Corporate Sport Sponsorship and Stock Returns:

Evidence from the NFL

Assaf Eisdorfer Elizabeth Kohl

March 2014

ABSTRACT

Most of the home stadiums/arenas of major-sport teams are sponsored by large publicly traded

companies. Using NFL data we find that stock returns to the sponsoring firms are affected by the

outcomes of games played in their stadiums. The mean difference between next-day abnormal

returns after a win and after a loss of the home team is 56 basis points for Monday night games

and 92 basis points for post-season elimination games. Evidence suggests that this effect is

partially driven by investor sentiment. The next-day abnormal return is further carried to

subsequent days. And moreover, the season-wide performance of NFL teams is reflected in the

sponsors’ off-season stock returns. Zero-investment portfolios of buying sponsors of winning

teams and shorting sponsors of losing teams yield mean excess return and factor-model alphas of

12 to 15 percent per season.

Keywords: Stock returns; Sport sponsorship

JEL Classifications: A12, G12, G14

* Assaf Eisdorfer is from University of Connecticut; Assaf.Eisdorfer@business.uconn.edu. Elizabeth Kohl is from University of Connecticut; Elizabeth.Kohl@business.uconn.edu.

We thank Ivo Welch for valuable comments and suggestions.

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1. Introduction

Consider a major sport team that is hosting an important and highly rated televised match, at which

it is expected to win. The match is played at the team’s home stadium, which is named after the

team’s sponsor, a large and well-known publically traded corporation. The name of the sponsor is

therefore repeatedly mentioned and seen during the match. Suppose that the team is unexpectedly

losing the match. Will this loss be reflected in the return on the sponsor’s stock in the next trading

day? If so, is it because the sponsor has been associated with the prevailing disappointment in the

team? Or is the market reacting to the financial implications of the loss – a shorter season, fewer

opportunities to promote the sponsor? Would the market have had a similar reverse reaction after

a win? In this study, we document that stock prices of companies that sponsor National Football

League (NFL) stadiums are affected by the outcomes of important individual games played in the

stadiums. Our evidence suggests that this effect is partially driven by investor sentiment.

Several recent studies have explored the association between sport matches outcomes and

stock market returns. Boyle and Walter (2002) find no relation between the success of the New

Zealand national rugby team and the stock market reaction in the country. Ashton, Gerrard, and

Hudson (2003) find a strong relationship between the performance of the English national soccer

team and the change in the price of shares traded on the London stock exchange, where good (bad)

performances by the national team are followed by positive (negative) market returns. Edmans,

Garc'ia, and Norli (2007) conduct a cross-country analysis and find that losses in soccer (and other

sports) matches have an economically and statistically significant negative effect on the losing

country’s stock market. They attribute this relation to the sudden change in investor mood. Yet

they find no evidence of a corresponding effect after wins. Scholtens and Peenstra (2009) analyze

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matches of eight publically traded European soccer teams. They find that the stock market response

is significant and positive for victories and negative for defeats.

Our study is the first to examine the effect of professional sport matches outcomes on stock

returns of the teams' sponsors. Although corporate sponsorship of professional sports stadia can

be traced back to the early 1900s, the number of major league teams playing in corporate-named

stadiums and arenas has sharply increased in the last two decades. As of the end of 2013, 62 percent

of the home stadiums/arenas of the four major league sports (Football, Baseball, Basketball, and

Hockey) were sponsored by publically traded companies.

Sponsoring a sport team is a major decision for a corporation. It is typically a long-term

commitment that requires a significant investment. The average price for acquiring naming rights

of a team’s stadium in the U.S. National Football League in recent years is 120 million dollars for

an average period of 17 years (see details in Table A1). In turn, the sponsoring company is

provided an opportunity to tie the company’s brands with a successful and popular sport

organization, an opportunity to establish a strong relation with a large fan base and the local

community, and a range of effective marketing tools – the sponsor typically gets branding and

signage inside and outside the stadium, product placement rights within the stadium, exclusivity

for use of its products by the team (official sponsor status), and has access to the team’s coaches

and players for promotions.1

In light of these potential advantages of sport sponsorship, attaching the company’s name to a

sport team can also be risky. If the team does not perform well, it will likely suffer less exposure

1 For example, the NFL team the Dallas Cowboys has recently signed a 25-year, 500 million dollar stadium naming rights deal with AT&T. As part of the deal, AT&T will continue to invest in improvements to wireless technology at the stadium, the city of Dallas will get 5 percent of revenue from the deal to help pay off the city's debt, and even the Legends Drive near the stadium will become AT&T Blvd. (DallasNews.com, July 25, 2013).

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in the media, lower demand for the team’s games and merchandises, and damaged reputation, all

of which can affect the team’s sponsoring company. The nature of competitive sports suggests that

a single event or game can largely determine the success versus failure of the team, and thereby

the value of its sponsoring company.2

Our study investigates if the outcome of NFL games can predict stock returns of the teams’

sponsors.3 We concentrate on the NFL for several reasons. First, football is the most popular sport

in the U.S. and has been for many years by a wide margin. Football games attract the largest crowds

and achieve the highest television ratings among all major sports.4 Second, a fairly large proportion

of NFL stadiums are sponsored by publically traded companies, 20 out of total of 32 NFL teams

(see details in Table A1). Third, the importance of a single game in the NFL is very high, relative

to the other major sports (baseball, basketball, and hockey). This is because the NFL season is very

short (16 games, compared to at least 82 games in the other sports). Furthermore, the NFL post-

season (playoff) system is based on one game at each progression towards the championship,

compared to a series of games (typically best-of-seven) in the other sports.

The NFL therefore provides a unique setting to assess the impact of outcomes of important and

popular sport matches on the stock price of the sponsoring companies. We manually collected

detailed data on all NFL games over the years 2003-2012 for teams that are sponsored by publically

2 For example, an article in BusinessWeek observes: “BMW Oracle (ORCL) team sailboat, eliminated in May from the America's Cup qualifying competition in Valencia, Spain, before the main event even started. German press reports put the cost of the failed Cup bid at nearly $200 million.” (Jack Ewing, June 7, 2007). 3 Extant stadium naming literature focuses on market reaction to the initiation of stadium sponsorship (see, for example, Clark, Cornwell, and Pruitt (2002), Becker-Olsen (2003), and Leeds, Leeds, and Pistolet (2007)). We however investigate the sensitivity of the sponsoring firms’ market value to the sport matches held in their stadiums. 4 A 2009 ESPN Sports poll asked respondents to name their favorite spectator sport (defined as one in which the responder attends games or matches, watches them on TV, listens to them on the radio, or reads about them). The poll results showed that professional football is the most popular sport with 24.4%, where professional baseball is the second with 11.0%. The Harris Poll has recently released the results of its annual survey of the favorite sports of Americans; professional football was the most popular with 35%, followed by professional baseball with 14% (BusinessInsider.com, January 27, 2014). According to the NFL, during the fall of 2013, NFL games accounted for 34 of the 35 most-watched TV shows among all programming (Bloomberg.com, February 5, 2014).

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traded companies. The sample contains 2,669 games (1,346 home games) during the pre-seasons,

regular seasons, and post-seasons of 20 teams with 23 sponsoring companies.

We begin our analysis by looking at home games of the NFL teams and comparing the next-

day abnormal stock returns of their sponsors after wins and losses. We measure abnormal returns

using five different models. When all home games are included in the sample, the results do not

indicate any abnormal return after wins or losses. This is not very surprising given that most games

are played simultaneously with other games, are not played in prime time hours, and are not

nationally televised. We therefore focus our examination on two subsamples of games that attract

the highest interest: regular season games that are played on Monday nights and post-season

games. The games in these two categories are stand-alone (no other games played at the same

time), nationally broadcasted by a major television network, played at prime time hours (the

Monday night games), and are critical for the team success (the post-season elimination games).

The results show a strong effect of the games' outcomes on the sponsors’ stock returns. First,

after Monday night games, the sponsor of the home team typically earns a positive abnormal return

if the team had won (average of 0.56 percent across all models of abnormal return) and no

abnormal return if the team had lost. For post-season games the difference is even greater; the

losing teams’ sponsors earn, on average, abnormal returns lower by 0.92 percent than that of the

winning teams’ sponsors, although, on average, the next-day abnormal return is negative both after

wins and losses (-0.23 and -1.16 percent, respectively). This can be explained by the crucial effect

a payoff game loss has on the team, as it eliminates the team from the playoff contention. This

may also indicate that there is a surprise effect, i.e., it is more expected that the home team wins

in a post-season game (a playoff game is typically played at the stadium of the team with the better

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record); thus a win does not have much effect while a loss leads to a significant negative return of

the home team's sponsor. (We incorporate the element of surprise in the game’s outcome later on.)

