股票市场中的市场磨坊依赖模式：基于多成分条件分布的可预测性和非对称性建模
Market Mill Dependence Pattern in the Stock Market: Modeling of Predictability and Asymmetry via Multi-Component Conditional Distribution
摘要：
最近的研究揭示了一些惊人的依赖模式，在高频股票价格动态特征之间的概率相互关系的两个后续的价格增量x（推动）和y（响应）所描述的二元概率分布P（x，y）[1，2，3，4]。有两个属性，P（x，y）的市场磨坊不对称性和可预测性，由于非零z形平均条件响应，是特别重要的。本文的主要目标是把一个模型复制的Z形平均条件响应和市场磨坊不对称的P（x，y）相对于轴y=0。本文对条件分布P（y）建立了一个基于多分量分析的概率模型|x）具有描述两种性质的推相关权重和平均值。市场磨坊的不对称性和可预测性之间的关系进行了讨论。一个可能的连接模型，以代理为基础的图片概述。
英文摘要：
Recent studies have revealed a number of striking dependence patterns in high frequency stock price dynamics characterizing probabilistic interrelation between two consequent price increments x (push) and y (response) as described by the bivariate probability distribution P(x,y) [1,2,3,4]. There are two properties, the market mill asymmetries of P(x,y) and predictability due to nonzero z-shaped mean conditional response, that are of special importance. Main goal of the present paper is to put together a model reproducing both the z-shaped mean conditional response and the market mill asymmetry of P(x,y) with respect to the axis y=0. We develop a probabilistic model based on a multi-component ansatz for conditional distribution P(y|x) with push-dependent weights and means describing both properties. A relationship between the market mill asymmetry and predictability is discussed. A possible connection of the model to agent-based picture is outlined.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
