Advertisement

Journal of Business Economics

, Volume 85, Issue 3, pp 293–317 | Cite as

A macroeconomic forecasting market

  • Florian Teschner
  • Christof Weinhardt
Original Paper

Abstract

Macro-economic forecasts are used extensively in industry and government even though the historical accuracy and reliability is disputed. Modern information systems facilitate participatory, crowd-sourced processes that harness the collective intelligence. One instantiation of such wisdom of the crowds are prediction markets which have proven to successfully forecast the outcome of elections, sport events and product sales. Consequently we specifically design a prediction market for macro-economic variables in Germany. The proposed market design differs significantly from previous ones. It solves some of the known problems such as low liquidity and partition-dependence framing effects. The market acts as a mechanism not only to aggregate dispersed information but also to aggregate individual forecasts. It does so by incentivizing participation and rewards early, precise forecasts. Moreover, the market-platform is yet alone in aggregating these forecasts continuously and for a long time horizon. Analyzing the market-generated forecasts, we find that forecast accuracy improves constantly over time and that generated forecasts performed well in comparison to the Bloomberg-survey forecasts. From an individual perspective, market participants interact in a repeated decision-making environment closely resembling decision-making in financial markets. We analyze the impact of cognition, risk-aversion and confidence on trading activity and success.

Keywords

Prediction Markets Macro-economic forecasting User heterogeneity Market-engineering Crowd sourcing 

