IDEAS home Printed from https://ideas.repec.org/a/spr/jeicoo/v18y2023i2d10.1007_s11403-022-00365-6.html
   My bibliography  Save this article

“Less is more” or “more is better”? The effect of asymmetric information distribution on market efficiency and wealth inequality

Author

Listed:
  • Rocco Caferra

    (University of Bari Aldo Moro
    Unitelma Sapienza University of Rome)

  • Simone Nuzzo

    (University of Bari Aldo Moro)

  • Andrea Morone

    (University of Bari Aldo Moro)

Abstract

We provide experimental and empirical evidence on the role of information distribution and accuracy in solving market inefficiency and the related welfare implications. To this end, we vary both (i) the informativeness of the private signal released and (ii) the fraction of informed traders. At market level, results evidence how higher informativeness is not conclusive, since mispricing persists. Furthermore, a reduction of signal precision reflects traders’ uncertainty aversion through lower price volatility. Results are robust when both all and half of the players are informed (i.e., received the signals). At subject level, we observe that informed traders are gaining more profits. Anyway, the signal precision and distribution do not statistically change the profits of both net market-winners and net market-losers. This suggests that—on average—the intensity of asymmetric information distribution (i.e., the size of the information imbalance between informed and uniformed traders) does not alter the wealth distribution.

Suggested Citation

  • Rocco Caferra & Simone Nuzzo & Andrea Morone, 2023. "“Less is more” or “more is better”? The effect of asymmetric information distribution on market efficiency and wealth inequality," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(2), pages 233-250, April.
    • Handle: RePEc:spr:jeicoo:v:18:y:2023:i:2:d:10.1007_s11403-022-00365-6
    DOI: 10.1007/s11403-022-00365-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11403-022-00365-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11403-022-00365-6?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Day, Richard H. & Huang, Weihong, 1990. "Bulls, bears and market sheep," Journal of Economic Behavior & Organization, Elsevier, vol. 14(3), pages 299-329, December.
    2. 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.
    3. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
    4. Sunder, Shyam, 1992. "Market for Information: Experimental Evidence," Econometrica, Econometric Society, vol. 60(3), pages 667-695, May.
    5. Larry G. Epstein & Martin Schneider, 2008. "Ambiguity, Information Quality, and Asset Pricing," Journal of Finance, American Finance Association, vol. 63(1), pages 197-228, February.
    6. Thomas Lux & Michele Marchesi, 2000. "Volatility Clustering In Financial Markets: A Microsimulation Of Interacting Agents," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 675-702.
    7. Huber, Jurgen & Kirchler, Michael & Sutter, Matthias, 2008. "Is more information always better: Experimental financial markets with cumulative information," Journal of Economic Behavior & Organization, Elsevier, vol. 65(1), pages 86-104, January.
    8. Kirchler, Michael & Huber, Jurgen, 2007. "Fat tails and volatility clustering in experimental asset markets," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1844-1874, June.
    9. 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.
    10. Iori, Giulia, 2002. "A microsimulation of traders activity in the stock market: the role of heterogeneity, agents' interactions and trade frictions," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 269-285, October.
    11. Chen, Joseph & Hong, Harrison & Stein, Jeremy C., 2001. "Forecasting crashes: trading volume, past returns, and conditional skewness in stock prices," Journal of Financial Economics, Elsevier, vol. 61(3), pages 345-381, September.
    12. Copeland, Thomas E & Friedman, Daniel, 1991. "Partial Revelation of Information in Experimental Asset Markets," Journal of Finance, American Finance Association, vol. 46(1), pages 265-295, March.
    13. Charles Noussair & Yilong Xu, 2015. "Information mirages and financial contagion in an asset market experiment," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 42(6), pages 1029-1055, November.
    14. Olivier Brandouy & Pascal Barneto & Lawrence Leger, 2003. "Asymmetric information, imitative behaviour and communication: price formation in an experimental asset market," The European Journal of Finance, Taylor & Francis Journals, vol. 9(5), pages 393-419.
    15. Charles R. Plott & Vernon L. Smith, 1978. "An Experimental Examination of Two Exchange Institutions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 45(1), pages 133-153.
    16. Benoit Mandelbrot, 1963. "New Methods in Statistical Economics," Journal of Political Economy, University of Chicago Press, vol. 71(5), pages 421-421.
    17. Giovanni Ferri & Andrea Morone, 2014. "The effect of rating agencies on herd behaviour," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 9(1), pages 107-127, April.
    18. John Y. Campbell & Tarun Ramadorai & Benjamin Ranish, 2019. "Do the Rich Get Richer in the Stock Market? Evidence from India," American Economic Review: Insights, American Economic Association, vol. 1(2), pages 225-240, September.
    19. Te Bao & John Duffy, 2021. "Signal extraction: experimental evidence," Theory and Decision, Springer, vol. 90(2), pages 219-232, March.
    20. Barreda Tarrazona, Iván J. & Grimalda, Gianluca & Morone, Andrea & Nuzzo, Simone & Teglio, Andrea, 2017. "Centralizing information improves market efficiency more than increasing information: Results from experimental asset markets," Kiel Working Papers 2072, Kiel Institute for the World Economy (IfW Kiel).
    21. Thomas Lux & Michele Marchesi, 1999. "Scaling and criticality in a stochastic multi-agent model of a financial market," Nature, Nature, vol. 397(6719), pages 498-500, February.
    22. 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).
    23. Charles Noussair & Yilong Xu, 2015. "Information mirages and financial contagion in an asset market experiment," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 42(6), pages 1029-1055, November.
    24. Farmer, J. Doyne & Joshi, Shareen, 2002. "The price dynamics of common trading strategies," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 149-171, October.
    25. Kirchler, Michael, 2009. "Underreaction to fundamental information and asymmetry in mispricing between bullish and bearish markets. An experimental study," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 491-506, February.
    26. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    27. Frederic S. Mishkin, 1991. "Asymmetric Information and Financial Crises: A Historical Perspective," NBER Chapters, in: Financial Markets and Financial Crises, pages 69-108, National Bureau of Economic Research, Inc.
    28. repec:grz:wpsses:2021-04 is not listed on IDEAS
    29. 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.
    30. Naser Abumustafa & Salah Nusair, 2011. "Insider trading during the 2008 financial crisis," Applied Financial Economics, Taylor & Francis Journals, vol. 21(5), pages 301-307.
    31. Beja, Avraham & Goldman, M Barry, 1980. "On the Dynamic Behavior of Prices in Disequilibrium," Journal of Finance, American Finance Association, vol. 35(2), pages 235-248, May.
    32. Alfarano, Simone & Camacho, Eva & Morone, Andrea, 2015. "Do investors rely too much on public information to be justified by its accuracy? An experimental study," FinMaP-Working Papers 30, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    33. Porter, David P & Smith, Vernon L, 1995. "Futures Contracting and Dividend Uncertainty in Experimental Asset Markets," The Journal of Business, University of Chicago Press, vol. 68(4), pages 509-541, October.
    34. Andrea Morone, 2008. "Financial markets in the laboratory: an experimental analysis of some stylized facts," Quantitative Finance, Taylor & Francis Journals, vol. 8(5), pages 513-532.
    Full references (including those not matched with items on IDEAS)

