Financial bubbles: A learning effect modelling approach

Hsieh, Tsung Han; Li, Youwei; McKillop, Donal G.

Abstract

This chapter studies financial bubbles by incorporating a learning effect into the coordination game model which was articulated by Ozdenoren and Yuan [36]. Monte Carlo simulation is then utilised to analyse how the addition of a learning effect impacts upon the investment decision of informed investors as well as the formation of the aggregate investment. The simulation exercise demonstrates that both the learning effect and the feedback effect contribute to price multiplicity with price multiplicity observed when informed investors have more precise private information. The analysis emphasises that the learning effect is stronger in situations where informed investors act counter to the price signal and the actions of uninformed investors.

Citation

Hsieh, T. H., Li, Y., & McKillop, D. G. (2009). Financial bubbles: A learning effect modelling approach. In A. Brabazon, & M. O'Neill (Eds.), Natural computing in computational finance (117-135). Springer Verlag. https://doi.org/10.1007/978-3-540-95974-8_7

• Publication Date: Mar 1, 2009
• Deposit Date: Mar 19, 2019
• Journal: Natural Computing in Computational Finance; Studies in Computational Intelligence
• Print ISSN: 1860-949X
• Electronic ISSN: 1860-9503
• Publisher: Springer Verlag
• Pages: 117-135
• Series Title: Studies in Computational Intelligence
• Series Number: 185
• Book Title: Natural computing in computational finance
• ISBN: 9783540959731; 9783540959748
• DOI: https://doi.org/10.1007/978-3-540-95974-8_7
• Keywords: Private information; Risky asset; Aggregate demand; Coordination game; Private signal
• Public URL: https://hull-repository.worktribe.com/output/1390122
• Publisher URL: https://link.springer.com/chapter/10.1007%2F978-3-540-95974-8_7