JEL Classification

D40 D02 E37 

References

  1. Abramowicz MB (2004) Information markets, administrative decision making, and predictive cost-benefit analysis. Univ Chicago Law Rev 71:933–1020Google Scholar
  2. Allen F, Gale D (1992) Stock-Price Manipulation. Rev Financ Stud 5(3):503–529CrossRefGoogle Scholar
  3. Ang JS, Schwarz T (1984) Risk aversion and information structure: an experimental study of price variability in the securities markets. J Financ 40(3):825–844Google Scholar
  4. Armstrong JS (2008), ‘Combining Forecasts’, principles of forecasting: a handbook for researchers and practitioners, J. Scott Armstrong, ed., Norwell, MA: Kluwer Academic Publishers, 2001Google Scholar
  5. Arrow KJ, Forsythe R, Gorham M, Hahn R, Hanson R, Ledyard JO, Levmore S, Litan R, Milgrom P, Nelson FD, Neumann GR, Ottaviani M, Schelling TC, Shiller RJ, Smith VL, Snowberg E, Sunstein CR, Tetlock PC, Tetlock PE, Varian HR, Wolfers J, Zitzewitz E (2008) Economics: the promise of prediction markets. Science 320(5878):877–878CrossRefGoogle Scholar
  6. Barber BM, Odean T (2000) Trading is hazardous to your wealth: the common stock investment performance of individual investors. J Financ 55:773–806CrossRefGoogle Scholar
  7. Barber BM, Odean T (2001) Boys will be boys: gender, overconfidence, and common stock investment. Quart J Econ 116(1):261–292CrossRefGoogle Scholar
  8. Barber BM, Lee Y-T, Liu Y-J, Odean T (2009) Just how much do individual investors lose by trading? Rev Financ Stud 22(2):609–632CrossRefGoogle Scholar
  9. Bennouri M, Gimpel H, Robert J (2011) Measuring the impact of information aggregation mechanisms: an experimental investigation. J Econ Behav Organ 78(3):302–318CrossRefGoogle Scholar
  10. Berg JE, Rietz TA (2006) The iowa electronic markets: stylized facts and open issues. In: Hahn RW, Tetlock PC (eds) Information markets: a new way of making decisions. AEI Press, Washington, pp 142–169Google Scholar
  11. Berg J, Forsythe R, Nelson F, Rietz TA (2000) ‘Results from a dozen years of election futures market research’, working paperGoogle Scholar
  12. Berg JE, Nelson FD, Rietz TA (2008) Prediction market accuracy in the long run. Int J Forecast 24(2):285–300CrossRefGoogle Scholar
  13. Berlemann M, Nelson F (2005) ‘Forecasting inflation via experimental stock markets some results from pilot markets’, ifo working papers (Ifo working papers No. 10)Google Scholar
  14. Berlemann M, Dimitrova K, Nenovsky N (2005) ‘Assessing market expectations on exchange rates and inflation: a pilot forecasting system for Bulgaria’, working paperGoogle Scholar
  15. Brabham DC (2008) ‘Crowdsourcing as a model for problem solving convergence’. The international journal of research into new media technologies, Sage publications, 2008, 14, 75Google Scholar
  16. Brenner M, Eldor R, Hauser S (1999) ‘The price of options illiquidity’, technical report, New York University, Leonard N. Stern School of BusinessGoogle Scholar
  17. Camerer CF, Lowenstein G (2003), Behavioral Economics: Past, Present, Future’Advances in Behavioral Economics’, Princeton University Press, pp. 3–51Google Scholar
  18. Campbell DW, Sareen J, Stein MB, Kravetsky LB, Paulus MP, Hassard ST, Reiss JP (2009) Happy but not so approachable: the social judgments of individuals with generalized social phobia. Depress Anxiety 26(5):419–424CrossRefGoogle Scholar
  19. Clements M, Hendry D (2002) ‘An overview of economic forecasting’, A companion to economic forecasting, 1–18Google Scholar
  20. Cowgill B, Zitzewitz E (2013) Corporate prediction markets: evidence from Google, Ford, and Koch industries. Working paperGoogle Scholar
  21. Cowgill B, Wolfers J, Zitzewitz E (2009) Using prediction markets to track information flows: Evidence from google. In S. Das, M. Ostrovsky, D. Pennock, and B. Szymanksi (Eds.), Auctions, Market Mechanisms and Their Applications, Volume 14 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pp. 3–3. Berlin: SpringerGoogle Scholar
  22. Croushore D, Stark T (2001) A real-time data set for macroeconomists. Journal of econometrics 105(1):111–130CrossRefGoogle Scholar
  23. Deck C, Lin S, Porter D (2013) Affecting policy by manipulating prediction markets: experimental evidence. J Econ Behav Organ 85:48–62CrossRefGoogle Scholar
  24. Erikson RS, Wlezien C (2008) Are political markets really superior to polls as election predictors?. Public Opinion Quart 72(2):190–215CrossRefGoogle Scholar
  25. Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Financ 25(2):383–417CrossRefGoogle Scholar
  26. Fama EF (1991) Efficient capital markets: ii. J Financ 46(5):1575–1617CrossRefGoogle Scholar
  27. Fellner G, Maciejovsky B (2007) Risk attitude and market behavior: evidence from experimental asset markets. J Econ Psychol 28(3):338–350CrossRefGoogle Scholar
  28. Fildes R, Stekler H (2002) The state of macroeconomic forecasting. J Macroecon 24(4):435–468CrossRefGoogle Scholar
