IDEAS home Printed from https://ideas.repec.org/p/gat/wpaper/1735.html
   My bibliography  Save this paper

Information (Non)Aggregation in Markets with Costly Signal Acquisition

Author

Listed:
  • Brice Corgnet

    (Univ Lyon, GATE L-SE UMR 5824, F-69130 Ecully, France; EM Lyon Business School, 23 Avenue Guy de Collongue, F-69130 Écully)

  • Cary Deck

    (University of Alabama, 261 Stadium Drive, Tuscaloosa, AL 35487; Chapman University, One University Drive, Orange, CA 92866)

  • Mark DeSantis

    (Chapman University, One University Drive, Orange, CA 92866)

  • David Porter

    (Chapman University, One University Drive, Orange, CA 92866)

Abstract

Markets are often viewed as a tool for aggregating disparate private knowledge, a stance supported by past laboratory experiments. However, traders’ acquisition cost of information has typically been ignored. Results from a laboratory experiment involving six treatments varying the cost of acquiring signals of an asset’s value suggest that when information is costly, markets do not succeed in aggregating it. At an individual level, having information improves trading performance, but not enough to offset the cost of obtaining the information. Although males earn more through trading than females, this differential is offset by the greater propensity of males to buy information such that total profit is similar for males and females. Looking at individual skills, we find that higher theory of mind is associated with greater trading profit, greater overall profit, and an increased likelihood of acquiring information while cognitive reflection is associated with greater profit but not a greater propensity to acquire information.

Suggested Citation

  • Brice Corgnet & Cary Deck & Mark DeSantis & David Porter, 2017. "Information (Non)Aggregation in Markets with Costly Signal Acquisition," Working Papers 1735, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
  • Handle: RePEc:gat:wpaper:1735
    as

