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【信息通知】香港中文大学MORIS STRUB学术报告通知

2018-8-29 19:45:18

 

【报告题目】: Portfolio Selection Under Loss Aversion with a Mentally Adjusted Reference Point

【报告人】:MORIS STRUB 博士 香港中文大学

【报告时间】:2018年9月5日15:00-17:00

【报告地点】:大学生活动中心308

【报告人简介】:

Dr. Moris Strub received his PhD in Financial Engineering from the Chinese University of Hong Kong in 2018after obtaining a BSc in Mathematics and a MSc in Applied Mathematics from ETH Z¨ urich, both with distinction.He currently is a postdoctoral researcher at the Chinese University of Hong Kong.His research interests includeportfolio selection problems in behavioral finance and economics,preference formation and updating and risk management.

【报告内容简介】:

Most models on portfolio selection under loss aversion make a strong assumption on what the reference point is or how it is formed. In this paper, we relax this assumption by proposing three models where the investor mentally adjusts an initial, exogenous reference point: A model of mental reference point updating, where the process of determining an optimal solution influences the formation of the reference point, a model of a partially endogenous reference point, where the reference point is determined among a set of candidates through an equilibrium condition, and a model of a mentally optimal reference point, which strikes a balance between a completely exogenous reference point and a reference point fully determined by the chosen investment strategy in a predefined manner. We find that optimal trading behavior is remarkably robust with respect to mental reference point formation. Whereas the investor behaves as if her reference point were completely exogenous in the model of reference point updating, an investor with a partially endogenous or mentally optimal reference point exhibits a lower level of loss aversion. We further find that the optimal trading behavior of a loss averse investor with a mentally adjusted reference point converges to the neoclassical solution with increasing experience and sophistication of the investor under those two latter models.