Evolutionary Selection of Forecasting and Quantity Decision Rules in Experimental Asset Markets

Jiahua Zhu, Te Bao and Wai Mun Chia
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Jiahua Zhu: Department of Economics, Nanyang Technological University, Singapore
Te Bao: Department of Economics, Nanyang Technological University, Singapore
Wai Mun Chia: Department of Economics, Nanyang Technological University, Singapore

No 1807, Economic Growth Centre Working Paper Series from Nanyang Technological University, School of Social Sciences, Economic Growth Centre

Abstract: Bao etal.(2017) find that bubbles are less likely to emerge in experimental asset markets when subjects make price forecasts only(LearningtoForecast treatment, LtF) than when they make trading quantity decisions (Learning to Optimize treatment, LtO or both price forecasts and quantity decisions (mixed treatment). This paper provides two explanations for this difference. First, the subjects in the LtO and mixed treatment usually have a high intensity of choice parameter, which leads them to switch faster between the decision rules and a greater fraction of the population to choose the destabilizing strong trend-following rule. Second, the actual quantity decision may deviate substantially and persistently from the conditionally optimal level given the price forecasts in the mixed treatment, which amplifies the price deviation from thefundamental value. Our findings are helpful for understanding the root of financial bubbles and financial crisis, and designing policies to stabilize the market.

Keywords: Asset Bubble; Heuristic Switching Model; Learning to Forecast; Learning to Optimize; Experimental Economics (search for similar items in EconPapers)
JEL-codes: C91 C92 D83 D84 R30 (search for similar items in EconPapers)
Pages: 53 pages
Date: 2018-07
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