Designing Prediction Markets to Achieve Convergence Speed

  • Pannate Jongpanichkultorn Chulalongkorn University
  • Daricha Sutivong Chulalongkorn University
  • Prabhas Chongstitvatana Chulalongkorn University

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Abstract

The aim of this paper is twofold: to propose the model of artificial prediction markets that capture the characteristics of real prediction markets and to study the impact of key parameters on the performance of the proposed markets. In the experiments, the artificial markets are implemented and the market performance in terms of convergence speed is measured. Our experimental results show that the number of traders and the mean value of initial belief have no significant impact on the convergence speed. However, the trader’s memory size impacts negatively on the convergence because of its delay in adjusting to the true value. Finally, the external information transmission rate and the ratio of smart traders have positive impacts on the convergence of the prediction markets. The insights can assist a market maker in designing and constructing more efficient prediction markets.

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Author Biographies
Pannate Jongpanichkultorn

Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand

Daricha Sutivong

Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand

Prabhas Chongstitvatana

Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand

Published
Vol 22 No 4, Jul 31, 2018
How to Cite
P. Jongpanichkultorn, D. Sutivong, and P. Chongstitvatana, “Designing Prediction Markets to Achieve Convergence Speed”, Eng. J., vol. 22, no. 4, pp. 177-190, Jul. 2018.

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