    Download full text from publisher

    File URL: ftp://ftp.gate.cnrs.fr/RePEc/2017/1735.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jacobsen, Ben & Potters, Jan & Schram, Arthur & van Winden, Frans & Wit, Jorgen, 2000. "(In)accuracy of a European political stock market: The influence of common value structures," European Economic Review, Elsevier, vol. 44(2), pages 205-230, February.
    2. Lintner, John, 1969. "The Aggregation of Investor's Diverse Judgments and Preferences in Purely Competitive Security Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 4(4), pages 347-400, December.
    3. Plott, Charles R & Sunder, Shyam, 1982. "Efficiency of Experimental Security Markets with Insider Information: An Application of Rational-Expectations Models," Journal of Political Economy, University of Chicago Press, vol. 90(4), pages 663-698, August.
    4. Plott, Charles R & Sunder, Shyam, 1988. "Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets," Econometrica, Econometric Society, vol. 56(5), pages 1085-1118, September.
    5. Sunder, Shyam, 1992. "Market for Information: Experimental Evidence," Econometrica, Econometric Society, vol. 60(3), pages 667-695, May.
    6. Cary Deck & David Porter, 2013. "Prediction Markets In The Laboratory," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 589-603, July.
    7. Brice Corgnet & Mark Desantis & David Porter, 2018. "What Makes a Good Trader? On the Role of Intuition and Reflection on Trader Performance," Journal of Finance, American Finance Association, vol. 73(3), pages 1113-1137, June.
    8. Florian Hauser & Jürgen Huber & Bob Kaempff, 2015. "Costly Information in Markets with Heterogeneous Agents: A Model with Genetic Programming," Computational Economics, Springer;Society for Computational Economics, vol. 46(2), pages 205-229, August.
    9. Brice Corgnet & Roberto Hernán-González & Praveen Kujal & David Porter, 2015. "The Effect of Earned Versus House Money on Price Bubble Formation in Experimental Asset Markets," Review of Finance, European Finance Association, vol. 19(4), pages 1455-1488.
    10. Oechssler, Jörg & Roider, Andreas & Schmitz, Patrick W., 2009. "Cognitive abilities and behavioral biases," Journal of Economic Behavior & Organization, Elsevier, vol. 72(1), pages 147-152, October.
    11. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    12. Darrell Duffie & Semyon Malamud & Gustavo Manso, 2009. "Information Percolation With Equilibrium Search Dynamics," Econometrica, Econometric Society, vol. 77(5), pages 1513-1574, September.
    13. Peter Bossaerts & Cary Frydman & John Ledyard, 2014. "The Speed of Information Revelation and Eventual Price Quality in Markets with Insiders: Comparing Two Theories," Review of Finance, European Finance Association, vol. 18(1), pages 1-22.
    14. Lucy F. Ackert & Bryan K. Church & Richard Deaves, 2002. "Bubbles in experimental asset markets: Irrational exuberance no more," FRB Atlanta Working Paper 2002-24, Federal Reserve Bank of Atlanta.
    15. Cabrales, Antonio & Gossner, Olivier & Serrano, Roberto, 2017. "A normalized value for information purchases," Journal of Economic Theory, Elsevier, vol. 170(C), pages 266-288.
    16. Benjamin J. Gillen & Charles R. Plott & Matthew Shum, 2017. "A Pari-Mutuel-Like Mechanism for Information Aggregation: A Field Test inside Intel," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 1075-1099.
    17. Antoine J. Bruguier & Steven R. Quartz & Peter Bossaerts, 2010. "Exploring the Nature of “Trader Intuition”," Journal of Finance, American Finance Association, vol. 65(5), pages 1703-1723, October.
    18. Hanson, Robin & Oprea, Ryan & Porter, David, 2006. "Information aggregation and manipulation in an experimental market," Journal of Economic Behavior & Organization, Elsevier, vol. 60(4), pages 449-459, August.
    19. Charles Noussair & Steven J.Tucker & Yilong Xu, 2014. "A Futures Market Reduces Bubbles but Allows Greater Profit for More Sophisticated Traders," Working Papers in Economics 14/12, University of Waikato.
    20. Bruno Biais & Denis Hilton & Karine Mazurier & Sébastien Pouget, 2005. "Judgemental Overconfidence, Self-Monitoring, and Trading Performance in an Experimental Financial Market," Review of Economic Studies, Oxford University Press, vol. 72(2), pages 287-312.
    21. Joyce E. Berg & Thomas A. Rietz, 2003. "Prediction Markets as Decision Support Systems," Information Systems Frontiers, Springer, vol. 5(1), pages 79-93, January.
    22. Lawrence Choo & Todd R. Kaplan & Ro’i Zultan, 2019. "Information aggregation in Arrow–Debreu markets: an experiment," Experimental Economics, Springer;Economic Science Association, vol. 22(3), pages 625-652, September.
    23. Anderson, Lisa R & Holt, Charles A, 1997. "Information Cascades in the Laboratory," American Economic Review, American Economic Association, vol. 87(5), pages 847-862, December.
    24. Ernan Haruvy & Charles N. Noussair, 2006. "The Effect of Short Selling on Bubbles and Crashes in Experimental Spot Asset Markets," Journal of Finance, American Finance Association, vol. 61(3), pages 1119-1157, June.
    25. Emmanuel Dechenaux & Dan Kovenock & Roman Sheremeta, 2015. "A survey of experimental research on contests, all-pay auctions and tournaments," Experimental Economics, Springer;Economic Science Association, vol. 18(4), pages 609-669, December.
    26. Camerer, Colin & Dreber, Anna & Forsell, Eskil & Ho, Teck-Hua & Huber, Jurgen & Johannesson, Magnus & Kirchler, Michael & Almenberg, Johan & Altmejd, Adam & Chan, Taizan & Heikensten, Emma & Holzmeist, 2016. "Evaluating replicability of laboratory experiments in Economics," MPRA Paper 75461, University Library of Munich, Germany.