We re-examine the impact of the games' outcomes by regressing the next-day abnormal return

on a dummy variable indicating a game win and a set of control variables. This regression offers

the advantage that we can control for other well-known determinants of stock returns and thus

estimate the marginal influence of the game outcome on the sponsor's stock return. The regression

results confirm those found in the mean differences; the coefficient of the win dummy is positive

and significant. Averaging across all models yields a marginal next-day abnormal return of 0.81 if

the team wins a Monday night game, and 0.90 percent if the team wins a playoff game.

We assess the effect of cross-sectional characteristics on the results. The impact of the outcome

of home games on the sponsoring firms’ stock returns varies with the nature of the sponsorship.

There is a stronger impact for large-scale sponsorships and in the early years of the sponsorship.

We find however only a weak effect of the size of the media market of the team as well as the

market value of the team. The extent to which the game outcome was unexpected has a significant

effect on the results only for post-season games.

We next explore the drivers for the effect of match outcomes on sponsors’ stock returns. As

discussed above, the outcome of a single game can have a tremendous effect on the success of the

team (e.g., a post-season elimination game), and thereby has important financial implications for

the sponsoring company. Market efficiency therefore suggests that post-game stock returns reflect

changes in investors’ expectations of future cash flows as a result of the games’ outcomes,

especially if unexpected. Yet there may be also a behavioral effect. Prior studies associate stock

market reaction to sport events with changes in investor mood. Edmans et al. (2007) argue that

national sport events (especially soccer matches) can produce substantial and correlated mood

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swings in a large proportion of a country’s population, which is translated into the stock market

movement in the country. Although our analysis does not examine national sport events, but rather

is at the team-sponsor level, the large popularity of the NFL and the massive fan base of every

team, raise the question of whether investor mood plays a role in the post-game return on the

sponsors’ stocks. That is, does the unique ability of sport events to generate strong emotions at the

moment of time among large populations, also generate stock market overreaction for the

sponsoring company attached to the team?

We employ two tests to assess the presence of stock market overreaction to game outcome.

First, we look at the return on the sponsors' stock after away games (games played at the opponent's

stadium). Rational expectations suggest a similar effect of wins/losses in regular season away

games and home games, as both should have similar implications on the team and the sponsor for

the remaining of the season. Behavioral bias suggests a stronger effect for home games; this is

because the sponsoring company is more visible during the game, and thus investors associate the

game outcome with the sponsoring firm. Second, we examine whether the next-day return tends

to reverse in the following days, as evidence of initial overreaction.

Both tests indicate that behavioral bias is more likely present after Monday night games. First,

the outcomes of away games played on Monday night do not lead to any abnormal return on the

sponsors’ stocks as they do for home games, which is inconsistent with rational expectations. The

post-season sample, however, shows some effect also in away games, albeit weaker than that found

in home games. Second, the first-day abnormal return almost completely reversed in the

subsequent days after Monday night games (which can indicate investor overreaction), but not

after post-season games. This difference can be attributed to the stronger impact a post-season

game has on the team and thereby its sponsor.

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We investigate whether incorporating these post-game return patterns provide profit

opportunities. We form a weekly portfolio of buying the stocks of all sponsoring companies whose

teams won that week, and selling the stocks of all sponsoring companies whose teams lost that

week. We hold this portfolio from the second trading day to the fifth trading day after the game

(as the first-day profit cannot be earned). The portfolio generates abnormal profits, particularly for

home games whose outcomes are hard to predict and by nature attract more attention. The mean

excess return for this portfolio is 12 to 15 percent per season, and it remains similar after adjusting

to risk using standard models.

Finally, we take our analysis from the instant, post-game investor reaction to a season-wide

view. We examine whether the performance of a team during the entire season is carried to the

sponsoring company’s value after the season. We form a post-season portfolio of buying the stocks

of all sponsoring companies whose teams had a “successful” season (measured by winning record,

improvement from the previous season, or playoff participation) and selling the stocks of all

sponsoring companies whose teams had a “non-successful” season. Value-weighted portfolios

exhibit surprisingly strong results: mean excess return and factor-model alphas of approximately

2 percent a month. We do not have a convincing explanation for these high abnormal profits, but

rather acknowledge a possible presence of mispricing, as the implications of a team’s performance

on its sponsor are gradually revealed and thus reflected in post-season price movement.

This paper contributes to the literature in several aspects. To the best of our knowledge, we

are the first study to highlight the sensitivity of sponsoring corporations’ value to the outcome of

individual sporting events. Although NFL stadium sponsors are typically among the largest and

most well-known firms listed on the U.S. stock exchanges, the outcome of a single match played

by their sponsored teams can lead to significant swings in sponsor firm stock prices. Second, our

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evidence indicates that stock market reaction to game outcomes is driven, at least to a certain

extent, by investor sentiment. This finding is consistent with prior studies attributing country-wide

market price movement after sport matches to changes in investor mood (e.g., Edmans et al.

(2007)) and also with the documented emotional reactions that NFL matches can illicit.

Third, in a broader asset pricing view, we show that the effect of game outcome on sponsoring

firm value provides profitable stock trading opportunities. Trading strategies earn abnormal returns

comparable and even higher than those generated by the most puzzling return patterns, such as

momentum and distress effects. Moreover, the sponsor-based strategies involve only very large,

highly traded, and highly liquid stocks, thus are less affected by market frictions and easier to

implement.

The paper proceeds as follows. Section 2 describes the data, Section 3 examines the effect of

home-game outcome on sponsoring firm return, Section 4 offers team performance-based stock

trading opportunities, and Section 5 concludes.

2. Data

We manually collected data on all NFL games over the years 2003-2012 for teams that are

sponsored by publically traded companies. Using official NFL team websites and stadium

websites, we identified teams who are or have engaged in stadium naming rights agreements with

publicly traded firms, and obtained key characteristics of the agreements. We then use team

websites as well as secondary sports websites to gather data on game schedules across the sample

period, including game date, location, score, television coverage, and more.5 Based on the game

5 In addition to NFL.com, secondary websites included Sports Illustrated (sportsillustrated.cnn.com), ESPN (espn.go.com), and ProFootball Weekly (profootballweekly.com).

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date, we identified the first subsequent day of stock market activity for the sponsoring firms.

Sunshine Forecasts’ database was used to identify historic bidding spreads for each game. Our

sample contains 2,669 games (1,346 home games) during the pre-season, regular season, and post-

season. The sample covers 20 NFL teams and 23 sponsoring companies. Table A1 lists the sample

teams, stadiums, and sponsoring companies. Table A2 shows the home, away, Monday night, and

post-season game distribution over the sample period.

We combine the NFL data with CRSP and Compustat to draw accounting variables and stock

return data on the sponsoring companies. Table 1 presents descriptive statistics for the sponsoring

companies and for all CRSP/Compustat firms over the ten-year sample period. Not surprisingly,

firms that sponsor home stadiums of NFL teams are typically much larger than the average firm,

have less growth opportunities (indicated by higher book-to-market ratios), and higher leverage

ratios. Sponsoring firm are also highly traded and highly liquid (trading volume, Amihud’s

illiquidity measure, and bid-ask spread, all are significantly different than that of the average firm).

Mean daily stock returns of sponsoring firms are not substantially different than those of the full

sample, but are less volatile (measured by the standard deviation of daily stock returns over a

month), whereas market beta is somewhat higher for sponsoring firms.

