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Call Market Experiments: Efficiency and Price Discovery through Multiple Calls and Emergent Newton Adjustments

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  • Charles R. Plott
  • Kirill Pogorelskiy

Abstract

We study multiple-unit, laboratory experimental call markets in which orders are cleared by a single price at a scheduled "call." The markets are independent trading "days" with two calls each day preceded by a continuous and public order flow. Markets approach the competitive equilibrium over time. The price formation dynamics operate through the flow of bids and asks configured as the "jaws" of the order book with contract execution featuring elements of an underlying mathematical principle, the Newton-Raphson method for solving systems of equations. Both excess demand and its slope play a systematic role in call market price discovery.

Suggested Citation

  • Charles R. Plott & Kirill Pogorelskiy, 2017. "Call Market Experiments: Efficiency and Price Discovery through Multiple Calls and Emergent Newton Adjustments," American Economic Journal: Microeconomics, American Economic Association, vol. 9(4), pages 1-41, November.
    • Handle: RePEc:aea:aejmic:v:9:y:2017:i:4:p:1-41
    Note: DOI: 10.1257/mic.20150201
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    Cited by:

    1. Collins, Sean M. & James, Duncan & Servátka, Maroš & Vadovič, Radovan, 2021. "Attainment of equilibrium via Marshallian path adjustment: Queueing and buyer determinism," Games and Economic Behavior, Elsevier, vol. 125(C), pages 94-106.
    2. Koji Kotani & Kenta Tanaka & Shunsuke Managi, 2019. "Which performs better under trader settings, double auction or uniform price auction?," Experimental Economics, Springer;Economic Science Association, vol. 22(1), pages 247-267, March.
    3. Charles R. Plott & Timothy N. Cason & Benjamin J. Gillen & Hsingyang Lee & Travis Maron, 2023. "General equilibrium methodology applied to the design, implementation and performance evaluation of large, multi-market and multi-unit policy constrained auctions," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 75(3), pages 641-693, April.
    4. Duffy, John & Rabanal, Jean Paul & Rud, Olga A., 2021. "The impact of ETFs in secondary asset markets: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 674-696.
    5. Kuhle, Wolfgang, 2021. "Equilibrium with computationally constrained agents," Mathematical Social Sciences, Elsevier, vol. 109(C), pages 77-92.
    6. Caginalp, Carey & Caginalp, Gunduz, 2018. "The quotient of normal random variables and application to asset price fat tails," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 457-471.
    7. Carey Caginalp & Gunduz Caginalp, 2019. "Derivation of non-classical stochastic price dynamics equations," Papers 1908.01103, arXiv.org, revised Aug 2020.
    8. Selten, Reinhard & Neugebauer, Tibor, 2019. "Experimental stock market dynamics: Excess bids, directional learning, and adaptive style-investing in a call-auction with multiple multi-period lived assets," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 209-224.
    9. Sarkissian, Jack, 2020. "Quantum coupled-wave theory of price formation in financial markets: Price measurement, dynamics and ergodicity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    10. Tobias Brünner & René Levínský, 2023. "Price discovery and gains from trade in asset markets with insider trading," The European Journal of Finance, Taylor & Francis Journals, vol. 29(3), pages 255-277, February.
    11. John Duffy & Jean Paul Rabanal & Olga A. Rud, 2019. "The Impact of ETFs on Asset Markets: Experimental Evidence," Working Papers 154, Peruvian Economic Association.
    12. Collins, Sean M. & James, Duncan & Servátka, Maroš & Vadovič, Radovan, 2020. "Attainment of Equilibrium: Marshallian Path Adjustment and Buyer Determinism," MPRA Paper 104103, University Library of Munich, Germany.
    13. Gunduz Caginalp, 2020. "Fat tails arise endogenously in asset prices from supply/demand, with or without jump processes," Papers 2011.08275, arXiv.org, revised Mar 2021.
    14. Caginalp, Carey & Caginalp, Gunduz, 2020. "Derivation of non-classical stochastic price dynamics equations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    15. Benjamin J. Gillen & Masayoshi Hirota & Ming Hsu & Charles R. Plott & Brian W. Rogers, 2021. "Divergence and convergence in Scarf cycle environments: experiments and predictability in the dynamics of general equilibrium systems," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 71(3), pages 1033-1084, April.
    16. Jack Sarkissian, 2020. "Quantum coupled-wave theory of price formation in financial markets: price measurement, dynamics and ergodicity," Papers 2002.04212, arXiv.org.
    17. Caginalp, Carey & Caginalp, Gunduz, 2019. "Price equations with symmetric supply/demand; implications for fat tails," Economics Letters, Elsevier, vol. 176(C), pages 79-82.
    18. Emiko Fukuda & Shuhei Sato & Junyi Shen & Ken-Ichi Shimomura & Takehiko Yamato, 2020. "Walrasian Dynamics with Endowment Changes: The Gale Example in a Laboratory Market Experiment," Discussion Paper Series DP2020-20, Research Institute for Economics & Business Administration, Kobe University, revised Apr 2021.

    More about this item

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D41 - Microeconomics - - Market Structure, Pricing, and Design - - - Perfect Competition
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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