公共信息、内部启发性对监管市场均衡的影响  被引量:2

The impact of public information and insider heuristic on equilibrium in regulatory insider trading market

在线阅读下载全文

作  者:张铁红 周永辉 ZHANG Tiehong;ZHOU Yonghui(School of Mathematical Sciences,Guizhou Normal University,Guiyang,Guizhou 550001,China;School of Big Data and Computer Sciences,Guizhou Normal University,Guiyang,Guizhou 550001,China)

机构地区:[1]贵州师范大学数学科学学院,贵州贵阳550001 [2]贵州师范大学大数据与计算机科学学院,贵州贵阳550001

出  处:《贵州师范大学学报(自然科学版)》2020年第3期76-82,共7页Journal of Guizhou Normal University:Natural Sciences

基  金:国家自然科学基金(No.11861025);贵州省科技计划项目(黔科合平台人才[2018]5769号)。

摘  要:主要研究理性内部交易者、启发性内部交易者和做市商均收到部分风险资产公共信息时的内部交易监管市场,证明了该市场的最优交易策略、有效定价规则和最优监管力度组成的线性Bayesian-Nash均衡的存在唯一性。结果表明:当市场处于该线性均衡时,1)启发性内部交易者的利润总是高于理性内部交易者的利润;2)当公共信息越偏离资产的真实信息程度越大时,理性内部交易者的利润越多,噪声交易者损失更大,市场深度越小,监管力度越强;3)当启发性内部交易者重视信息程度越大时,理性内部交易者利润越小,而启发性内部交易者利润越大,市场监管力度越强,但是噪声交易者损失不受影响。This paper mainly studies a regulatory market when a rational insider,a heuristic insider and a market maker all receive the same public information,and proves the existence and uniqueness of linear Bayesian-Nash equilibrium consisting of the optimal trading strategy,effective pricing rule and optimal regulation.It shows that in the linear equilibrium,1)the profit of the heuristic insider is always higher than that of the rational insider;2)the more degree of public information deviating from the real information of the asset,the smaller the profit of the rational insider is,the smaller the loss of noise traders is,the weaker the market liquidity is,and the stronger the regulation is;3)the more attention the heuristic insider paying to the information,the smaller the profit of the rational insider is,the more the profit of heuristic insider is and the stronger the market regulation is,while the loss of noise traders will not be affected.

关 键 词:理性内部交易者 启发性内部交易者 公共信息 市场监管 Bayesian-Nash均衡 

分 类 号:O211[理学—概率论与数理统计]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象