3. Effect of home-game outcome on sponsoring firm return

3.1 Calculating abnormal returns

To measure the effect of game outcome on the stock price of the stadium’s sponsoring firm,

we estimate the abnormal return in the first trading day after the game (hereafter referred to as

‘post-game day’). The firm’s abnormal stock return is the difference between its raw return and its

expected return for that day. To mitigate the sensitivity of the results to a specific model of

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expected return, we employ five different models that are commonly used in the literature (for

detailed analyses of the models see Brown and Warner (1985) and Barber and Lyon (1997)). The

first is mean-adjusted model: expected return is estimated by averaging the firm’s raw returns

during the past 250 trading days prior to the game (see, for example, Handjinicolaou and Kalay

(1984); Kato and Loewenstein (1995); Maxwell and Stephens (2003); and Bessembinder, Kahle,

Maxwell, and Xu (2009)). The second is market-adjusted model: expected return is estimated by

the value-weighted market index on the post-game day (see Teoh, Welch, and Wong (1998b);

Ritter (1991); Kadiyala and Rau (2004); and Fidrmuc, Goergen, and Renneboog (2006)). The third

is market model: expected return is estimated by the fitted value of the stock return on the post-

game day, based on a regression of the sponsoring firm’s raw return on the value-weighted market

index return during the past 250 days prior to the game (see Lummer and McConnell (1989);

Campbell, Ederington, and Vankudre (1991); O'Hara and Wayne (1990); and Chang, Cheng, and

Yu (2007)). The fourth is reference portfolio: expected return is estimated by the equal-weighted

average return of firms in a size/book-to-market portfolio that includes the sponsoring firm;

portfolios are formed by first sorting all stocks into ten equal deciles according to the firm’s size

as of the beginning of the post-game day, and then within each decile, sorting all stocks into five

equal book-to-market quintiles (see Brav and Gompers (1997); Barber, Lyon, and Tsai (1999);

Datta, Iskandar-Datta, and Raman (2001); and Bouwman, Fuller, and Nain (2009)). The fifth is

matched (control) firm: expected return is measured by the return of a firm with the closest book-

to-market ratio within the same size decile as the sponsoring firm (see Teoh, Welch, and Wong

(1998a); Eberhart, Altman, and Aggarwal (1999); Hertzel, Lemmon, Linck, and Rees (2002);

Carlson, Fisher, and Giammarino (2006); Chhaochharia and Grinstein (2007); and Liu, Szewczyk,

and Zantout (2008)).

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3.2 Game samples

We consider three samples of home games that represent different levels of importance and

visibility. The first is the full sample of home games, which include all pre-season, regular season,

and post-season games (a total of 1,346 games over the period 2003-2012). We do not expect

strong results for the full sample. Most NFL games are played simultaneously with other games,

are not played in prime time hours, and are not nationally televised. Hence, the stadium sponsoring

companies are not visible to a large, national audience for a typical game. The situation is much

different however for a small group of selected games that provide very high visibility for the

sponsoring companies. These are the games that are played on Monday night, our second tested

sample (78 home games).

Monday night games are distinct. A game played on Monday night is the last game played in

the NFL week (Thursday to Monday), receives exclusive game-day publicity at a national level, is

always played in prime-time hours (typically at 8:30pm EST), and is nationally televised. In

addition, Monday night games are usually chosen based on the importance and the general interest

of the game. For many years ‘Monday Night Football’ has been one of the highest-rated television

shows. This means that sponsoring companies are more visible for games held on Monday night

in their stadiums, and thus are more likely to be affected by the outcome of the games. Important

to this study, there is also typically a 24 hour gap between the start time of the last game on Sunday

and the Monday night game. This allows the market reaction measured to be isolated from the

reaction to other NFL-week games.

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Our third sample includes games that are also very visible, but much more important – the

post-season (playoff) elimination games (48 home games). 6 As with Monday night games, post-

season games are stand-alone and typically nationally broadcasted by a major television network,

thus providing high visibility to a stadium’s sponsoring firm. But more importantly, post-season

games are the most meaningful games for the teams, as their outcome solely determines if the team

will continue to compete for the championship (in case it wins) or will be eliminated from the

competition (if it losses). The perception of a successful season versus a failed one is often

determined by a single post-season game. The outcome of post-season games therefore can have

a direct impact on the visibility of the sponsors in the rest of the season. We expect that the post-

season sample will exhibit the strongest results.

Table A2 shows the distribution of the games for the three samples. The games are fairly

uniformly distributed over the years, which provides a solid ground for our examination. That is,

the results are not likely affected by an unusual effect in a specific season, or by cross-sectional

dependence induced by same-day clustering (see Brown and Warner (1985)).

3.3 Post-game day abnormal return

We begin our empirical analysis by comparing the abnormal stock returns of sponsoring

companies after home-game wins and losses of their sponsored teams. Table 2 presents the

abnormal returns according to the five models described above for the three samples. All returns

are reported in percent terms. As expected, when all home games are included in the sample, the

results do not indicate any abnormal return after wins or losses. In fact, the average abnormal

6 The post-season sample does not include the Super bowl game as it is played in a neutral stadium.

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return after a loss of the home team is higher by 17 basis points than that after a win, yet is not

statistically significant.

The results show a strong effect for highly visible and important games. After Monday night

games, the sponsor of the home team typically earns a positive abnormal return if the team had

won, an average of 0.56 percent across all models with a t-statistic of 2.02, and no abnormal return

if the team had lost. Although the average difference between the win and loss post-game day

abnormal returns is large, its t-statistic is only 1.34. This is partially due to the relatively small

sample of Monday night games (74 home games). Examining the abnormal return models

separately reveals that four models show fairly strong effect (differences in win-loss abnormal

returns of 51 to 100 basis points) while the fifth one, the market-model, does not show significant

difference in abnormal returns (6 basis points). We do not have an explanation for why this specific

model exhibit different results than the other models.

For post-season games the difference is even greater. Averaging across all models, the losing

teams’ sponsors earn an abnormal return lower by 0.92 percent than that of the winning teams’

sponsors (t-statistic of 1.70). All five abnormal return models show strong results (differences in

win-loss abnormal returns of 62 to 118 basis points). Unlike the Monday night games, the post-

season effect seems driven by negative returns, as the average the post-game day abnormal return

is negative both after wins and losses (-0.23 and -1.16 percent, respectively). This can be explained

by the crucial effect a payoff game loss has on the team. A playoff loss eliminates the team from

the playoff contention, effectively ending the team’s season. A playoff win only guarantees to keep

the team in the competition for one more game. While wins and losses in the regular season serve

to seed the team for playoff matchups, wins and losses in the post-season ultimately determine the

success or failure of the team’s entire season. The post-season results may also indicate that there

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is a surprise effect. As a playoff game is typically played at the stadium of the team with the better

record, there is a greater expectation that the home team will win in a post-season game; thus a

win does not have much effect while a loss leads to a significant negative return of the home team's

sponsor.7

These mean differences in abnormal returns after a win and a loss of home games in sponsored

stadiums are meaningful. Clark, Cornwell, and Pruitt (2002) find that the sponsorship agreement

announcement increases the sponsor’s stock price by 1.65 percent on average in the four major

sports. We show that the outcome of a single game affects the sponsor’s stock by an average return

of 0.56 percent (Monday night games) and 0.92 percent (post-season games). These effects are

also comparable and stronger than those reported in Edmans et al. (2007). They find that at the

national level a loss in the soccer World Cup elimination stage leads to a next-day abnormal stock

return of -0.49 percent, whereas a win does not leads to a significant positive return.

We re-examine the impact of game outcomes on the sponsors’ stock returns using a regression

approach. For the three samples of home games we regress the post-game day abnormal stock

return of the sponsoring firm on a dummy variable that equals one if the sponsored home team had

won and zero if it had lost. This regression offers the advantage that we can control for other well-

known determinants of stock returns and thus check for the marginal influence of the game

outcome on the sponsor's stock return. The control variables are the natural log of the sponsoring

firm’s size, its book-to-market ratio (both as of the beginning of the post-game trading day), and

its abnormal stock return on the trading day prior to the game.

7 Note that although post-season games are played during the weekend, their post-game day negative abnormal returns are not driven by the weekend effect (see, French (1980)). This is because for most models of abnormal return, the expected returns are estimated from Monday returns as well.

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The regression results reported in Table 3 are consistent with the effects reported in Table 2

(all reported coefficients are multiplied by 100). The full sample of games does not show any

significant effect of a win over a loss of the home team. For Monday night games, the outcome of

the game significantly affects the sponsoring firm stock price. The coefficient of the win dummy

variable across all models is 0.806 (t-statistic of 2.13). That is, the marginal effect of a win of the

home team over a loss on the sponsor’s stock return is 81 basis points, compared to an equivalent

effect 56 basis points reported in Table 2. As in Table 2, out of the five models of abnormal returns,

the market model is the only one that does not show a significant effect. The post-season games

sample also exhibits a strong impact of a win of the home team over a loss. Averaging across all

models yields a marginal post-game day abnormal return of 90 basis points (t-statistic of 1.63),

which is very similar to the equivalent effect reported in Table 2 of 92 basis points.

