Local Sports Sentiment and the Returns and Trading Behavior of Locally Headquartered Stocks: A Firm-Level Analysis

1. Local Sports Sentiment and the Returns and Trading Behavior of Locally Headquartered Stocks: A Firm-Level Analysis Shao-Chi Chang Sheng-Syan Chen Robin K. Chou Yueh-Hsiang Lin

2. Sports Sentiment (Source:http://www.northjersey.com /sports/yankees/Wangs_world.html)

3. Sports results and stock returns • Psychological literature (Wright and Bower, 1992; Bagozzi et al., 1999): people tend to be more optimistic as to future prospects when they are in a better mood. • Investors experience a strong reaction in relation to team performance. • Such reactions result in pessimistic or optimistic assessments, affect the evaluations of future prospects, and influence trading behavior in financial markets and stock price changes.

4. Investor sentiment and trading behavior and stock returns • Variables of sentiment used in the literature that impact investor mood: weather conditions: • Sunshine: the more the cloud cover, the worse the investors’ mood: • Saunders, AER, 1993: The first paper that documents a negative relation between (Dow-Jones Industry Average and NYSE/AMEX) index returns and sunshine. • Hirshleifer and Shumway, JF, 2003: find a negative relation between index returns (across 26 countries) and sunshine.

5. Investor sentiment and trading behavior and stock returns • Sunshine: the more the cloud cover, the worse the investors’ mood: • Chang et al., JBF, 2008: • Cloud cover has a significant influence on stock returns only at the market open. • Cloudy skies have a significant influence on investors’ intraday trading behavior.

6. Investor sentiment and trading behavior and stock returns • Variables of sentiment used in the literature that impact investor mood: weather conditions: • Daylight: • Kamstra et al., AER, 2000: The sleep desynchronosis caused by daylight saving time has a negative influence on the investors’ mood and risk taking, and (US main index) stock returns on the following Mondays. • Kamstra et al., AER, 2003: Winter Blue: Fewer hours of daylight have a negative influence on the investors’ mood and risk taking, and (9 countries’ main index) stock returns.

7. Investor sentiment and trading behavior and stock returns • Variables of sentiment used in the literature that impact investor mood: weather conditions: • Temperature: • Cao and Wei, JBF, 2005: higher temperature leads to apathy, impede risk-taking, and lower (8 countries’ main index) daily stock returns. • Lunar cycles: • Yuan et al., JEF, 2006: Full moon phases is related to depressed mood and lower stock returns (than new moon phases) across 48 countries.

8. Sports sentiment • Psychological literature demonstrates that sports results have a significant influence on mood. • Wang’s world • Edmans et al., JF, 2007: • Find an economically and statistically significant market decline (across 39 countries) after soccer losses and other sports such as cricket, rugby, ice hockey, and basketball games.

9. Motivation of this paper • Why investigating sports sentiment? • Top finance journals did accept papers related to sports sentiment. • There remain unanswered/unresolved questions within this area (investor sentiment and trading behavior and stock returns) of the research.

10. Contributions of this paper: I. Firm level • Comparison: • Edmans et al., JF, 2007: analyze the effects of sports results on investor mood through an examination of aggregate stock market indices. • This paper: undertakes firm-level analysis: • According to Baker and Wurgler (JF, 2006), investor sentiment has impact on stock returns more when firms are with specific characteristics. • Small stocks, young stocks, high growth stocks, non-dividend-paying stocks, and stocks with high return volatility, low asset tangibility, and low asset profitability are more likely to be affected by shifts in investor sentiment.

11. Contributions of this paper: II. Intraday returns • Comparison: • Edmans et al., JF, 2007: examine the effects of sports sentiment on daily stock returns. • This paper: examines the effects of sports sentiment on intraday returns: • Literature documents a strong seasonality in intraday return patterns. Sports sentiment may affect stock returns more significantly at market opening periods. • The short measurement period reduces the sources of variability that may be attributed to other unrelated extraneous factors, outliers or subsamples, and therefore, strengthen the evidence of the relation between sports sentiment and stock returns.

12. Contributions of this paper: III. Trading Behavior • Comparison: • Edmans et al., JF, 2007: do not consider. • This paper: provides additional evidence on the effects of sports sentiment on trading activities in the market. • Previous studies (Brown, 1999; Baker and Stein, 2004; and Chang et al., 2008) suggest that the mood of investors may affect their trading activities. • Literature (Cohen et., 2002 and Loughran and Schultz, 2004) also suggests that individual investors are more likely to deviate from rational valuation of securities than are institutional investors. Therefore, the impact of sports sentiment on trading activities is expected to be more pronounced for individual investors.

13. Contributions of this paper: IV. “Local” issue • Home bias/local bias: • Literature suggests that investors both hold and trade substantially more shares in local companies/countries than in other firms/countries. • Explanations: • For investors, foreign stocks are less familiar than local stocks. Due to aversion to ambiguity, investors tend to overweight the stocks they are more familiar.