  29. Forsythe R, Nelson F, Neumann GR, Wright J (1992) Anatomy of an experimental political stock market. Am Econ Rev 82(5):1142–1161Google Scholar
  30. Forsythe R, Rietz TA, Ross TW (1999) Wishes, expectations and actions: a survey on price formation in election stock markets. J Econ Behav Organ 39:83–110CrossRefGoogle Scholar
  31. Frederick S (2005) Cognitive reflection and decision making. J Econ Perspect 19(4):25–42CrossRefGoogle Scholar
  32. Gadanecz B, Moessner R, Upper C (2007) ‘Economic derivatives’, BIS quarterly review, 68Google Scholar
  33. Gillen BJ, Plott CR, Shum M (2012) Information aggregation mechanisms in the field: Sales forecasting inside intel. Working paperGoogle Scholar
  34. Gjerstad S, Hall M (2005), ‘Risk aversion, beliefs, and prediction market equilibrium’, Economic Science Laboratory, University of Arizona Google Scholar
  35. Goel S, Reeves DM, Watts DJ, Pennock DM (2010, June). Prediction without markets. In Proceedings of the 11th ACM conference on Electronic commerce (pp. 357-366). ACMGoogle Scholar
  36. Graefe A (2010) Are prediction markets more accurate than simple surveys? Foresight. Int J Appl Forecast Issue 19:39–43Google Scholar
  37. Gurkaynak R, Wolfers J (2006) ‘Macroeconomic Derivatives: an initial analysis of market-based macro forecasts, uncertainty, and risk’, technical report, National Bureau of Economic Research, Inc, No. 11929Google Scholar
  38. Güth W, Krahnen JP, Rieck C (1997) Financial markets with asymmetric information: a pilot study focusing on insider advantages. J Econ Psychol 18(2–3):235–257CrossRefGoogle Scholar
  39. Hanson R (2003) Combinatorial information market design. Inf Sys Front 5(1):107–119CrossRefGoogle Scholar
  40. Hanson R (2006) ‘Foul play in information markets’. In: Hahn RW, Tetlock PC (ed.) Information markets: a new way of making decisions. pp. 126–141. Washington D.C.: AEI Press. Incollection George Foul Play in Information MarketsGoogle Scholar
  41. Hanson R, Oprea R (2007) A Manipulator Can Aid Prediction Market Accuracy. Economica 76:304–314CrossRefGoogle Scholar
  42. Hanson R, Oprea R, Porter D (2006) Information Aggregation and Manipulation in an Experimental Market. J Econ Behav Organ 60(4):449–459CrossRefGoogle Scholar
  43. Hayek FA (1945) The use of knowledge in society. Am Econ Rev 35(4):519–530Google Scholar
  44. Holt CA, Laury SK (2000) Risk aversion and incentive effects. Am Econ Rev 92:1644–1655CrossRefGoogle Scholar
  45. Jian L, Sami R (2012) Aggregation and manipulation in prediction markets: effects of trading mechanism and information distribution. Manage Sci 58(1):123–140CrossRefGoogle Scholar
  46. Kirchler E, Maciejovsky B (2002) Simultaneous over- and underconfidence: evidence from experimental asset markets. J Risk Uncertain 25(1):65–85CrossRefGoogle Scholar
  47. Kittur A, Kraut RE (2008) Harnessing the wisdom of crowds in wikipedia: quality through coordination. Proceedings of the 2008 ACM conference on Computer supported cooperative work (CSCW ‘08). ACM, New York, pp 37–46CrossRefGoogle Scholar
  48. Ledyard J, Hanson R, Ishikida T (2009) An experimental test of combinatorial information markets. J Econ Behav Organ 69(2):182–189CrossRefGoogle Scholar
  49. Leitch G, Tanner J (1991) Economic forecast evaluation: profits versus the conventional error measures. Am Econ Rev 81(3):580–590Google Scholar
  50. Lo AW (2007) ‘Efficient markets hypothesis’. The new Palgrave: a dictionary of economics 2Google Scholar
  51. Luckner S, Schroeder J, Slamka C (2008), ‘On the forecast accuracy of sports prediction markets’. In: Gimpel H, Jennings NR, Kersten G, Ockenfels A, Weinhardt C (eds) “Negotiation, auctions and market engineering” 1, 227–234Google Scholar
  52. Manski CF (2006) Interpreting the predictions of prediction markets. Econ Lett 91:425–429CrossRefGoogle Scholar
  53. Mbemap M (2004) Economic derivatives and the art and science of investment. J Deriv Account 1:5–8Google Scholar
  54. McNees SK (1992), ‘How large are economic forecast errors?’, New England Economic review(Jul), 25–42Google Scholar
  55. Milgrom P, Stokey N (1982) ‘Information, trade, and common knowledge’. Journal of Economic Theory 26(1)Google Scholar
  56. Neumann D (2004) ‘Market Engineering: A structured design process for electronic markets’, PhD thesis, KarlsruheGoogle Scholar
  57. Oliven K, Rietz TA (2004) Suckers are born but markets are made: individual rationality, arbitrage, and market efficiency on an electronic futures market. Manage Sci 50(3):336–351CrossRefGoogle Scholar
  58. Oller L-E, Barot B (2000) The accuracy of European growth and inflation forecasts. Int J Forecast 16(3):293–315CrossRefGoogle Scholar
  59. Osterloh S (2008) Accuracy and properties of german business cycle forecasts. Appl Econ Quart 54(1):27–57 (formerly: Konjunkturpolitik)CrossRefGoogle Scholar