Both Table 2 and 3 therefore indicate that stock returns to the sponsoring firms are substantially

affected by the outcomes of highly visible and important games played in their stadiums.

3.4 Cross-sectional determinants

We explore cross-sectional effects on the association between home-game outcomes and

sponsoring firms’ stock returns. We test the influence of a set of variables that represent three

aspects. The first aspect is sponsorship characteristics. As shown in Table A1, the magnitude of

the sponsorship agreement varies over a wide range - from a total value of 6.2 million dollars

(Alltel Corporation sponsoring the home stadium of the Jacksonville Jaguars) to 400 million

dollars (MetLife Inc. sponsoring the home stadium of the New York Giants and the New York

Jets). Large variation is also observed in the horizon of the sponsorship agreements and the average

annual price for the stadium naming rights. Because the size and horizon of the sponsorship

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agreement are likely to indicate higher importance of this agreement for the sponsoring company,

we expect these sponsorship characteristics will strengthen the impact of home game wins over

losses on the sponsors’ stock prices. We also expect that the stock price of sponsoring companies

will be more sensitive to game outcomes in the early years of the sponsorship, due to a higher

uncertainty of the partnership with the team in these years.

The second aspect we examine is the market size of the team. When more people follow and

support an NFL team (e.g., watching and attending games or buying firm merchandises), then the

team's performance should have a stronger impact on its sponsor. We use two proxies for the

market size of the team. The first proxy is the media market of the team, measured by population

in TV households within a 75-mile radius of the team’s stadium.8 The second proxy is the market

value of the team, reasoning that larger teams attract more attention, not just at the local level, but

also nationally. Estimates of NFL team market values are obtained from Forbes.9

The third aspect is the element of surprise in the game outcome. The results above are based

on sorting all games into outright wins and losses. The outright outcome of the game ultimately

determines the prospect of the success of the team, and thus affects also the value of the team's

sponsor. In addition, any game’s outcome contains new information; i.e., even if the win/loss is

expected, it is not guaranteed, and thus the actual outcome is important for the team. Yet, if the

win/loss is unexpected, it creates news of larger magnitude for the team and thereby for its sponsor.

An unexpected win or loss can be viewed as any other corporate news that carries value for the

company, and thus can have stronger stock market reaction. To capture the extent to which a game

8 The NFL defines a team's "local" market as all the TV markets that lie within a 75-mile radius of the stadium. 9 See Forbes.com, “NFL Team Values: The Business of Football”, August 14, 2013.

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outcome is unexpected we use two measures. The first is the betting spread for the game and the

second is the number of consecutive wins prior to the game (truncated above at 3 wins).

We test the effect of all characteristics by regressing the post-game day abnormal stock return

to the sponsoring firm on an interaction term between the win dummy variable and each

characteristic. We use the same control variables of log size, book-to-market ratio, and pre-game

abnormal return. Table 4 presents the results. We list the predicted sign of each interaction term

next to the characteristic. The sponsorship size and annual naming rights investment seem to have

a positive effect on the impact of games outcomes for the full sample and post-season games, as

expected, but not for Monday night games. The horizon of the sponsorship does not show any

effect on the results. The tenure of the sponsorship has a negative effect on the impact of game

outcomes for Monday night games and especially for post-season games, which is consistent with

our argument. These results therefore confirm that the sponsorship characteristics play a role in

the relation between game outcome and sponsor stock return.

Team characteristics do not seem to have a meaningful effect on the results. There is only some

evidence of weak effect of team value for the full games sample and media market for post-season

games. The results indicate that the effect of win/loss in post-season home games is stronger if the

outcome was unexpected; t-statistics of -1.88 and -1.27 for the coefficients of betting spread and

prior consecutive wins, respectively. Yet, no significant effect is found for the full sample and

Monday night games. This is consistent with Scholtens and Peenstra (2009) who find that the value

of publically traded European soccer teams has similar sensitivity to expected and unexpected

outcome of their games.

19

3.5 Rational expectations versus investor sentiment

The findings above raise the question regarding the mechanism driving the impact of home-

game outcomes on the sponsoring firms’ stock price. On the one hand, outcomes of important

games can have real financial implications for the sponsoring companies. A team that just lost an

important regular season game that affects its chances to compete for the championship in the

current season will naturally attract less attention in the remainder of the season. And a team that

just lost a post-season elimination game ended the NFL-affiliated media mentions of their stadium,

effectively ending the sponsor’s naming rights campaign until the next game is played in the

stadium. These losses mean reduced media coverage, TV ratings, home game attendance, and

demand for team products – all of which should affect the sponsoring firm’s future cash flows. A

reverse effect is expected had the firm won the game. A rational expectations argument suggests

therefore that the post-game day change in the sponsor’s stock price reflects changes in expected

cash flows.

On the other hand, outcomes of sport events are correlated with sudden change in investor

mood, which is often reflected in stock price movements (see Boyle and Walter (2002) and Edmans

et al. (2007)). This argument can apply specifically to the NFL due to its high popularity and the

strong emotions it generates. For example, White (1989) documents that elimination from NFL

playoff games leads to a significant increase in homicides in the cities following the games. The

question is, therefore, whether the sentiment of the team’s fans is reflected in the team’s sponsor

stock prices.

The results so far provide some indication for whether the post-game day abnormal stock

return to the sponsoring firm is driven by rational expectations or investor sentiment. For example,

the evidence that outcomes of regular season home games affect sponsors’ return only for Monday

20

night games is more consistent with the investor sentiment argument. This is because the main

difference between Monday night games and all other regular season games is visibility, not the

level of importance. That is, if two games are equally important for their teams, they should create

the same impact on the sponsoring firms’ value. But if the more visible game generates a stronger

impact, it is likely driven by correlated change in fan sentiment towards the team and thereby its

sponsor. In contrast, the evidence that outcomes of post-season elimination games create stronger

impact than those of Monday night games is consistent with the rational expectations argument.

This is because both types of games are very visible, but the post-season games are, on average,

much more important than Monday night games (and any other regular season games).

To further explore the drivers for the effect of match outcomes on sponsors’ stock returns, we

employ two tests. First, we look at the return on the sponsors' stock after wins/losses in away

games (games played at the opponent's stadium). Rational expectations suggest a similar effect of

wins/losses in away games and home games, as both should have similar implications on the team

and the sponsor for the remaining of the season. Behavioral bias suggests a stronger effect for

home games; this is because the sponsoring company is more visible during the game, and thus

investors associate the game outcome with the sponsoring firm.

Table 5 replicates the main results of Tables 2 and 3 for away games. Both mean difference

and regression results indicate that outcomes of away games played on Monday night do not lead

to any abnormal return on the sponsors’ stocks as they do for home games, which is inconsistent

with rational expectations. The post-season sample, however, shows some effect also in away

games, albeit weaker than that found in home games. The average difference in post-away game

abnormal return between wins and losses is 0.36 to 0.63 percent, compared to 0.90 to 0.92 for

21

home games. This suggests that the effect of post-season games outcome is driven mostly by

rational expectations and only to some extent by investor sentiment.

Second, we examine whether the post-game day abnormal return tends to reverse in the

following days, as evidence of initial overreaction. The abnormal return in subsequent days yields

very interesting pattern (displayed in Figure 1). For Monday night games, the first-day positive

difference in abnormal return after wins and losses is almost completely reversed in the next four

days, which is more consistent with first-day overreaction to the game outcome. In contrast, for

post-season games, the next four days continue to exhibit a positive difference in abnormal return

after wins and losses; this pattern is more consistent with first-day underreaction to game outcome.

Although both these patterns suggest possible behavioral biases, the change in the sponsor stock

price following post-season games seems more real, as the price does not tend to reverse in

subsequent days as in the case of Monday night games. Note that this distinction is consistent with

the conclusions drawn from the prior results, relating the effect of Monday night games to their

high visibility, whereas the impact of post-season games is coming primarily from the importance

of these games.

4. Team performance-based trading strategies

Given the price impact that game outcomes generate, we examine the existence of profitable

trading opportunities embedded in the patterns of post-game stock returns. We begin with a week-

by-week trading strategy based on game outcomes in the recent week, and we continue to explore

season-wide trading based on team performance during the recent season.

22

4.1 Post-game trading

The findings above suggest that the reaction to game outcome is not limited to the next trading

day only, but rather seems to continue in the following days, both for Monday night and post-

season games. Although the two samples exhibit different return patterns around the game (i.e.,

the Monday night next-day abnormal return tends to reverse eventually), in both cases the initial

abnormal return after a win over a loss continues in the same direction in the next few days.

