Biased Learning Creates Overconfidence  

作　　者：NI Xuanming WU Chen ZHAO Huimin 

机构地区：[1]School of Software and Microelectronics,Peking University,Beijing 100871,China [2]Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China [3]University of Chinese Academy of Sciences,Beijing 100049,China. [4]Business School,Sun Yat-Sen University,Guangzhou 510275,China

出　　处：《Journal of Systems Science & Complexity》2018年第6期1603-1617,共15页系统科学与复杂性学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant Nos.71303265and 71573289;the Innovative Research Group Project of National Natural Science Foundation of China under Grant No.71721001

摘　　要：The aim of this paper is to develop a multi-period economic model to interpret how the people become overconfident by a biased learning that people tend to attribute the success to their abilities and failures to other factors.The authors suppose that the informed trader does not know the distribution of the precision of his private signal and updates his belief on the distribution of the precision of his knowledge by Bayer's rule.The informed trader can eventually recognize the value of the precision of his knowledge after an enough long time biased learning,but the value is overestimated which leads him to be overconfident.Furthermore,based on the definition on the luckier trader who succeeds the same times but has the larger variance of the knowledge,the authors find that the luckier the informed trader is,the more overconfident he will be;the smaller the biased learning factor is, the more overconfident the informed trader is.The authors also obtain a linear equilibrium which can explain some anomalies in financial markets,such as the high observed trading volume and excess volatility.The aim of this paper is to develop a multi-period economic model to interpret how the people become overconfident by a biased learning that people tend to attribute the success to their abilities and failures to other factors. The authors suppose that the informed trader does not know the distribution of the precision of his private signal and updates his belief on the distribution of the precision of his knowledge by Bayer’s rule. The informed trader can eventually recognize the value of the precision of his knowledge after an enough long time biased learning, but the value is overestimated which leads him to be overconfident. Furthermore, based on the definition on the luckier trader who succeeds the same times but has the larger variance of the knowledge, the authors find that the luckier the informed trader is, the more overconfident he will be; the smaller the biased learning factor is,the more overconfident the informed trader is. The authors also obtain a linear equilibrium which can explain some anomalies in financial markets, such as the high observed trading volume and excess volatility.

关 键 词：Bayes'rule biased LEARNING OVERCONFIDENCE 

分 类 号：O1[理学—数学]