returns. Using the GARCH model, he investigated the effects of national football matches on stock market returns. Evidence suggested that the wins of the Turkish national team did not affect the BIST-100 index return, whereas the failures (losses and draws) had a negative effect. According to the GARCH analysis made for all matches of Turkish national football team between 1988-2011, sporting success does not have a statistically significant effect on BIST-100 index return. Whereas, losses and draws cause a negative reaction on stock market return. The reason for the negative reaction of the stock market to the failures of national football team can result from the negative mood of football fans.

The studies that have been acknowledged so far have all researched the effect of national teams’ successes or failures on stock market returns. There has also been a plethora of other studies have studied whether a relationship exists between the results of football clubs affecting stock market returns as a whole or for football clubs listed on the market. For instance, Christos Floros (2014) found stock prices change in response to events such as a positive or negative result in relation to the mood of investors or supporters. The author gathered evidence that suggested positive effects of draws on Benfica’s and Ajax’s stock returns, and a negative effect of draws and losses on Juventus’s stock returns. Whereas, no effects were reported for Porto club. Therefore, draws can be perceived to be a bad result for Juventus investors, meaning that they sell stock. Meanwhile it can be considered a good result for Benfica and Ajax investors, leading them to buy stock. However, this paper does not explain as to why a draw can be perceived as a good result for one club but causes a negative reaction for another club.

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An explanation may come from an earlier study by Boido and Fasano (2007). They argued that football data for Italian teams showed that the average price/return ratio following wins is higher than average prices/return ratio following losses. The paper also examined the impact of drawn matches to check for possible conflicting results. The evidence suggested that Italian investors dislike matches ending in draws which may explain why Christos Floros (2014) established a negative effect of draws on the stock returns of Juventus. They believe that since football can be one of the most relevant factors in influencing investor moods as analysed by behavioural finance researchers, for Italian markets, sports performance affects the financial performance of listed football clubs.

Markman and Hirt (2002) conducted a study that showed that fans are subject to an allegiance bias. This is the belief that sporting fans who are invested in a desired outcome will have biased predictions. Therefore, if football fans believe their football team will win a certain match, we would expect to find a greater stock market reaction after losses than wins since this outcome was unexpected. To add to this, Godinho and Cerqueira (2014) state that the stock prices should only respond to the unexpected element of match results. These results are weighted by a new measure of match importance that they develop. When this measure is used to weigh the unexpected component of the results, they find that there is a significant link between the results and stock performance for 12 out of the 13 considered clubs.

The majority of studies that investigate the linkage between sporting success and stock market performance do indeed find evidence of an existing relationship. One notable exception is Gerlach (2011) who argues that sports do not cause unusual returns in either domestic or foreign markets. Even though the results of the matches of publicly traded football teams can have a direct economic effect on stock returns and share price, he states that this is not the case for national football teams whose performance does not affect stock returns. The paper implies that changes in investor sentiment following national team matches has no effect on stock market returns.

Conversely, expectations of potential economic benefits to be derived from the success of the national team can cause the stock market to react positively (Ashton et al, 2003). Also, Renneboog and Vanbrabant (2000) analysed 17 British teams during 1995-1998. The authors concluded that wins led to increases in price and draws and defeats resulted in a decline in price, with defeats having a bigger effect. To add to this, Palomino et al. (2005) studied 16 British teams for the period between 1999-2002 and found statistically significant abnormal returns. However, based on data for 10 clubs in the years 1997 to 2000 using the OLS Model, Zuber et al. (2005) found a lack of relationship between sporting performance and stock return. Henceforth, there remains considerable debate in the existing literature on the effect of football results on stock market performance.

Hypothesis

Following the literature review in the previous section, the null hypothesis can be formulated as follows: outcomes of matches of the England national football team do not affect the FTSE 100 index. This null hypothesis cooperates with the efficient market theory, in which markets are efficient and investors are rational. The alternative hypothesis is that wins lead to a positive effect on the stock market and losses lead to a negative effect on the FTSE 100 index. This is inspired from the research of the psychology literature which suggests that wins are associated with a good mood and losses with a bad mood.

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H0 : Football results do not affect the stock market

H1 : There are positive stock market reactions after wins, and negative stock market reactions after defeats

Data

The data we use in this paper is time series data in which stock returns have been collected from Thompson Reuters’ program DataStream. The market index used is the FTSE100 index from 1998 to 2018. The FTSE 100 is an index composed of the 100 largest companies listed on the London Stock Exchange (LSE). The index is seen as a good indication of the performance of major companies listed in the UK. Daily data is used, whereby the index is traded five days a week (Monday, Tuesday, Wednesday, Thursday and Friday). If one of these days is a holiday, and therefore no trading takes place, the “holiday function” of DataStream is used to account for the problem of non-trading days which are not in the weekend (Klein, Zwergel, & Henning Fock, 2009). To measure the effect of football matches on the stock returns, the first trading day after the event is chosen. The reason for this is to ensure that we have the return for a complete day when the outcome of the football match is known. When a match is played on Friday, Saturday or Sunday, the daily returns of Monday are used.

The international football results of the England national team are gathered from http://www.soccerbase.com from the period between 15 June 1998 through to 14 July 2018. The dataset includes all games from the World Cup played in that period, not including qualifying matches. The total sample consists of 28 matches (12 wins, 5 draws and 11 losses) that are defined as relevant mood events. The World Cup is the most renowned football tournament that is held every four years in which national teams battle it out to be named world champion. After progression in the qualifying rounds, a total of 32 teams will have progressed as competitors in the World Cup. The teams are divided into groups of four and take part in group matches. The teams in each group play against each other once with the top two advancing to the knockout stage. In this stage, which starts with 16 teams, no draws are allowed so matches will have extra time and a penalty shootout to determine a winner. Therefore, at the end of each stage half of the remaining teams are eliminated. The team that wins all of their elimination matches will win the World Cup.

In order to estimate wins and losses impact on stock returns while controlling for calendar effects such as the Monday effect, I will estimate the following Ordinary Least Square (OLS) regression:

(1)

• Where is the return of period t

• 0 is the regression intercept coefficient”,

• −1 is the previous day return and is included to account for first order serial correlation”,

• , i = 1″,…”,4, are dummy variables for the days of week, Monday through Thursday

• , i = 1″,…”,5, are dummy variables for days for the days after a non-weekend holiday. These dummy variables are used for controlling for the Monday effect and other confounding effects.

The model is a modified version of the original model used by Edmans et al (2007). In their model, they include a market index, but since this research focuses solely on one country it is not necessary to include it in this model. After estimating the regression model above the effect of the result of a football match can be approximated by using the residuals ( ̂ ) of the regression:

(2)

Where is a dummy variable that takes the value one if England wins, which makes t the first trading day after the match and zero otherwise. is a dummy variable for losses.

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