  60. Prakash, Loungani (2001) How accurate are private sector forecasts? Cross-country evidence from consensus forecasts of output growth. Int J Forecast 17(3):419–432CrossRefGoogle Scholar
  61. Roll R (1984) Orange juice and weather. Am Econ Rev 74(5):861–880Google Scholar
  62. Roth EA (2009) What have we learned from market design? Innovation policy and the economy, Vol 9. University of Chicago Press, 79–112Google Scholar
  63. Rothschild D (2009) Forecasting Elections Comparing Prediction Markets, Polls, and Their Biases. Public Opin Quart 73(5):895–916CrossRefGoogle Scholar
  64. Schuh S (2001) ‘An evaluation of recent macroeconomic forecast errors’, New England Economic Review, 35–56Google Scholar
  65. Servan-Schreiber E, Wolfers J, Pennock DM, Galebach B (2004) Prediction markets: does money matter? Electron Markets 14(3):243–251CrossRefGoogle Scholar
  66. Seuken S, Parkes DC, Horvitz E, Jain K, Czerwinski M, Tan D (2012) ‘Market user interface design’. In Proceedings of the 13th ACM Conference on Electronic Commerce (pp. 898–915). ACMGoogle Scholar
  67. Shiller RJ (1993), Macro markets: creating institutions for managing society’s largest economic risks/Robert J. Shiller, Clarendon, OxfordGoogle Scholar
  68. Snowberg E, Wolfers J (2005) ‘Explaining the favorite-longshot bias: is it risk-love, or misperceptions?’, University of Pennsylvania, mimeoGoogle Scholar
  69. Snowberg E, Wolfers J, Zitzewitz E (2007) Partisan impacts on the economy: evidence from prediction markets and close elections. Q J Econ 122(2):807–829CrossRefGoogle Scholar
  70. Sonnemann U, Camerer CF, Fox CR, Langer T (2008) ‘Partition-dependent framing effects in lab and field prediction markets’, working paperGoogle Scholar
  71. Spann M, Skiera B (2004) Einsatzmöglichkeiten virtueller Börsen in der Marktforschung. Zeitschrift für Betriebswirtschaft 74:25–48Google Scholar
  72. Stathel S, Luckner S, Teschner F, Weinhardt C, Reeson A, Whitten S (2009), ‘AKX - An exchange for predicting water dam levels in Australia’, in Proceedings of the 4th International Symposium on Information Technologies in Environmental Engineering, pp. 78-90Google Scholar
  73. Subrahmanyam A (1991) Risk aversion, market liquidity, and price efficiency. Rev Financ Stud 4(3):417–441CrossRefGoogle Scholar
  74. Surowiecki J (2004) The wisdom of crowds: why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations. Doubleday, New YorkGoogle Scholar
  75. Teschner F, Weinhardt C (2012) Evaluating hidden market design’. auctions, market mechanisms, and their applications. Springer, New York, pp 5–17CrossRefGoogle Scholar
  76. Teschner F, Mazarakis A, Riordan R, Weinhardt C (2011) “Participation, feedback & incentives in a competitive forecasting community,” Proceedings of the International Conference on Information Systems (ICIS), Shanghai, China, Paper 16, 1–14Google Scholar
  77. Thaler RH, Sunstein CR, Balz JP (2010) ‘Choice architecture’, working paperGoogle Scholar
  78. Thomason, D. (2012). “Should presidential campaigns spend more money manipulating intrade?” The Atlantic, October 23, 2012Google Scholar
  79. Tversky A, Kahneman D (1974) Judgment under Uncertainty: heuristics and Biases. Science 185(4157):1124–1131CrossRefGoogle Scholar
  80. Tziralis G, Tatsiopoulos I (2007) Prediction markets: an extended literature review. J Predict Markets 1(1):75–91Google Scholar
  81. Vajna T (1977) Prognosen für die Politik. (Grenzen, Fehler, Möglichkeiten der Wirtschaftsprognosen, Deutscher Instituts-VerlagGoogle Scholar
  82. Wärneryd KE (1996) Risk attitudes and risky behavior. J Econ Psychol 17(6):749–770CrossRefGoogle Scholar
  83. Weinhardt C, Gimpel H (2006) Market engineering: an interdisciplinary research challenge. In: Jennings N, Kersten G, Ockenfels A, Weinhardt C (eds) Dagstuhl seminar proceedings 06461 negotiation and market engineering. Internationales Begegnungs- und Forschungszentrum (IBFI), Schloss Dagstuhl, GermanyGoogle Scholar
  84. Weinhardt C, Holtmann C, Neumann D (2003) Market-engineering. Wirtschaftsinformatik 45(6):635–640CrossRefGoogle Scholar
  85. Weinhardt C, Neumann D, Holtmann C, Ganz W (2006) Germany: computer-aided market engineering. Communications-ACM 49(7):79CrossRefGoogle Scholar
  86. Wolfers J, Zitzewitz E (2004) Prediction Markets. J Econ Perspect 18(2):107–126CrossRefGoogle Scholar
  87. Wolfers J, Zitzewitz E (2006a) Five open questions about prediction markets. In: Hahn Robert W, Tetlock Paul C (eds) Information markets: a new way of making decisions. AEI Press, Washington D.C., pp 13–36Google Scholar
  88. Wolfers J, Zitzewitz E (2006b) ‘Interpreting prediction market prices as probabilities’, Technical report, Federal Reserve Bank of San FranciscoGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Karlsruhe Institute of Technology (KIT)KarlsruheGermany

Personalised recommendations