14. Contributions of this paper: IV. “Local” issue • Comparison: • Edmans et al., JF, 2007: relate sports outcomes to country stock index returns, where the submitted orders may come from investors located all over the world and may not affected by the sports results. • This paper: • focuses on the relation between local* sports results and the return patterns of locally headquartered Nasdaq** stocks. • also examines NYSE firms as a benchmark. *In most American professional sports, a team is usually located at a city. **Loughran and Schultz (JFQA, 2004) show that a substantial amount of trading for Nasdaq stocks originates from the city where the firm is based.

15. Sample and Methodology • To establish contribution IV, we use American football games outcomes as the proxy for the sentiment: • The Harris Poll in 2007: What is your favorite sport in the U.S.? • Football: 29%, • Baseball: 14%, • Auto racing: 9%, • Men’s basketball, 7%.

16. Sample and Methodology • Sample: Nasdaq stocks where the first three digits of the zip code of the headquarter is located in the same geographical area as the city of an NFL football team: • Obtain the data from the official National Football League (NFL) website for the city where a football team is located. • Use the website of the United States Postal Service to identify the zip code of the city of the football team.

17. Sample and Methodology • Obtain the zip codes of firms’ headquarters from the Compustat database. • If a Nasdaq firm’s headquarter and the city of an NFL football team have the same first three-digit zip code, they are defined to be in the same geographical area.* * Suggested by Tim Loughran.

18. Sample and Methodology • The NFL game results are from Statfox, a premier sports handicapping community on the Internet that provides game statistics and betting information for all the major sports in the U.S. and Canada. • The sample period is from September 1994 through December 2004: • The number of teams in the NFL increase from 28 to 32. • Each team plays 16 regular games in a season, starting in early September and ending in late December or early January of the next year. • The majority of the football games take place on Sunday afternoon, although some are played at other times (generally Monday, Thursday, or Sunday nights). • We use stock returns and trading variables on the first trading day following the game.

19. Sample and Methodology • Adjust for the pre-game expectation on game results: • Point-spread of the betting market is widely used in the literature as the measure of market expectation on the game results for various sports in the U.S.: • Ex: When team A is favored over team B by 5 points, meaning that a bet on team A required team A to win by more than 5 points. • Point-spread is “the equilibrium price” of the batting market.

20. Sample and Methodology • Point-spread will fluctuate, which is similar with the futures market. • Many professional bettors in the betting market who attempt to exploit any potential mispricing opportunity. Furthermore, the information on the betting market is widely assimilated and quickly updated. Therefore, literature suggests that the betting lines reflect pre-game expectations and sentiments. • Literature documents that more new information and pre-game sentiment are incorporated into the closing point spread than the opening point spread.

21. Sample and Methodology • Ex.: if the closing point spread indicates that team A is favored over team B by 5 points, • team A wins the game when it beats team B by more than 5 points. • team A loses the game when it beats team B by less than 5 points. • The game ties when team A beats team B by exactly 5 points.

22. Hypothesis 1 (related to Contribution IV) • Hypothesis 1. Locally headquartered stocks with losing football teams experience lower returns relative to those stocks with winning football teams. • CRSP provides the daily return.

23. Hypothesis 1: testing results • Regress daily returns (in percentage) against DLoss, a dummy variable which equals one if the football team fails to cover the point spread (i.e., loses the game), and zero if it covers the point spread (i.e., wins the game). • Control (untabulated) firm dummy variables. • The coefficients on DLoss is statistically significant, but not economically significant, assuming a daily investment frequency and after considering a 0.5% one-way transaction cost.

24. Hypothesis 2 • Does negative sentiment impact the sentiment-return relation? • Kahmeman and Tversky (Heuristics Bias): people tend to overweight recent experience at the expense of long-term average when processing information. • Another similar effect: Hot-hand effect. • Fans of local football teams may become more pessimistic when their teams experience recent successive losses. • Hypothesis 2. The lower return effects of football game losses for locally headquartered stocks are stronger when the local teams experience a successive loss of games.

25. Hypothesis 2: testing results • Regress daily returns (in percentage) against • DSL1, a dummy variable which equals one if the football team covers the point spread in the previous game but fails to cover the point spread in the current game, and zero otherwise. • DSL2, a dummy variable which equals one if the team fails to cover the point spread both in the previous and current games, and zero otherwise. • Results are consistent with Hypothesis 2.

26. Hypothesis 3(related to Contribution I) • Baker and Wurgler (2006):investor sentiment has impact on stock returns more when firms are with specific characteristics. • The more difficult the stocks are valued, the more likely the stocks are affected by shifts in propensity to speculate and therefore investor sentiment. • The riskier and more costly the stocks are arbitraged, the more likely the stocks are affected by shifts in investor sentiment. • In practice, the same stocks that are the hardest to arbitrage also tend to be the most difficult to value.

27. Hypothesis 3 • Hypothesis 3. The negative effects of football game losses on the returns of locally headquartered stocks are stronger for small firms, young firms, high growth firms, non-dividend-paying firms, and firms with high return volatility, low asset profitability, and low asset tangibility. • Measure firm characteristics (from the Compustat and the CRSP) at the end of June prior to the football game: • Firm size (SIZE): the market capitalization. • Firm age (AGE): the number of years since the firm’s first appearance on the CRSP. • The book-to-market equity ratio (B/M).