Assuming that the next-day abnormal return cannot be realized by investors, we examine whether

one can earn abnormal profits by trading sponsors’ stocks in the subsequent days.

Because a profitable trading rule requires a sufficiently large number of traded securities at the

moment of time, we do not limit our examination to the Monday night and post-season games

because they cannot provide more than one (Monday night) or only a few (post-season) games in

a given week. We rather consider three large groups of games. The first group contains all games

as an initial general check. The second group contains all home games, motivated by the strong

impact of games played in the sponsored stadium. The third group contains all home games whose

outcome is most unpredictable. We consider this group because any outcome in these games, win

or loss, contains some element of news that can lead to sharper stock market reaction. We classify

games as unpredictable if the pre-game betting spread for the game score is no higher than 3 in

absolute value.10

We examine the performance of the following long-short investment strategy. Every week

during the NFL season we form a portfolio of buying the stocks of all sponsoring companies whose

teams won that week, and selling the stocks of all sponsoring companies whose teams lost that

10 A cutoff of 3 is natural as it represents the points awarded for a field goal in an NFL game.

23

week. We hold this portfolio from the second trading day after the game until the fifth trading day.

We perform both equal-weighted portfolios and value-weighted portfolios according to the

sponsoring firm’s size (measured by market value of equity).

Table 6 shows the portfolios’ mean excess daily returns (in excess of the risk-free rate) and

alphas from factor models over the years 2003-2012. The CAPM one-factor model uses the market

factor. The three factors in the 3-factor model are the Fama and French (1993) factors. The four

factors in the 4-factor model are the Fama-French factors augmented with a momentum factor. All

factor returns are downloaded from Kenneth French’s website. All returns and alphas are in percent

per day.

When applied to all games, the portfolio does not generate abnormal returns, which is

consistent with the price movement observed in Figure 1. The sample of all home games shows

some evidence of abnormal profit; the mean daily excess return is around 0.1 percent per day, and

factor-model alphas are fairly similar, where the t-statistics are 1.05 to 1.37. The results are very

strong when the investment is applied to unpredictable home games. The mean excess return is

0.35 to 0.44 percent per day and factor-model alphas are similar at 0.34 to 0.43 percent (all t-

statistics are between 2.30 and 2.44). Considering that the latter sample consists of an average of

33 trading days in a season, this investment strategy yields an abnormal profit of 12 to 15 percent

per year. The magnitude of this profit is comparable and even higher than those generated by the

most puzzling return patterns, such as momentum (see Jegadeesh and Titman (1993)) and distress

effect (see Dichev (1998); Campbell, Hilscher, and Szilagyi (2008)). These findings suggest

therefore that the observed daily abnormal returns following home games, especially unpredictable

ones, can be translated into profitable trading opportunities.

24

4.2 Post-season trading

The results above show that outcomes of single games can lead to patterns of abnormal stock

returns to the sponsoring firms. We explore the stock market reaction to the team's overall

performance during an entire season. The NFL season is relatively short, about four months

(typically September to January). The performance of a team in a certain season has important

implications for the long off-season period. Implications include reputational effects (both for the

team directly and for the managers, coaches, and players affiliated with the team), probability of

future success (particularly regarding player retention and draft placement), and the fans’ mood.

We examine whether a team's performance during a season is carried to its sponsor's stock returns

in the post-season period.

We perform a simple trading rule. In the beginning of March of every year over the period

2003-2012, we form a portfolio of buying the stocks of all sponsoring companies whose team had

a “successful” season and selling the stocks of all sponsoring companies whose team had a “non-

successful” season. We use three criteria for classifying a team’s season as successful. The first is

if the team has a winning record (i.e., more wins than losses) in the last season. The second is if

the team’s record has improved in the last season (number of wins has increased from the previous

season). And the third is if the team participated in the playoff games. We form both equal-

weighted and value-weighted portfolios with holding periods of three and six months.

Table 7 shows the portfolios’ mean excess monthly returns and alphas from factor models over

the years 2003-2012. While the equally weighted portfolios do earn a significant profit, the value-

weighted portfolios exhibit surprisingly strong results: mean excess return of 1.7 percent per month

and factor-model alphas of 1.8 percent averaging across all three performance criteria and two

investment horizons. The profits are particularly high for 6-month investment horizon using the

25

playoff participation as the performance measure: mean excess return of 2.2 percent per month

and factor-model alphas of 2.4 percent. This implies abnormal profits 14 to 15 percent per off-

season period. The performance of NFL teams during a season therefore provides a substantial

predictive ability over the team sponsors’ stock returns during the off-season period.

4.3 Discussion

The sources of anomalous patterns in stock return are typically assessed in terms of risk and

mispricing. As shown in Table 7, standard models of risk, such as the three and four-factor models

have difficulty in explaining the variation in returns associated with game outcomes and season-

wide performances. There is always a considered possibility that unknown risks factors drive the

results. However, as high risk implies high expected return, we find it unlikely that sponsors of

teams that just finished a successful season become more risky than sponsors of unsuccessful

teams.

The mispricing argument implies that it take investors several months to realize the true value

of the benefits (costs) to the sponsoring firm after a successful (unsuccessful) season of its

sponsored team. That is, the season-wide effect is driven by systematic market underreaction. This

behavioral-bias argument has been previously considered as possible driver of stock return

anomalies (see, for example, Ikenberry, Lakonishok, and Vermaelen (1995); Barberis, Shleifer,

and Vishny (1998)). Yet, we should take into account that our sample contains a period of ten

years in which the number and scale of sponsorship has increased. Hence, the high returns to the

successful teams’ sponsoring companies during the off-season can reflect new ways the sponsors

find to capitalize on the successful season, which are not expected at this stage by the market.

26

Another important aspect of the season-wide effect, as well as the post-game effect, is that it

involves only very large companies. These companies are typically highly traded, highly liquid,

have less information asymmetry, and are strong financially. This means that the portfolios’ profits

document above are not likely driven by market frictions and are easier to implement, compared

to other pricing effects. For example, Griffin and Lemmon (2002) document that the value

anomaly is stronger among distressed stocks; Avramov, Chordia, Jostova, and Philipov (2007)

points to momentum being present mostly among low credit rating stocks; and Eisdorfer (2008)

shows that approximately 40 percent of the momentum profit is generated by delisting returns. The

NFL teams’ stadium sponsors are not likely to default or be delisted from the stock exchange. In

addition, the profits generated by anomalous stock return portfolios typically require massive short

selling, which is not always feasible for many stocks (e.g., low liquid stocks, distressed stocks). In

that respect, therefore, it is fairly easy to form the long-short NFL sponsors’ portfolios.

5. Conclusions

We document that stock prices of companies that sponsor NFL stadiums are affected by the

outcomes of important games played in the stadiums. Employing five different models of abnormal

return our results show that the mean difference between next-day return after a win and after a

loss of the home team is 56 basis points for Monday night games and 92 basis points for post-

season elimination games. This effect is stronger for large-scale sponsorships and in the early years

of the sponsorship. The team’s market value as well as the size of a team’s media market have

only a marginal effect, and the extent to which a game outcome was unexpected affects the results

only for post-season games.

27

We explore whether the post-game day abnormal returns represent rational changes in

expectations of future cash flows, or overreaction by investors associating the team’s performance

with its sponsoring company. Evidence suggests that the impact of home game outcomes is

partially driven by investor sentiment, mainly for Monday night games; the first-day abnormal

return is almost completely reversed in the next four days, and the outcomes of away games do

not generate any abnormal returns.

We investigate whether the post-game return patterns provide profit opportunities. We form a

weekly portfolio of buying the stocks of all sponsoring companies whose teams won that week,

and selling all sponsoring companies whose teams lost that week. Applying this investment

strategy to home games considered as unpredictable (using pre-game betting spread data)

generates mean excess return and factor-model alphas of 12 to 15 percent per season.

We further find that the performance of a team during the entire season is carried to the

sponsoring company’s value after the season. Off-season long-short portfolios of stadium sponsors

of teams that had successful and unsuccessful seasons yield abnormal profit of 2 percent and higher

per month. This is comparable and even higher than the profits generated by the most puzzling

return patterns, such as momentum and distress effects. In addition, the sponsor-based trading

strategies involve only very large, highly traded, and highly liquid stocks, and thus are less affected

by market frictions and easier to implement.

