IDEAS home Printed from https://ideas.repec.org/a/eee/soceco/v93y2021ics2214804321000641.html
   My bibliography  Save this article

Sonic Thunder vs. Brian the Snail: Are people affected by uninformative racehorse names?

Author

Listed:
  • Merz, Oliver
  • Flepp, Raphael
  • Franck, Egon

Abstract

This paper examines whether individuals’ decision making is affected by fast-sounding horse names in a betting exchange market environment. In horse racing, the name of a horse does not depend on the horse's performance and is thus uninformative. If positive affect towards fast-sounding horse names is present, we expect less accurate prices, i.e., winning probabilities, and lower returns due to the increased demand for these bets. Using over 3 million horse bets, we find evidence that the winning probabilities of bets on horses with fast-sounding names are overstated, which impairs the prediction accuracy of such bets. This finding implies that prices in betting exchange markets are distorted by incorporating affective, misleading information from a horse's fast-sounding name. Consequently, this bias translates into significantly lower betting returns for horses with names classified as fast-sounding compared to the returns for all other horses.

Suggested Citation

  • Merz, Oliver & Flepp, Raphael & Franck, Egon, 2021. "Sonic Thunder vs. Brian the Snail: Are people affected by uninformative racehorse names?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 93(C).
    • Handle: RePEc:eee:soceco:v:93:y:2021:i:c:s2214804321000641
    DOI: 10.1016/j.socec.2021.101724
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2214804321000641
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.socec.2021.101724?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Arne Feddersen & Brad R. Humphreys & Brian P. Soebbing, 2017. "Sentiment Bias And Asset Prices: Evidence From Sports Betting Markets And Social Media," Economic Inquiry, Western Economic Association International, vol. 55(2), pages 1119-1129, April.
    2. Egon Franck & Erwin Verbeek & Stephan Nüesch, 2011. "Sentimental Preferences and the Organizational Regime of Betting Markets," Southern Economic Journal, John Wiley & Sons, vol. 78(2), pages 502-518, October.
    3. Flepp, Raphael & Nüesch, Stephan & Franck, Egon, 2017. "The liquidity advantage of the quote-driven market: Evidence from the betting industry," The Quarterly Review of Economics and Finance, Elsevier, vol. 64(C), pages 306-317.
    4. Alasdair Brown & Fuyu Yang, 2017. "The Role of Speculative Trade in Market Efficiency: Evidence from a Betting Exchange," Review of Finance, European Finance Association, vol. 21(2), pages 583-603.
    5. Franck, Egon & Verbeek, Erwin & Nüesch, Stephan, 2010. "Prediction accuracy of different market structures -- bookmakers versus a betting exchange," International Journal of Forecasting, Elsevier, vol. 26(3), pages 448-459, July.
    6. David Forrest & Ian Mchale, 2007. "Anyone for Tennis (Betting)?," The European Journal of Finance, Taylor & Francis Journals, vol. 13(8), pages 751-768.
    7. Samuel M. Hartzmark & Abigail B. Sussman, 2019. "Do Investors Value Sustainability? A Natural Experiment Examining Ranking and Fund Flows," Journal of Finance, American Finance Association, vol. 74(6), pages 2789-2837, December.
    8. David Hirshleifer & Tyler Shumway, 2003. "Good Day Sunshine: Stock Returns and the Weather," Journal of Finance, American Finance Association, vol. 58(3), pages 1009-1032, June.
    9. Leighton Vaughan Williams & J. James Reade, 2016. "Forecasting Elections," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(4), pages 308-328, July.
    10. Denis Gromb & Dimitri Vayanos, 2010. "Limits of Arbitrage: The State of the Theory," NBER Working Papers 15821, National Bureau of Economic Research, Inc.
    11. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    12. Buhagiar, Ranier & Cortis, Dominic & Newall, Philip W.S., 2018. "Why do some soccer bettors lose more money than others?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 18(C), pages 85-93.
    13. Rothschild, David, 2015. "Combining forecasts for elections: Accurate, relevant, and timely," International Journal of Forecasting, Elsevier, vol. 31(3), pages 952-964.
    14. Raphael Flepp & Stephan Nüesch & Egon Franck, 2016. "Does Bettor Sentiment Affect Bookmaker Pricing?," Journal of Sports Economics, , vol. 17(1), pages 3-11, January.