28. Hypothesis 3 • Return volatility (VOLATILITY): the standard deviation of monthly returns over the 12 months ending in June prior to the football game. • Asset tangibility (TANGIBILITY): the gross property, plant, and equipment scaled by the lagged total assets. • Firm profitability (PROFITABILITY): the average ROA for the three years before the football game. • Dividend per share (DPS): total cash dividend divided by the number of shares outstanding.

29. Hypothesis 3: testing results • Regress daily returns (in percentage) against • DLossFC, where FC equals one if the stocks of firms are more likely to be affected by sports sentiment, and zero otherwise. • The sample median of each firm characteristic at the end of June prior to each football season is used to classify the sample firms into two subsamples, except for DPS. • if DPS equals zero, the firm is a non-dividend-paying firm and FC equals one. • Results are consistent with Hypothesis 3.

30. Hypothesis 3: testing results

31. Hypothesis 4 (related to Contribution II) • The mood induced by sports outcomes may have a more pronounced influence on the investors’ decision-making process at the beginning of the trading hours. • As more information arrives in the market during the trading day, the influence of sports results on stock returns diminish quickly. • Hypothesis 4. The effects of football game outcomes on the returns of locally headquartered stocks are stronger at the market open that will not last for the entire trading day.

32. Hypothesis 4: testing results • Obtain intraday returns from the Trade and Quote (TAQ) database. • Split a trading day (9:30-16:00) into thirteen 30-minute intervals. The interval return is calculated as the natural log of the prices that are nearest to the beginning of the interval subtracted from the natural log of the prices that are nearest to the end of the interval. • Regress intraday returns (in percentage) against DLossfor different trading periods. • Control (untabulated) firm dummy variables. • Results are consistent with Hypothesis 4.

33. Hypothesis 4: testing results

34. Hypothesis 4: testing results • Further examine finer intervals during the first 30 minutes after the market open.

35. Hypothesis 4+Hypothesis 2

36. Hypothesis 4+Hypothesis 3

37. Hypotheses 5 and 6(related to Contribution III) • When the sentiments of investors are bullish, they may become overconfident, overestimate the relative precision of their own private signals, become more optimistic and have more of a desire to trade and tend to buy rather than sell. • Hypothesis 5. Locally headquartered stocks with losing football teams experience lower turnover ratios, market depth, and numbers of buy orders, but higher bid-ask spreads, relative to those stocks with winning football teams.

38. Hypotheses 5 and 6 • Literature suggests that individual investors are more likely to deviate from rational valuation of securities than are institutional investors. • The sports-induced impact is expected to be more pronounced for individual investors. • Hypothesis 6. The negative effects of football game losses on the trading volume of locally headquartered stocks are significantly stronger for non-institutional traders than for institutional traders.

39. Hypotheses 5 and 6 : testing results • Obtain trading variables from the TAQ database: • Order imbalance ratio based on trading volume (OISVOL):the trading volume of seller-initiated trades divided by the total trading volume. • Order imbalance ratio based on number of trades (OISNUM): the number of seller-initiated trades divided by the total number of trades. • Average trading volume per trade (TURNPER) scaled by number of firm shares outstanding at the end of the previous month. • Cumulative trading volume (TURN), scaled by number of firm shares outstanding at the end of the previous month. • Market depth (DEPTH): the average quote size at the best bid and ask prices.

40. Hypotheses 5 and 6 : testing results • The first difference in the percentage effective (DIF_ES), which is defined as twice the absolute value of the difference between the trading price and the mid-point of the ask and the bid prices, scaled by the mid-point of the ask and the bid prices. • The first difference in the percentage quoted (DIF_QS), which is the difference between the ask price and the bid price scaled by the mid-point of the ask and bid prices. • The non-institutional investors’ trading volume divided by the total trading volume (NIVOL), where the institutional investors’ trading are defined as trades over $20,000. • Regress aforementioned variables against DLossfor the opening period. • Results are consistent with Hypotheses 5 and 6.

41. Hypotheses 5 and 6 : testing results

42. NASDAQ firms vs. NYSE firms • Whether the results of football games affect the next-day returns of firms listed on the NYSE? No.

43. Conclusions • This study shows that 1.Losses in football matches by local teams lead to significantly lower next-day stock returns for locally headquartered stocks. 2.The negative effects of football game losses on stock returns are significantly stronger when the football teams experience a successive loss of games. 3.The impact of football game results on the stock returns of locally headquartered stocks depend on firm characteristics. 4.Football game losses significantly and negatively influence next-day stock returns only at the market open. 5.The negative effects of football game losses on stock returns at the market open are significantly stronger when football teams experience a successive loss of games.

44. Conclusions 6.These effects during the market opening period are also significantly stronger for those firms with characteristics that are more vulnerable to shifts in investor sentiment. 7.When football teams lose matches, there are significantly more seller-initiated trades and lower turnover ratios and market depth for locally headquartered stocks. 8.The negative effects of game losses on trading volume around the market open are significantly stronger for non-institutional traders than for institutional traders. 9. There is not any significant impact of football game outcomes on the returns of NYSE stocks. The End