    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. Nuzzo, Simone & Morone, Andrea, 2017. "Asset markets in the lab: A literature review," Journal of Behavioral and Experimental Finance, Elsevier, vol. 13(C), pages 42-50.
    2. Merl, Robert, 2022. "Literature review of experimental asset markets with insiders," Journal of Behavioral and Experimental Finance, Elsevier, vol. 33(C).
    3. repec:grz:wpsses:2021-04 is not listed on IDEAS
    4. Rocco Caferra & Gabriele Tedeschi & Andrea Morone, 2023. "Agents interaction and price dynamics: evidence from the laboratory," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(2), pages 251-274, April.
    5. Morone, Andrea & Caferra, Rocco, 2020. "Inequalities in financial markets: Evidences from a laboratory experiment," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 88(C).
    6. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2001. "Microscopic Models of Financial Markets," Papers cond-mat/0110354, arXiv.org.
    7. Kirchler, Michael, 2009. "Underreaction to fundamental information and asymmetry in mispricing between bullish and bearish markets. An experimental study," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 491-506, February.
    8. Xue-Zhong He, 2003. "Asset Pricing, Volatility and Market Behaviour: A Market Fraction Approach," Research Paper Series 95, Quantitative Finance Research Centre, University of Technology, Sydney.
    9. Kirchler, Michael & Huber, Jürgen, 2009. "An exploration of commonly observed stylized facts with data from experimental asset markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1631-1658.
    10. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    11. Youwei Li & Xue-Zhong He, 2005. "Long Memory, Heterogeneity, and Trend Chasing," Computing in Economics and Finance 2005 113, Society for Computational Economics.
    12. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    13. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    14. Simone Alfarano & Thomas Lux, 2007. "A Minimal Noise Trader Model with Realistic Time Series Properties," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 345-361, Springer.
    15. Torsten Trimborn & Philipp Otte & Simon Cramer & Max Beikirch & Emma Pabich & Martin Frank, 2018. "SABCEMM-A Simulator for Agent-Based Computational Economic Market Models," Papers 1801.01811, arXiv.org, revised Oct 2018.
    16. He, Xue-Zhong & Li, Youwei, 2007. "Power-law behaviour, heterogeneity, and trend chasing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(10), pages 3396-3426, October.
    17. Youwei Li & Xue-Zhong (Tony) He, 2005. "Heterogeneity, Profitability and Autocorrelations," Computing in Economics and Finance 2005 244, Society for Computational Economics.
    18. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.
    19. Morone, Andrea & Nuzzo, Simone, 2015. "Market Efficiency, Trading Institutions and Information Mirages: evidence from an experimental asset market," MPRA Paper 67448, University Library of Munich, Germany.
    20. Hommes, C.H., 2005. "Heterogeneous Agents Models: two simple examples, forthcoming In: Lines, M. (ed.) Nonlinear Dynamical Systems in Economics, CISM Courses and Lectures, Springer, 2005, pp.131-164," CeNDEF Working Papers 05-01, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    21. Simon Cramer & Torsten Trimborn, 2019. "Stylized Facts and Agent-Based Modeling," Papers 1912.02684, arXiv.org.

    More about this item

    Keywords

    Experimental finance; Asymmetric information; Wealth inequality; Market efficiency;
    All these keywords.

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • G1 - Financial Economics - - General Financial Markets
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty

    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:spr:jeicoo:v:18:y:2023:i:2:d:10.1007_s11403-022-00365-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.