28

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Table 1. Descriptive statistics

The table presents descriptive statistics for the sample of only sponsoring firms and for all CRSP/Compustat firms over the ten-year period 2003-2012. For all variables, observations outside the top and the bottom percentiles are excluded. Size is market equity value (in millions of dollars). Book-to-market is book equity value divided by market equity value. Book leverage is the ratio of book value of total debt to book value of total assets. Stock return is the daily return over the sample period, and Stdev of stock returns is the standard deviation of the daily stock returns in a calendar month (both are reported in percent terms). Market beta is measured by regression of stock return on market return over the past 60 months. Trading volume is the monthly volume over the sample period (in thousands). Amihud’s illiquidity is the monthly average of daily ratios of absolute return to dollar trading volume (in millions). Bid-ask spread is the difference between the stock’s closing ask price and bid price, divided by the average bid and ask prices (reported in percent terms).

Sponsors sample (23 firms) Full sample (11,621 firms) Mean Median Stdev Mean Median Stdev Size 29,842.3 13,446.6 45,342.5 2,484.8 301.1 7,338.1 Book-to-market ratio 0.908 0.464 2.232 0.781 0.509 1.001 Leverage ratio 0.287 0.311 0.200 0.195 0.142 0.205 Daily stock return 0.052 0.022 2.304 -0.081 0.000 3.119 Stdev of stock returns 2.156 1.690 1.650 2.754 2.141 2.196 Market beta 1.248 1.034 0.836 1.132 0.997 0.930 Trading volume 203.87 49.71 557.70 16.57 2.94 40.30 Amihud’s illiquidity 0.023 0.002 0.113 6.291 0.170 24.314 Bid-ask spread 0.116 0.052 0.305 0.923 0.251 1.841

32

Table 2. Post-home game day abnormal returns

The table presents the average abnormal stock returns of NFL stadium sponsoring companies in the first trading day after a home game of their sponsored teams over the ten-year period 2003-2012. Abnormal returns are presented for all home games and separately for wins and for losses of the home teams, within three samples: full sample games (including all pre-season, regular season and post-season games), regular season games played on Monday night, and all post-season (playoff) games. Abnormal return is the difference between the raw return and the expected return, as measured by five models: (i) Mean-adjusted return model; expected return is measured by the average stock return in the past 250 days prior to the game. (ii) Market-adjusted model; expected return is measured by the value-weighted market index return on the post-game day. (iii) Market model; expected return is measured by the fitted value of the stock return on the post-game day, based on market model regression estimated in the past 250 days prior to the game. (iv) Reference portfolio; expected return is measured by the equal-weighted average return of firms in the same size/book-to-market portfolio. (v) Matched firm; expected return is measured by the return of a firm with the closest book-to-market ratio within the same size decile as the sponsoring firm. The table also reports the average of all five measures of abnormal return. All returns are in percent and t-statistics are in parentheses.

Model of abnormal return N Mean- Market- Market Reference Matched Average

adjusted adjusted model portfolio firm Abn return Full sample games All games 1,346 0.104 0.063 0.042 0.063 0.042 0.065 (0.95) (0.74) (0.52) (0.78) (0.42) (0.75) Wins 808 0.040 -0.023 -0.029 -0.012 0.008 -0.003 (0.27) (-0.19) (-0.24) (-0.10) (0.06) (-0.02) Losses 538 0.199 0.194 0.148 0.175 0.095 0.167 (1.30) (1.74) (1.45) (1.72) (0.69) (1.51) Win-Loss difference -0.159 -0.217 -0.176 -0.187 -0.087 -0.170

(-0.72) (-1.25) (-1.06) (-1.14) (-0.42) (-0.96) Monday night games

All games 74 0.591 0.290 0.125 0.218 0.327 0.310 (1.74) (1.35) (0.70) (1.07) (1.07) (1.49) Wins 41 1.040 0.516 0.152 0.471 0.626 0.560 (2.06) (1.68) (0.65) (1.66) (1.69) (2.02) Losses 33 0.037 0.009 0.092 -0.096 -0.045 -0.001 (0.09) (0.03) (0.33) (-0.34) (-0.09) (0.00) Win-Loss difference 1.000 0.507 0.060 0.567 0.671 0.561

(1.48) (1.18) (0.17) (1.40) (1.09) (1.34) Post-season games

All games 48 -0.695 -0.546 -0.531 -0.511 -0.321 -0.521 (-2.06) (-1.87) (-2.21) (-2.12) (-0.93) (-2.03) Wins 33 -0.392 -0.208 -0.293 -0.316 0.047 -0.232 (-1.63) (-0.90) (-1.48) (-1.51) (0.18) (-1.28) Losses 15 -1.360 -1.290 -1.060 -0.939 -1.130 -1.160 (-1.44) (-1.68) (-1.68) (-1.51) (-1.21) (-1.64) Win-Loss difference 0.970 1.080 0.762 0.623 1.180 0.923

(1.34) (1.75) (1.49) (1.20) (1.60) (1.70)

33

Table 3. Regression of post-home game day return on game win

The table presents regressions of NFL sponsoring companies’ abnormal stock return in the first trading day after a home game of their sponsored teams over the ten-year period 2003-2012. The independent variables are a dummy variable that equals one if the team won and zero if it lost, (log) firm size, book-to-market ratio, and the abnormal return in the trading day prior to the game. Regression results are presented for all home games, and separately for wins and for losses of the home teams, within three samples: full sample games (including all pre-season, regular season, and post-season games), regular season games played on Monday night, and all post-season games. Abnormal return is measured using the five models described in Table 2. The table also reports the coefficient on the average abnormal return. All coefficients are multiplied by 100 and t-statistics are in parentheses.

Model of abnormal return Mean- Market- Market Reference Matched Average adjusted adjusted model portfolio firm Abn return

Full sample (N=1,346) Intercept 3.671 2.703 1.878 2.413 2.915 2.751 (2.78) (2.61) (1.90) (2.49) (2.42) (2.64) Win dummy -0.130 -0.193 -0.156 -0.167 -0.039 -0.146 (-0.59) (-1.11) (-0.94) (-1.03) (-0.19) (-0.83) Log size -0.209 -0.150 -0.104 -0.134 -0.169 -0.154 (-2.65) (-2.43) (-1.76) (-2.32) (-2.35) (-2.48) Book-to-market -0.044 -0.035 -0.024 -0.032 -0.038 -0.035 (-1.27) (-1.29) (-0.93) (-1.26) (-1.21) (-1.28) Lag abnormal return -2.085 -9.037 -11.327 -10.820 -24.787 -16.520

(-0.47) (-2.05) (-2.57) (-2.51) (-7.01) (-3.77) R-square 0.006 0.008 0.008 0.009 0.039 0.015

Monday night (N=74) Intercept 10.216 4.621 1.520 5.587 7.970 6.126 (2.65) (1.89) (0.70) (2.34) (2.23) (2.62) Win dummy 1.124 0.696 0.168 0.823 1.106 0.806 (1.80) (1.76) (0.48) (2.13) (1.91) (2.13) Log size -0.605 -0.270 -0.077 -0.335 -0.473 -0.361 (-2.60) (-1.83) (-0.59) (-2.33) (-2.19) (-2.56) Book-to-market -0.266 -0.190 -0.147 -0.211 -0.255 -0.216 (-2.90) (-3.21) (-2.79) (-3.62) (-2.89) (-3.84) Lag abnormal return -31.065 -29.942 -14.089 -17.431 -24.209 -24.760

(-3.32) (-3.59) (-1.54) (-1.91) (-2.34) (-2.95) R-square 0.274 0.254 0.140 0.201 0.216 0.273

Post-season (N=48) Intercept 2.238 3.665 2.882 4.803 -3.234 1.970 (0.45) (0.90) (0.79) (1.31) (-0.60) (0.51) Win dummy 0.660 1.015 0.676 0.696 1.342 0.898 (0.93) (1.73) (1.27) (1.31) (1.75) (1.63) Log size -0.176 -0.273 -0.217 -0.333 0.115 -0.171 (-0.62) (-1.17) (-1.03) (-1.59) (0.37) (-0.78) Book-to-market -0.372 -0.373 -0.273 -0.229 -0.022 -0.261 (-1.76) (-2.18) (-1.76) (-1.50) (-0.10) (-1.62)

34

Lag abnormal return 35.498 49.321 17.455 26.242 29.236 34.154 (1.85) (2.74) (1.00) (1.53) (1.53) (1.81) R-square 0.186 0.273 0.124 0.146 0.105 0.167

