管理评论

管理评论 ›› 2020, Vol. 32 ›› Issue (7): 166-179.

• 中国系统管理学专辑 • 上一篇    下一篇

基于TEI@I方法的中国保险业保费收入预测

周桦1, 卢志源2, 郑敏3   

  1. 1. 中央财经大学保险学院/中国精算研究院, 北京 100081;
    2. 中央财经大学保险学院, 北京 100081;
    3. 中央财经大学中国精算研究院, 北京 100081
  • 收稿日期:2019-09-16 出版日期:2020-07-28 发布日期:2020-08-08
  • 通讯作者: 郑敏(通讯作者),中央财经大学中国精算研究院副研究员,硕士生导师,博士
  • 作者简介:周桦,中央财经大学保险学院/中国精算研究院教授,硕士生导师,博士;卢志源,中央财经大学保险学院硕士研究生。
  • 基金资助:
    国家社会科学基金一般项目（18BJY255）；国家自然科学基金面上项目（71571197）；高等学校学科创新引智计划资助项目（B17050）。

Forecasting Premium Income of China's Insurance Industry Based on TEI@I Methodology

Zhou Hua1, Lu Zhiyuan2, Zheng Min3   

  1. 1. School of Insurance/China Institute for Actuarial Science, Central University of Finance and Economics, Beijing 100081;
    2. School of Insurance, Central University of Finance and Economics, Beijing 100081;
    3. China Institute for Actuarial Science, Central University of Finance and Economics, Beijing 100081
  • Received:2019-09-16 Online:2020-07-28 Published:2020-08-08

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摘要： 本文基于TEI@I方法论，使用集成经济计量模型、文本挖掘和机器学习的分析框架，构建中国保险业保费收入的预测模型。该模型首先运用季节调整SARIMA模型对保费收入的主要趋势进行拟合，再使用机器学习中的支持向量回归方法对SARIMA模型的残差进行拟合，并运用文本挖掘技术增加相关的百度指数作为解释变量以提升模型的拟合度，最后再次使用支持向量回归整合拟合结果形成一个集成模型，从而得到精度更高的保费收入预测模型。本文利用我国月度保费收入数据，通过模型比较研究，验证了TEI@I方法在我国保费收入预测中的有效性与稳健性。

关键词: TEI@I方法论, 保费收入预测, SARIMA, 支持向量回归

Abstract: Premium income is the most important basic indicator reflecting the development level of an insurance industry in a country or region. It reflects the insurance demand of residents and the overall size of the insurance market in the country or region. At the same time, premium income is also a key variable of insurance density and insurance penetration. Based on TEI@I methodology, within the integrated frameworks of econometric model, text mining and machine learning, this paper constructs a model for forecasting premium income of China's insurance industry. In this model, this paper first uses the season-adjusted model, SARIMA, to fit the main trend of premium income. Then, this paper uses the support vector regression, which is a method in machine learning, to fit the residual of SARIMA. Under the guidance of TEI@I methodology, in order to improve the fitness of the model, this paper adds the related Baidu index through the text mining technology into explanatory variables. At last, this paper uses the support vector regression again to integrate the fitting results, so as to obtain an integrated forecasting model of premium income with a higher precision. Through the model comparison and based on the data of China's premium income, this paper verifies the effectiveness and robustness of the TEI@I methodology in China's premium income forecasting research.

Key words: TEI@I methodology, premium income forecast, SARIMA, support vector regression