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      科研管理 2019, Vol. 40 Issue       (9) :241-251 论文   DOI:
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       基于等截距变换雷达图的退市风险预警模型
       周颖,张舒明
        大连理工大学经济管理学院,辽宁 大连116024
       Delisting risk warning model based on equal intercept transformation radar map
       Zhou Ying, Zhang Shuming
       Faculty of Management and Economics, Dalian University of Technology, Dalian 116024, Liaoning, China
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摘要 退市风险警示制度是任何国家股市的一个重要制度。上市公司遇到退市风险警示,轻则会给个别公司的股价带来重大影响乃至至导致退市,重则会殃及相应版块甚至造成股市恐慌。本文通过引入等截距变换对现有的雷达图评价模型进行改进,建立基于改进型雷达图的退市风险预警模型。本文的创新与特色一是通过 “把相邻两个指标的顶点用共同的等截距上的顶点相连接而形成的多边形”的等截距变换对现有雷达图评价方法进行改进建立退市风险预警模型,确保了无论怎样改变指标排列次序、一个特定对象的评价结果是不变的、唯一的。解决了现有研究直接用相邻指标顶点连线构成的多边形的作法进而产生的“由于指标排列次序的变动而导致的同一个对象的评价结果截然不同”的问题。二是通过将上交所出台的《股票上市规则》中12种实施退市风险警示的情形作为指标体系中的全部指标,并把审计报告意见等非财务指标纳入评价体系,建立了符合真实市场退出制度的预警体系,改变了现有研究忽略上市公司退市规则中的非财务指标的不合理状况。并以三种*ST风险水平的上市公司为代表进行实证分析验证了本文建立的预警模型的合理性。
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周颖
张舒明
关键词退市风险   风险预警   雷达图   等截距变换     
Abstract

The delisting risk warning model is the key one for defending the normal order of the stock market and the interests of investors. It plays an important role in optimizing the allocation of resources in the capital market and in taking the health of the market. The delisting risk of any companies has a significant impact on its stock price, and even leads it to be delisted. Once the delisting early warning model is flawed, wrong predictions are inevitable. When the "good" company is predicted as a "bad "one, it will damage the shareholder rights and interests of the "good" company, even lead the company to be delisted from the capital market. And when the "bad" company is predicted as the "good "one, it will directly mislead the investors and the public, even makes the wrong investment decisions and causes to huge losses.
The method ofradar map(RC) presents the advantages of intuitive, uncomplicated, and interpretable in the comprehensive evaluation of the existing research. The drawback is that the evaluation results may be completely different and even the opposite when the order of indicators changes in radar map. This may be one of the reasons why radar map has not been applied to delisting early warning research and application. We overcome the drawbacks above, in order to apply the radar char method to establish the delisting early warning model.
The principle of using the equal interceptradar map(EIRC), our proposed in this paper, to carry out the delisting risk is as follows: we present the delisting risk by using the two areas in radar map enclosed respectively by the single indicator and all of indicators. The larger the area is, the greater the delisting risk is. When any one of the two areas enclosed approaches or reaches its maximum area value in the radar map, the delisting risk of the company by *ST is greater. On the contrary, the smaller the area in radar map is, the less delisting risk of the company by *ST is.
In this paper, we improve the RC based on equal intercept transformation and establish the delisting risk early warning model based on the improved RC. We perform polygon by using EIRC which connect the vertices of two adjacent indicators through the points on the same equal intercept. The area of the polygon enclosed by EIRC is constant regardless of the order of the indicators. And to RC in existing research, the polygon is formed by connecting directly the vertexes of adjacent indicators. The area of the polygon enclosed by RC changes with the order of the indicators changing. This can cause the evaluation result of the same company to be completely different or opposite, if the order of the indicators changing.
In existing research on financial early warning, the main drawback has two folds. First, existing study do not pay attention to the non-financial indicators in delisting early warning research for Chinese listed company. In fact, non-financial indicator, such as “audit report opinions”, is the fundamental criterion of the delisting early warning. Taking the delisting risk earlywarning model in the Stock Exchange Listing Rules of the Shanghai Stock Exchange as an example, there are all 12 indicators, where including 4 financial indicators and 8 non-financial indicators. However, in the indicator system research of delisting risk early warning, existing researchers present either ignoring non-financial indicators or selecting many financial indicators that are not included in the current delisting rules. Therefore, the indicator system used by existing research does not match with actual *ST criterion of Chinese listed company.
Second, the predictive models in existing research, such as Probit regression, are essentially the ones for discriminant or determining the probability of the company by *ST. It requires a large number of samples to fit and estimate the parameters in model. It is easy to produce prediction errors when the sample size is limited and parameters cannot be provided.
This paper introduces all 12 delisting situations, including of 4 financial indicators and 8 non-financial ones, according to the Stock Listing Rules issued by Shanghai Stock Exchange. We establish the delisting earlywarning model in line with the real situations, and change the disadvantages of existing researches that are out of touch with the real delisting rules. We analyze three situations of risk by *ST for Chinese listed companies and verify the performance of EIRC model our proposed in this paper.
In the empirical research section, this paper predicts the risk by *ST of three Chinese listed company, including of the Anhui Heli Co., Ltd. (Anhui Heli), Kunming Machine Tool Co., Ltd. (Kunming Machine Tool), and Hongda Co., Ltd. (Hongda), by using their data in the past five years (2011-2015) respectively.
For Anhui Heli, empirical research shows that the risk by *ST of single indicator is very small because the area of each indicator is much smaller than the maximum area of single indicator in EIRC. The total risk by *ST is also small, because its total area value 0.00358 is much smaller than the maximum area of 1.55. From these two aspects, the risk by *ST for Anhui Heli in 2016 is very small, that is, the possibility by *ST is negligible.
For Kunming Machine Tool, although the total area value 0.1517 is much smaller than the maximum area value 1.55, the area of the indicator “net profit” reaches the maximum value 0.1294. It will be *ST in 2016, because it meets the situation of “The audited net profit of the last two fiscal years is continuously negative or continuously negative after being retrospectively re-stated” which is the *ST rule of Shanghai Stock Exchange.
For Hongda, the area of the indicator “audit report opinion” reaches 0.10352, which is close to the maximum value 0.1294 of single indicator. This presents that the risk of the indicator “audit report opinion” is larger. The *ST risk of every other indicator and total risk are both lower. Although these do not meet the situations of early warnings by *ST, its management should still take measures to improve the corresponding processing of “audit report opinion” to reduce the risk by *ST.

Keywordsdelisting risk   risk warning   radar map   equal intercept transformation     
Received 2017-06-30; published 2019-09-16
Fund:国家社科基金一般项目(16BTJ017,起止时间:2016.06-2019.12)。
Corresponding Authors: 周颖     Email: zhouying@dlut.edu.cn
引用本文:   
周颖 张舒明.基于等截距变换雷达图的退市风险预警模型[J]  科研管理, 2019,V40(9): 241-251
Zhou Ying, Zhang Shuming.Delisting risk warning model based on equal intercept transformation radar map[J]  Science Research Management, 2019,V40(9): 241-251
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