35

Table 4. Determinants of the effect of home game outcome on sponsor stock return

We run the following regressions of NFL stadium sponsoring companies’ abnormal stock return (AR) in the first trading day after a home game of their sponsored teams over the ten-year period 2003-2012:

��� = � + ���� + �� � + ���� � + �����(���)� + ������ + �������� + ��

where ‘win’ is a dummy variable indicating a win of the home team and X is a characteristic of the team/sponsoring company. The remaining control variables are as in Table 3. We use eight characteristics for X: Sponsorship size is the total value of the sponsorship; Sponsorship horizon is the number of years of the sponsorship agreement; Annual investment is the sponsor’s average annual investment during the sponsorship term; Tenure is the number of years since the beginning of the sponsorship; Media market is the size of the media market associated with the sponsored team; Team value is the market value of the NFL team; Betting spread is the pre-game betting spread predicting the game outcome; and past win run is the number of consecutive wins prior to the game (truncated above at 3 wins). The predicted sign of each interaction term is listed in parenthesis next to the characteristic. The results are presented for the five models of abnormal return described in Table 2, and for the average return across all models, and separately for three samples: full sample games (including all pre-season, regular season, and post-season games), regular season games played on Monday night, and all post-season games. The table reports only the coefficient of the interaction terms(��). All coefficients are multiplied by 100 and t-statistics are in parentheses.

Model of abnormal return Mean- Market- Market Reference Matched Average adjusted adjusted model portfolio firm Abn return

Full sample (N=1,346) Win*Sponsorship size (+) 0.004 0.004 0.004 0.004 0.004 0.004 (1.73) (2.07) (2.20) (2.15) (1.76) (2.09) Win*Sponsorship horizon (+) 0.036 0.039 0.039 0.036 0.021 0.034 (0.94) (1.28) (1.36) (1.28) (0.59) (1.11) Win*Annual investment (+) 0.100 0.095 0.101 0.094 0.120 0.109 (1.38) (1.66) (1.84) (1.76) (1.71) (1.78) Win*Tenure (−) 0.004 0.006 0.015 -0.001 -0.014 0.001 (0.06) (0.12) (0.31) (-0.01) (-0.25) (0.03) Win*Media market (+) 0.028 0.005 0.005 0.000 -0.028 -0.002 (0.40) (0.10) (0.10) (0.00) (-0.40) (-0.00) Win*Team value (+) 1.258 0.734 0.888 0.675 1.098 0.928 (1.56) (1.14) (1.45) (1.12) (1.47) (1.43)

Win*Betting spread (−) -0.040 -0.052 -0.055 -0.058 -0.059 -0.053 (-0.80) (-1.30) (-1.50) (-1.60) (-1.30) (-1.30) Win*Past win run (−) -0.186 -0.021 -0.003 0.018 0.319 0.013 (-0.86) (-0.13) (-0.02) (0.12) (1.62) (0.08)

Monday night (N=74) Win*Sponsorship size (+) 0.002 0.001 0.000 -0.001 0.010 0.003 (0.28) (0.11) (-0.04) (-0.22) (1.54) (0.57) Win*Sponsorship horizon (+) -0.119 -0.029 0.019 -0.093 0.128 -0.015 (-0.97) (-0.36) (0.27) (-1.23) (1.10) (-0.20) Win*Annual investment (+) 0.116 0.058 0.018 0.030 0.352 0.119 (0.57) (0.44) (0.16) (0.24) (1.89) (0.95)

36

Win*Tenure (−) -0.233 -0.102 -0.033 -0.149 -0.065 -0.118 (-1.50) (-1.01) (-0.36) (-1.52) (-0.43) (-1.23) Win*Media market (+) 0.079 -0.028 -0.050 -0.021 0.035 0.003 (0.47) (-0.25) (-0.52) (-0.19) (0.22) (0.03) Win*Team value (+) -0.209 -0.473 -0.330 -0.899 3.523 0.334 (-0.09) (-0.31) (-0.25) (-0.60) (1.63) (0.23)

Win*Betting spread (−) -0.010 -0.013 -0.022 0.023 -0.085 -0.021 (-0.09) (-0.18) (-0.35) (0.32) (-0.81) (-0.30) Win*Past win run (−) -0.344 -0.169 -0.267 -0.024 1.220 0.076 (-0.53) (-0.39) (-0.68) (-0.06) (1.93) (0.18)

Post-season (N=48) Win*Sponsorship size (+) 0.018 0.012 0.011 0.017 0.017 0.015 (1.19) (1.12) (1.11) (1.81) (1.04) (1.43) Win*Sponsorship horizon (+) -0.246 0.069 -0.096 0.022 -0.403 -0.131 (-1.01) (0.36) (-0.55) (0.14) (-1.61) (-0.72) Win*Annual investment (+) 0.455 0.246 0.264 0.350 0.510 0.369 (1.48) (1.09) (1.26) (1.78) (1.55) (1.69) Win*Tenure (−) -0.442 -0.353 -0.378 -0.394 -0.735 -0.456 (-2.20) (-2.15) (-2.61) (-2.79) (-3.62) (-3.12) Win*Media market (+) 0.349 0.093 0.151 0.206 0.279 0.214 (1.39) (0.49) (0.87) (1.22) (1.00) (1.17) Win*Team value (+) 0.574 -2.141 -1.030 -0.729 2.134 -0.260 (0.21) (-1.09) (-0.56) (-0.41) (0.72) (-0.13)

Win*Betting spread (−) -0.354 -0.196 -0.217 -0.233 -0.233 -0.245 (-2.10) (-1.40) (-1.73) (-1.87) (-1.24) (-1.88) Win*Past win run (−) -0.994 -0.635 -0.489 -0.307 -0.392 -0.572 (-1.72) (-1.33) (-1.13) (-0.71) (-0.61) (-1.27)

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Table 5. Comparison of abnormal return after home and away games

The table replicates the main results of Tables 2 and 3 for away games. The second column shows the mean difference in the sponsoring companies’ abnormal stock returns after home game wins and after home game losses, averaged across the five models of abnormal return described in Table 2. The fifth column shows similar estimates for away games. The third column shows the coefficient of a win dummy variable (as described in Table 3) for the regression of the five-model average abnormal return. The sixth column shows the win dummy coefficient for the sample of away games. The sample period is 2003-2013, returns are in percent terms, regression coefficients are multiplied by 100, and t-statistics are in parentheses.

Five models average abnormal return Home games Away games N Win-Loss Win dummy N Win-Loss Win dummy difference coefficient difference coefficient

Full sample 1,346 -0.170 -0.146 1,323 -0.060 -0.055 (-0.96) (-0.83) (-0.50) (-0.46) Monday night 74 0.561 0.806 73 -0.112 -0.098 (1.34) (2.13) (-0.27) (-0.24)

Post-season 48 0.923 0.898 44 0.626 0.364 (1.70) (1.63) (0.62) (0.34)

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Table 6. Post-game trading rule

Every week during the NFL season we form a portfolio of buying the stocks of all sponsoring companies whose teams won that week and selling the stocks of all sponsoring companies whose teams lost that week. We hold the portfolio from the second trading day after the game until the fifth trading day. We apply this zero-investment trading strategy to three samples: full sample games (including all pre-season, regular season and post-season games), full sample home games (games played at the sponsored team’s home stadium), and full sample home games where the outcome of the game is most unpredictable; we classify games as unpredictable if the pre-game betting spread is no higher than 3 in absolute value. We perform both equal-weighted portfolios and value-weighted portfolios according to the sponsoring firm’s size (measured by market value of equity). The table shows the portfolios’ mean excess daily returns (in excess of the risk-free rate) and alphas from factor models. The CAPM one-factor model uses the market factor. The three factors in the 3-factor model are the Fama and French (1993) factors. The four factors in the 4-factor model are the Fama-French factors augmented with a momentum factor. All returns and alphas are in percent per day and the corresponding t-statistics are in parentheses. The sample period is 2003 to 2012.