    15. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
    16. Krčál, Ondřej & Kvasnička, Michal & Staněk, Rostislav, 2016. "External validity of prospect theory: The evidence from soccer betting," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 65(C), pages 121-127.
    17. Slovic, Paul & Finucane, Melissa L. & Peters, Ellen & MacGregor, Donald G., 2007. "The affect heuristic," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1333-1352, March.
    18. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    19. Alasdair Brown & Dooruj Rambaccussing & J. James Reade & Giambattista Rossi, 2018. "Forecasting With Social Media: Evidence From Tweets On Soccer Matches," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1748-1763, July.
    20. Martin Spann & Bernd Skiera, 2009. "Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipsters," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 55-72.
    21. David Forrest & Robert Simmons, 2008. "Sentiment in the betting market on Spanish football," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 119-126.
    22. Berg, Joyce E. & Nelson, Forrest D. & Rietz, Thomas A., 2008. "Prediction market accuracy in the long run," International Journal of Forecasting, Elsevier, vol. 24(2), pages 285-300.
    23. Steven D. Levitt, 2004. "Why are gambling markets organised so differently from financial markets?," Economic Journal, Royal Economic Society, vol. 114(495), pages 223-246, April.
    24. Denis Gromb & Dimitri Vayanos, 2010. "Limits of Arbitrage," Annual Review of Financial Economics, Annual Reviews, vol. 2(1), pages 251-275, December.
    25. Thaler, Richard H & Ziemba, William T, 1988. "Parimutuel Betting Markets: Racetracks and Lotteries," Journal of Economic Perspectives, American Economic Association, vol. 2(2), pages 161-174, Spring.
    26. Leighton Vaughan Williams & J. James Reade, 2016. "Prediction Markets, Social Media and Information Efficiency," Kyklos, Wiley Blackwell, vol. 69(3), pages 518-556, August.
    27. Avery, Christopher & Chevalier, Judith, 1999. "Identifying Investor Sentiment from Price Paths: The Case of Football Betting," The Journal of Business, University of Chicago Press, vol. 72(4), pages 493-521, October.
    28. Wardley, Marcus & Alberhasky, Max, 2021. "Framing zero: Why losing nothing is better than gaining nothing," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 90(C).
    29. Brown, Alasdair & Reade, J. James & Vaughan Williams, Leighton, 2019. "When are prediction market prices most informative?," International Journal of Forecasting, Elsevier, vol. 35(1), pages 420-428.
    30. Birru, Justin, 2018. "Day of the week and the cross-section of returns," Journal of Financial Economics, Elsevier, vol. 130(1), pages 182-214.
    31. Shafir, Eldar B. & Osherson, Daniel N. & Smith, Edward E., 1993. "The Advantage Model: A Comparative Theory of Evaluation and Choice under Risk," Organizational Behavior and Human Decision Processes, Elsevier, vol. 55(3), pages 325-378, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Raphael Flepp & Oliver Merz & Egon Franck, 2024. "When the league table lies: Does outcome bias lead to informationally inefficient markets?," Economic Inquiry, Western Economic Association International, vol. 62(1), pages 414-429, January.
    2. Dave Cliff & James Hawkins & James Keen & Roberto Lau-Soto, 2021. "Implementing the BBE Agent-Based Model of a Sports-Betting Exchange," Papers 2108.02419, arXiv.org.
    3. Robert East & Malcolm Wright, 2024. "Potential Predictors of Psychologically Based Stock Price Movements," JRFM, MDPI, vol. 17(8), pages 1-17, July.
    4. Dave Cliff, 2021. "BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling," Papers 2105.08310, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Oliver Merz & Raphael Flepp & Egon Franck, 2019. "Does sentiment harm market efficiency? An empirical analysis using a betting exchange setting," Working Papers 381, University of Zurich, Department of Business Administration (IBW).
    2. Oliver Merz & Raphael Flepp & Egon Franck, 2021. "Underestimating randomness: Outcome bias in betting exchange markets," Working Papers 390, University of Zurich, Department of Business Administration (IBW).
