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基于主导上市公司案例的中国健康保险公司在新冠疫情期间的公平绩效的实证研究

An Empirical Study on the Equity Performance of China's Health Insurance Companies During the COVID-19 Pandemic-Based on Cases of Dominant Listed Companies.

机构信息

School of Economics, Hangzhou Dianzi University, Hangzhou, China.

Alibaba Business College, Hangzhou Normal University, Hangzhou, China.

出版信息

Front Public Health. 2021 May 10;9:663189. doi: 10.3389/fpubh.2021.663189. eCollection 2021.

Abstract

The health insurance industry in China is undergoing great shocks and profound impacts induced by the worldwide COVID-19 pandemic. Taking for instance the three dominant listed companies, namely, China Life Insurance, Ping An Insurance, and Pacific Insurance, this paper investigates the equity performances of China's health insurance companies during the pandemic. We firstly construct a stock price forecasting methodology using the autoregressive integrated moving average, back propagation neural network, and long short-term memory (LSTM) neural network models. We then empirically study the stock price performances of the three listed companies and find out that the LSTM model does better than the other two based on the criteria of mean absolute error and mean square error. Finally, the above-mentioned models are used to predict the stock price performances of the three companies.

摘要

中国的健康保险业正受到全球 COVID-19 大流行带来的巨大冲击和深远影响。以中国人寿、中国平安和太平洋保险这三家龙头上市企业为例,本文研究了疫情期间中国健康保险公司的股票表现。我们首先使用自回归整合移动平均、反向传播神经网络和长短时记忆(LSTM)神经网络模型构建了股票价格预测方法。然后,我们实证研究了这三家上市公司的股票价格表现,发现基于平均绝对误差和均方误差的标准,LSTM 模型优于其他两个模型。最后,我们使用上述模型预测了这三家公司的股票价格表现。

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