    27. Smith, Vernon L & Suchanek, Gerry L & Williams, Arlington W, 1988. "Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets," Econometrica, Econometric Society, vol. 56(5), pages 1119-1151, September.
    28. Helena Veiga & Marc Vorsatz, 2010. "Information aggregation in experimental asset markets in the presence of a manipulator," Experimental Economics, Springer;Economic Science Association, vol. 13(4), pages 379-398, December.
    29. Shane Frederick, 2005. "Cognitive Reflection and Decision Making," Journal of Economic Perspectives, American Economic Association, vol. 19(4), pages 25-42, Fall.
    30. Forsythe, Robert & Lundholm, Russell, 1990. "Information Aggregation in an Experimental Market," Econometrica, Econometric Society, vol. 58(2), pages 309-347, March.
    31. Grossman, Sanford J & Stiglitz, Joseph E, 1976. "Information and Competitive Price Systems," American Economic Review, American Economic Association, vol. 66(2), pages 246-253, May.
    32. Robin Hanson & Ryan Oprea, 2009. "A Manipulator Can Aid Prediction Market Accuracy," Economica, London School of Economics and Political Science, vol. 76(302), pages 304-314, April.
    33. Andreas Hefti & Steve Heinke & Frédéric Schneider, 2016. "Mental capabilities, trading styles, and asset market bubbles: theory and experiment," ECON - Working Papers 234, Department of Economics - University of Zurich.
    34. Brice Corgnet & Mark DeSantis & David Porter, 2015. "Revisiting Information Aggregation in Asset Markets: Reflective Learning & Market Efficiency," Working Papers 15-15, Chapman University, Economic Science Institute.
    35. Jürgen Huber & Martin Angerer & Michael Kirchler, 2011. "Experimental asset markets with endogenous choice of costly asymmetric information," Experimental Economics, Springer;Economic Science Association, vol. 14(2), pages 223-240, May.
    36. repec:reg:rpubli:460 is not listed on IDEAS
    37. Kirchler, Michael, 2010. "Partial knowledge is a dangerous thing - On the value of asymmetric fundamental information in asset markets," Journal of Economic Psychology, Elsevier, vol. 31(4), pages 643-658, August.
    38. Page, Lionel & Siemroth, Christoph, 2017. "An experimental analysis of information acquisition in prediction markets," Games and Economic Behavior, Elsevier, vol. 101(C), pages 354-378.
    39. Copeland, Thomas E & Friedman, Daniel, 1992. "The Market Value of Information: Some Experimental Results," The Journal of Business, University of Chicago Press, vol. 65(2), pages 241-266, April.
    40. Huber, Jurgen, 2007. "`J'-shaped returns to timing advantage in access to information - Experimental evidence and a tentative explanation," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2536-2572, August.
    41. Hauser, Florian & Huber, Jürgen, 2012. "Short-selling constraints as cause for price distortions: An experimental study," Journal of International Money and Finance, Elsevier, vol. 31(5), pages 1279-1298.
    42. Rachel Croson & Uri Gneezy, 2009. "Gender Differences in Preferences," Journal of Economic Literature, American Economic Association, vol. 47(2), pages 448-474, June.
    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. Brice Corgnet & Cary Deck & Mark DeSantis & David Porter, 2020. "Forecasting Skills in Experimental Markets: Illusion or Reality?," Working Papers 2020, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    2. Ruiz-Buforn, Alba & Camacho-Cuena, Eva & Morone, Andrea & Alfarano, Simone, 2021. "Overweighting of public information in financial markets: A lesson from the lab," Journal of Banking & Finance, Elsevier, vol. 133(C).
    3. Brice Corgnet & Cary Deck & Mark Desantis & Kyle Hampton & Erik Kimbrough, 2019. "Reconsidering Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets," Working Papers halshs-02146611, HAL.
    4. Corgnet, Brice & DeSantis, Mark & Porter, David, 2021. "Information aggregation and the cognitive make-up of market participants," European Economic Review, Elsevier, vol. 133(C).
    5. Ruiz-Buforn, Alba & Alfarano, Simone & Camacho-Cuena, Eva & Morone, Andrea, 2020. "Single vs. multiple disclosures in an experimental asset market with information acquisition," MPRA Paper 101035, University Library of Munich, Germany.
    6. Ruiz-Buforn, Alba & Camacho-Cuena, Eva & Morone, Andrea & Alfarano, Simone, 2021. "Overweighting of public information in financial markets: A lesson from the lab," Journal of Banking & Finance, Elsevier, vol. 133(C).
    7. Ahrash Dianat & Christoph Siemroth, 2021. "Improving decisions with market information: an experiment on corporate prediction markets," Experimental Economics, Springer;Economic Science Association, vol. 24(1), pages 143-176, March.
    8. Brice Corgnet & Mark DeSantis & David Porter, 2020. "Information Aggregation and the Cognitive Make-up of Traders," Working Papers 20-18, Chapman University, Economic Science Institute.
    9. Lunawat, Radhika, 2021. "Learning from trading activity in laboratory security markets with higher-order uncertainty," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 90(C).
    10. Vorstaz, Marc & Lopes Moreira Da Veiga, María Helena & Peeters, Ronald, 2020. "Contagion in sequential financial markets: an experimental analysis," DES - Working Papers. Statistics and Econometrics. WS 31230, Universidad Carlos III de Madrid. Departamento de Estadística.

