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Open AccessArticle

An Improved MV Method for Stock Allocation Based on the State-Space Measure of Cognitive Bias with a Hybrid Algorithm

by Liwen Wang 1, Hecheng Wu 1,*, Gang Li 2 and Weixue Lu 1
1
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2
College of Economics, Northeastern University at Qinhuangdao, Qinhuangdao 066000, China
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(6), 1036; https://doi.org/10.3390/sym12061036
Received: 25 May 2020 / Revised: 10 June 2020 / Accepted: 17 June 2020 / Published: 20 June 2020
In classical finance theory, cognitive bias does not play any role in predicting returns. With the development of the economy, the classical theory gradually finds it difficult to offset the irrational demand through arbitrage. Due to the rise of behavioral economics, how to allocate stock portfolios in the highly subjective environment is an unavoidable problem. Considering the decision heterogeneity between the rational market and the irrational one, the mean-variance (MV) method was improved in the construction of a market bias index for stock portfolio allocation, which we called EMACB (exponential moving average of cognitive bias)-variance method. Besides, due to the lack of related research, we introduced a measure of aggregate investor cognitive bias by adopting the state-space model. Finally, the proposed method was applied in an investment allocation example to prove its feasibility, and its advantages were emphasized by a comparison with another relevant approach. View Full-Text
Keywords: stock allocation; portfolio efficiency; cognitive bias; mean-variance method stock allocation; portfolio efficiency; cognitive bias; mean-variance method
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Wang, L.; Wu, H.; Li, G.; Lu, W. An Improved MV Method for Stock Allocation Based on the State-Space Measure of Cognitive Bias with a Hybrid Algorithm. Symmetry 2020, 12, 1036.

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