    3. Andrew Grant & Anastasios Oikonomidis & Alistair C. Bruce & Johnnie E. V. Johnson, 2018. "New entry, strategic diversity and efficiency in soccer betting markets: the creation and suppression of arbitrage opportunities," The European Journal of Finance, Taylor & Francis Journals, vol. 24(18), pages 1799-1816, December.
    4. Pascal Flurin Meier & Raphael Flepp & Egon Franck, 2021. "Are sports betting markets semistrong efficient? Evidence from the COVID-19 pandemic," Working Papers 387, University of Zurich, Department of Business Administration (IBW).
    5. Raphael Flepp & Oliver Merz & Egon Franck, 2024. "When the league table lies: Does outcome bias lead to informationally inefficient markets?," Economic Inquiry, Western Economic Association International, vol. 62(1), pages 414-429, January.
    6. Kai Fischer & Justus Haucap, 2022. "Home advantage in professional soccer and betting market efficiency: The role of spectator crowds," Kyklos, Wiley Blackwell, vol. 75(2), pages 294-316, May.
    7. David Winkelmann & Marius Ötting & Christian Deutscher & Tomasz Makarewicz, 2024. "Are Betting Markets Inefficient? Evidence From Simulations and Real Data," Journal of Sports Economics, , vol. 25(1), pages 54-97, January.
    8. Gross, Johannes & Rebeggiani, Luca, 2018. "Chance or Ability? The Efficiency of the Football Betting Market Revisited," MPRA Paper 87230, University Library of Munich, Germany.
    9. Kai Fischer & Justus Haucap, 2020. "Betting Market Efficiency in the Presence of Unfamiliar Shocks: The Case of Ghost Games during the Covid-19 Pandemic," CESifo Working Paper Series 8526, CESifo.
    10. Luca De Angelis & J. James Reade, 2023. "Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball," Annals of Operations Research, Springer, vol. 325(1), pages 391-418, June.
    11. Luca De Angelis & J. James Reade, 2022. "Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball," Economics Discussion Papers em-dp2022-01, Department of Economics, University of Reading.
    12. Isabel Abinzano & Luis Muga & Rafael Santamaria, 2019. "Hidden Power of Trading Activity: The FLB in Tennis Betting Exchanges," Journal of Sports Economics, , vol. 20(2), pages 261-285, February.
    13. Angelini, Giovanni & De Angelis, Luca & Singleton, Carl, 2022. "Informational efficiency and behaviour within in-play prediction markets," International Journal of Forecasting, Elsevier, vol. 38(1), pages 282-299.
    14. Philip W. S. Newall & Dominic Cortis, 2021. "Are Sports Bettors Biased toward Longshots, Favorites, or Both? A Literature Review," Risks, MDPI, vol. 9(1), pages 1-9, January.
    15. Arne Feddersen & Brad R. Humphreys & Brian P. Soebbing, 2020. "Casual bettors and sentiment bias in NBA and NFL betting," Applied Economics, Taylor & Francis Journals, vol. 52(53), pages 5797-5806, November.
    16. David Winkelmann & Christian Deutscher & Marius Ötting, 2021. "Bookmakers’ mispricing of the disappeared home advantage in the German Bundesliga after the COVID-19 break," Applied Economics, Taylor & Francis Journals, vol. 53(26), pages 3054-3064, June.
    17. Goto, Shingo & Yamada, Toru, 2023. "What drives biased odds in sports betting markets: Bettors’ irrationality and the role of bookmakers," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 252-270.
    18. Raphael Flepp & Stephan Nüesch & Egon Franck, 2013. "Liquidity, Market Efficiency and the Influence of Noise Traders: Quasi-Experimental Evidence from the Betting Industry," Working Papers 341, University of Zurich, Department of Business Administration (IBW).
    19. Egon Franck & Erwin Verbeek & Stephan Nüesch, 2013. "Inter-market Arbitrage in Betting," Economica, London School of Economics and Political Science, vol. 80(318), pages 300-325, April.
    20. Alasdair Brown & Dooruj Rambaccussing & J. James Reade & Giambattista Rossi, 2018. "Forecasting With Social Media: Evidence From Tweets On Soccer Matches," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1748-1763, July.

    More about this item

    Keywords

    Affect heuristic; Decision making; Market efficiency; Betting market; Horse racing;
    All these keywords.

    JEL classification:

    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • G40 - Financial Economics - - Behavioral Finance - - - General
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:soceco:v:93:y:2021:i:c:s2214804321000641. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620175 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.