    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. Corgnet, Brice & DeSantis, Mark & Porter, David, 2021. "Information aggregation and the cognitive make-up of market participants," European Economic Review, Elsevier, vol. 133(C).
    2. Corgnet, Brice & DeSantis, Mark & Porter, David, 2020. "The distribution of information and the price efficiency of markets," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    3. Brice Corgnet & Mark DeSantis & David Porter, 2020. "Information Aggregation and the Cognitive Make-up of Traders," Working Papers 20-18, Chapman University, Economic Science Institute.
    4. Edward Halim & Yohanes E. Riyanto & Nilanjan Roy, 2019. "Costly Information Acquisition, Social Networks, and Asset Prices: Experimental Evidence," Journal of Finance, American Finance Association, vol. 74(4), pages 1975-2010, August.
    5. Robert Merl, 2021. "Literature Review of Experimental Asset Markets with Insiders," Working Paper Series, Social and Economic Sciences 2021-04, Faculty of Social and Economic Sciences, Karl-Franzens-University Graz.
    6. Marquardt, Philipp & Noussair, Charles N & Weber, Martin, 2019. "Rational expectations in an experimental asset market with shocks to market trends," European Economic Review, Elsevier, vol. 114(C), pages 116-140.
    7. Brice Corgnet & Mark Desantis & David Porter, 2018. "What Makes a Good Trader? On the Role of Intuition and Reflection on Trader Performance," Journal of Finance, American Finance Association, vol. 73(3), pages 1113-1137, June.
    8. Lionel Page & Christoph Siemroth & Itay Goldstein, 2021. "How Much Information Is Incorporated into Financial Asset Prices? Experimental Evidence," Review of Financial Studies, Society for Financial Studies, vol. 34(9), pages 4412-4449.
    9. Brice Corgnet & Cary Deck & Mark DeSantis & David Porter, 2020. "Forecasting Skills in Experimental Markets: Illusion or Reality?," Working Papers 2020, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    10. Brice Corgnet & Mark DeSantis & David Porter, 2015. "Revisiting Information Aggregation in Asset Markets: Reflective Learning & Market Efficiency," Working Papers 15-15, Chapman University, Economic Science Institute.
    11. Utz Weitzel & Christoph Huber & Florian Lindner & Jürgen Huber & Julia Rose & Michael Kirchler, 2018. "Bubbles and financial professionals," Working Papers 2018-04, Faculty of Economics and Statistics, University of Innsbruck, revised Oct 2018.
    12. Ruiz-Buforn, Alba & Camacho-Cuena, Eva & Morone, Andrea & Alfarano, Simone, 2021. "Overweighting of public information in financial markets: A lesson from the lab," Journal of Banking & Finance, Elsevier, vol. 133(C).
    13. Brice Corgnet & Cary Deck & Mark Desantis & Kyle Hampton & Erik Kimbrough, 2019. "Reconsidering Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets," Working Papers halshs-02146611, HAL.
    14. Corgnet, Brice & Hernán-González, Roberto & Kujal, Praveen, 2020. "On booms that never bust: Ambiguity in experimental asset markets with bubbles," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    15. Lawrence Choo & Todd R. Kaplan & Ro’i Zultan, 2019. "Information aggregation in Arrow–Debreu markets: an experiment," Experimental Economics, Springer;Economic Science Association, vol. 22(3), pages 625-652, September.
    16. Ackert, Lucy F. & Church, Bryan K. & Shehata, Mohamed, 1997. "Market behavior in the presence of costly, imperfect information: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 33(1), pages 61-74, May.
    17. Page, Lionel & Siemroth, Christoph, 2017. "An experimental analysis of information acquisition in prediction markets," Games and Economic Behavior, Elsevier, vol. 101(C), pages 354-378.
    18. Ackert, Lucy F. & Church, Bryan K. & Zhang, Ping, 2018. "Informed traders’ performance and the information environment: Evidence from experimental asset markets," Accounting, Organizations and Society, Elsevier, vol. 70(C), pages 1-15.
    19. Ciril Bosch-Rosa & Thomas Meissner & Antoni Bosch-Domènech, 2018. "Cognitive bubbles," Experimental Economics, Springer;Economic Science Association, vol. 21(1), pages 132-153, March.
    20. Robert Merl & Thomas Stöckl & Stefan Palan, 2021. "Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions," Working Paper Series, Social and Economic Sciences 2021-03, Faculty of Social and Economic Sciences, Karl-Franzens-University Graz.

    More about this item

    Keywords

    Prediction Markets; Information Acquisition; Laboratory Experiments; Behavioral Finance;
    All these keywords.

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • G1 - Financial Economics - - General Financial Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:gat:wpaper:1735. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/gateefr.html .

    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: Nelly Wirth (email available below). General contact details of provider: https://edirc.repec.org/data/gateefr.html .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.