多维视域下资本市场A股评级与应用研究

Study on A-share Ratings and Applications in the Capital Market from a Multidimensional Perspective

我国上市股票数量众多,目前高达5300多只。随着我国全面注册制的推行,上市公司的质量出现分化。因此,对上市公司财务状况、二级市场波动以及各类舆情等信息的识别、处理和追踪,需要通过模型来自动实现。本文从上市公司所属行业景气程度、公司经营财务状况、二级市场估值及关注度研究、公司事件等四个维度,对我国A股市场股票进行评级,实现对上市公司的整体评价。通过多维度的分析考量,不同评级股票未来涨跌幅具有明显区别,基于模型的研究结果,选取优质股票可获取年化约8.4%的收益率,同时对历史上出现风险的股票识别率可达到90%以上,以实现对重大风险股票的提前预警。研究结果发现,通过对公开披露的财务数据、市场表现及关注度等维度分析,可对上市公司经营情况进行评估,帮助上市公司发现自身风险,提高自身财务质量,促进我国证券市场长期健康发展。未来,可进一步引入其他有效因子,尝试其他统计学工具,并通过引入AI智能提升舆情识别的准确性,从而进一步提高模型识别的准确率。

China has a large number of listed stocks,currently reaching over 5300.With the implementation of the comprehensive registration system in our country,the quality of listed companies has differentiated.Therefore,the identification,processing,and tracking of financial status,secondary market fluctuations,and various public opinion information of listed companies need to be automatically achieved through models.This article evaluates the A-share market stocks in China from four dimensions:the prosperity level of the industry to which the listed companies belong,the financial status of the company's operations,research on secondary market valuation and attention,and company events,in order to achieve an overall evaluation of the listed companies.Through multidimensional analysis and consideration,there are significant differences in the future fluctuations of stocks with different ratings.Based on the research results of the model,selecting high-quality stocks can achieve an annualized return of about 8.4%.At the same time,the identification rate of stocks with historical risks can reach over 90%,in order to achieve early warning of major risk stocks.The research results found that by analyzing publicly disclosed financial data,market performance,and attention,the operational situation of listed companies can be evaluated,helping them identify their own risks,improve their financial quality,and promote the long-term healthy development of China's securities market.In the future,other effective factors can be further introduced,other statistical tools can be tried,and the accuracy of public opinion recognition can be improved by introducing AI intelligence,to further improv the accuracy of model recognition.