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Construction of Control Rights Allocation Index of Listed Companies Based on Neural Network and Machine Learning

机译:基于神经网络和机器学习的上市公司控制权分配指标的构建

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Control power is a core issue that every listed company pays great attention to. The company’s shareholding structure directly affects the allocation of control rights. Therefore, the shareholding structure of listed companies is analyzed, and various factors related to the allocation of company control rights are discussed. It is very important to build indicators of control allocation of listed companies and improve the governance model of listed companies. Based on this, this article proposes to use neural networks and machine learning techniques to build related models and solve related problems. This article takes the control allocation index of listed companies on the SSE and SZSE platforms under good securities’ market conditions as the research object and takes the stock holding allocation of listed companies as a reference for the control allocation index. Combine sliding removal technology and approximate entropy with sample entropy, select the sliding window and sliding step size as 21 data, keep the sliding window unchanged, and calculate the approximate entropy and sample entropy of the sequence after removing 21 data for each sliding value to analyze the correlation between the rate of return, complexity, and effectiveness. The results of the study show that the mean and median of the majority shareholder’s equity pledge behavior are 0.249 and 0, respectively, and the mean and median of the majority shareholder’s equity pledge ratio are 0.147 and 0, respectively, indicating that 24.9% of the companies in the sample have major shareholder equity. Pledge is limited by sample data, and the proportion of major shareholders’ equity pledge is moderate, which means that there is a certain gap in the quality of internal control between companies.
机译:控制电源是一个核心问题,每一个上市公司十分重视。该公司的股权结构直接影响到控制权的分配。因此,上市公司的股权结构进行了分析,并关系到公司控制权配置的各种因素进行了讨论。它建立上市公司的控制权分配的指标和完善上市公司的治理模式是非常重要的。在此基础上,本文提出利用神经网络和机器学习技术来构建相关的模型和解决相关问题。本文以良好的证券的市场状况为研究对象根据上交所和深交所的平台对上市公司的控制分配指标,并采取股份制分配的上市公司作为控制指标分配的参考。结合滑动去除技术和近似熵与样本熵,选择滑动窗和滑动步长为21点的数据,保持该滑动窗口不变,并且对于每个滑动值来分析去除21个数据后计算该序列的近似熵和样本熵回报,复杂性和有效性的比率之间的相关性。研究结果显示,平均和中位数的大股东的股权质押行为的结果分别为0.147和0,是0.249和0,分别,平均值和中位数的大股东的股权质押比例,显示的是24.9%公司样本中有大股东的股权。当头通过样本数据的限制,主要股东权益承诺的比例是适中的,这意味着存在于企业之间的内部控制的质量一定差距。

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