Full sample Full sample Unpredictable games Home games home games Total trading days 941 813 331 Equal- Value- Equal- Value- Equal- Value- weighted weighted weighted weighted weighted weighted Game win: excess return 0.080 0.108 0.048 0.053 0.217 0.274 Game loss: excess return 0.060 0.078 -0.045 -0.068 -0.131 -0.170 Win-Loss: excess return 0.020 0.030 0.094 0.121 0.348 0.443 (0.40) (0.52) (1.21) (1.37) (2.35) (2.44) Win-Loss: CAMP alpha 0.024 0.030 0.094 0.112 0.341 0.423 (0.47) (0.51) (1.22) (1.27) (2.30) (2.33) Win-Loss: 3-factor alpha 0.024 0.032 0.094 0.114 0.351 0.433 (0.48) (0.54) (1.22) (1.29) (2.37) (2.39) Win-Loss: 4-factor alpha 0.020 0.029 0.081 0.112 0.346 0.432 (0.40) (0.50) (1.05) (1.27) (2.34) (2.38)

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Table 7. Post-season trading rule

In the beginning of March of every year over the period 2003-2012 we form a portfolio of buying the stocks of all sponsoring companies whose team had a winning record (i.e., more wins than losses) in the last NFL season, and selling the stocks of all sponsoring companies whose team had a non-winning record. We hold this portfolio for 3 months (until the end of May) and 6 months (until the end of August). We perform both equal-weighted and value-weighted portfolios according to the sponsoring firm’s size (measured by market value of equity). We repeat this procedure using two alternative criteria to the winning record; the first is if the team has improved in the last season (number of wins has increased from the previous season), and the second is if the team participated in the playoff games. The table shows the portfolios’ mean excess monthly returns (in excess of the risk-free rate) and alphas from factor models. The CAPM one-factor model uses the market factor. The three factors in the 3-factor model are the Fama and French (1993) factors. The four factors in the 4-factor model are the Fama-French factors augmented with a momentum factor. All returns and alphas are in percent per month and the corresponding t-statistics are in parentheses.

3-month investment 6-month investment Equal- Value- Equal- Value- weighted weighted weighted weighted Winning-non winning teams Excess return -0.018 0.619 0.566 0.986 (-0.02) (0.70) (0.91) (1.50) CAPM alpha 0.646 1.448 0.812 1.442 (0.63) (1.70) (1.31) (2.38) 3-factor alpha 0.676 1.431 0.787 1.517 (0.65) (1.67) (1.25) (2.47) 4-factor alpha 0.676 1.431 0.798 1.530 (0.64) (1.63) (1.26) (2.47)

Improved-non improved teams Excess return 0.852 2.515 0.650 2.208 (0.96) (1.82) (1.04) (2.29) CAPM alpha 0.371 1.250 0.342 1.731 (0.40) (0.93) (0.56) (1.84) 3-factor alpha 0.407 1.313 0.380 1.741 (0.48) (0.98) (0.63) (1.84) 4-factor alpha 0.408 1.312 0.423 1.744 (0.48) (0.96) (0.71) (1.83)

Playoff-non playoff teams Excess return -0.116 2.142 0.275 2.209 (-0.12) (1.82) (0.43) (2.66) CAPM alpha 0.155 1.929 0.436 2.381 (0.15) (1.51) (0.67) (2.81) 3-factor alpha 0.191 1.969 0.433 2.431 (0.19) (1.51) (0.66) (2.85) 4-factor alpha 0.192 1.967 0.449 2.393 (0.18) (1.51) (0.68) (2.81)

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Figure 1. Difference in cumulative abnormal returns around home game wins and losses

For three samples of NFL home games over the period 2003-2012 (full sample, Monday night, and post-season), we calculate the abnormal return of the home team's sponsoring company in each day during a twelve-day window around the game day. Abnormal returns are measured using five models described in Table 2. For each of the three samples, we average abnormal returns separately for home game wins and home game losses, and calculate cumulative abnormal returns of the game wins averages and the game losses averages. The figure shows the difference between the cumulative abnormal returns around game wins and around game losses, based on the mean of the five abnormal return models.

0.96

0.97

0.98

0.99

1.00

1.01

1.02

1.03

1.04

-5 -4 -3 -2 -1 Gameday

1 2 3 4 5 6

Full sample Monday night Post-season

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Table A1. Publically traded companies sponsoring NFL teams’ stadiums

This table lists all NFL teams’ home stadiums sponsored by publically traded companies over the years 2003-2012.

Sponsorship

NFL Team City State Stadium Name Start Date Sponsoring Firm Total Years Avg. Annual

Bills Toronto Ontario Rogers Centre Jan 2008 Rogers Communications n/a n/a n/a

Broncos Denver CO Invesco Field at Mile High Sep 2001 Invesco Ltd. $ 120,000,000 20 $ 6,000,000

Cardinals Glendale AZ University of Phoenix Stadium Aug 2006 Apollo Group $ 154,000,000 26 $ 5,923,077

Chargers San Diego CA Qualcomm Stadium Jan 1997 Qualcomm $ 18,000,000 20 $ 900,000

Dolphins Miami FL Sun Life Stadium Jan 2010 Sun Life Financial $ 37,500,000 5 $ 7,500,000

Eagles Philadelphia PA Lincoln Financial Field Aug 2003 Lincoln National Corp $ 139,000,000 21 $ 6,619,048

Giants E. Rutherford NJ MetLife Stadium Aug 2011 MetLife Inc. $ 400,000,000 25 $16,000,000

Jaguars Jacksonville FL Alltel Stadium Jan 1997 Alltel Corp $ 6,200,000 10 $ 620,000

Jaguars Jacksonville FL EverBank Field Aug 2010 EverBank Financial Corp $ 16,600,000 5 $ 3,320,000

Jets E. Rutherford NJ MetLife Stadium Aug 2011 MetLife Inc. $ 400,000,000 25 $16,000,000

Lions Detroit MI Ford Field Aug 2002 Ford Motor Company $ 40,000,000 20 $ 2,000,000

Panthers Charlotte NC Ericsson Stadium Sep 1996 LM Ericsson $ 20,000,000 10 $ 2,000,000

Panthers Charlotte NC Bank of America Stadium Jan 2004 Bank of America $ 140,000,000 20 $ 7,000,000

Patriots Foxborough MA Gillette Stadium May 2002 The Gillette Company n/a 15 n/a

Patriots Foxborough MA Gillette Stadium Oct 2005 Proctor & Gamble $ 105,000,000 15 $ 7,000,000

Raiders Oakland CA McAfee Coliseum Jan 2005 McAfee Inc. $ 13,700,000 5 $ 2,740,000

Raiders Oakland CA Overstock.Com Apr 2011 Overstock.com $ 7,200,000 6 $ 1,200,000

Ravens Baltimore MD M&T Bank Stadium May 2003 M&T Bank $ 75,000,000 15 $ 5,000,000

Redskins Landover MD FedEx Field Nov 1999 FedEx $ 205,000,000 27 $ 7,592,593

Saints New Orleans LA Mercedes-Benz Superdome Oct 2011 Daimler AG n/a 10 n/a

Seahawks Seattle WA Qwest Field Jun 2004 Qwest $ 75,000,000 15 $ 5,000,000

Seahawks Seattle WA CenturyLink Field Jun 2011 CenturyLink Inc. $ 75,000,000 15 $ 5,000,000

Steelers Pittsburgh PA Heinz Field Aug 2001 H.J. Heinz Company $ 57,000,000 20 $ 2,850,000

Texans Houston TX Reliant Stadium Aug 2002 Reliant Energy $ 320,000,000 32 $10,000,000

Texans Houston TX Reliant Stadium Aug 2010 NRG Energy $ 320,000,000 32 $10,000,000

Titans Nashville TN LP Field Jun 2006 Louisiana-Pacific $ 30,000,000 10 $ 3,000,000

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Table A2. Game sample distribution

The table presents the distribution of NFL teams’ home and away games over the years 2003-2012 for three samples: (i) Full sample, which includes all pre-season, regular season and post-season games, (ii) Monday night sample, which includes regular season games that are played on Monday night, and (iii) post-season sample, which includes all post-season (playoff) elimination games, except the Super bowl game.

Full sample Monday night games Post-season games # games Home Away # games Home Away # games Home Away 2003 195 97 98 8 4 4 2 1 1

2004 228 113 115 10 5 5 6 5 1 2005 242 128 114 14 7 7 9 7 2 2006 275 138 137 18 11 7 14 5 9 2007 286 144 142 17 8 9 10 5 5 2008 285 144 141 15 10 5 10 5 5 2009 262 131 131 16 6 10 15 7 8 2010 267 133 134 14 5 9 7 3 4 2011 309 157 152 16 7 9 8 5 3 2012 320 161 159 19 11 8 11 5 6

Total 2,669 1,346 1,323 147 74 73 92 48 44