non-ST Companies Research Articles

Early Warning of Financial and Business Management Based on Three Improved BP-NN Algorithms

To address the issue of early warning in financial management and economics, this article presents a study based on our improved BP-NN algorithms. This approach improves the benefits of early warning systems in financial and business management based on BP neural network algorithm technology and improves BP neural network algorithms. Based on the analysis and calculation of the results, the inconsistency of the financial model industry estimates is 66.3% and 72.7% for CT and non-CT companies. The actual discrimination rate of the hedge fund model is 81.3% and 83.9% for ST and ST companies, respectively. Compared with the net structure of the financial index, the general guidance model improved the ability of ST companies and non-ST companies to withstand risks by 14.27% and 8.76%, respectively. It can be concluded that the integration of nonfinancial indicators into the estimation model can improve the accuracy of the estimation of the model. Experiments have shown that research based on our improved BP-NN algorithms can not only eliminate BP network inadequacies but also improve the accuracy of early warning in financial markets.

Prediction of corporate financial distress based on digital signal processing and multiple regression analysis

Abstract In order to reduce the default rate of corporate bond market, the author proposes to use digital signal processing and multiple regression analysis to study the prediction system of financial distressed companies. First, design the research method, Logistic regression model is the most commonly used multivariate statistical method when modeling binary dependent variables, it can solve the problem of nonlinear classification, it has no specific requirements for the distribution of variables, and the accuracy of judgment is high. The author selects 32 financial ratios from the perspectives of solvency, operating ability, profitability, development ability, per share index, and risk level. Taking special treatment (ST) due to abnormal financial status as a sign of financial distress in listed companies, when selecting samples, the matching principle is adopted to select non-ST companies as matching samples. Two methods of logistic regression and support vector machine are used for empirical testing, and both in-sample testing and out-of-sample prediction are performed. The results show that when using the logistic regression method, the propensity to default indicator (TTD) reflected in the text content, it can indeed improve the out-of-sample prediction accuracy of the financial distress prediction model, and it is consistent with the in-sample test, this is mainly reflected in the reduction of the first type of error, that is, the probability of misjudging a financially distressed company as a normal company. Changes in the proportions have little effect on the relative importance of financial ratio variables when modeling with support vector machines, the propensity to default indicator (TTD) entered the top ten important variables in both ratios, and ranked fourth among all indicators when the ratio was 1:2, importance has increased significantly. From this it can be seen that, when using support vector machine to build a financial distress prediction model, the propensity to default indicator (TTD) has played an important role. In the case of using the support vector machine method, adding the default tendency indicator (TTD) reflected by the text information can also improve the accuracy of the financial distress prediction model.

Analysis of Non-standard Audit Opinions of Chinese Listed Companies’ Financial Statements: Based on the Data of Shanghai and Shenzhen A-share from 2015 to 2016

Based on the data of Chinese Shanghai and Shenzhen A-share listed companies from 2015 to 2016, this paper analyzes the general situation of audit opinion of listed companies’ annual financial statements and the distribution of non-standard audit opinions, and finds that ST companies were more likely to receive non-standard audit opinion than non-ST companies. This article further analyzes the specific matters that lead to the non-standard audit opinion, and summarizes the matters that made it easy for the CPA to issue the non-standard audit opinion. This paper has a certain reference to solve the problems existing in the process of audit, prevent the audit risk and improve the audit quality. Besides, it lays the foundation for the study of the non-standard audit opinion after the implementation of the new auditing standards.

基于Z-Score指标决策树的财务风险预警模型研究

财务风险预警模型是预测财务危机的主要手段之一。Z分数模型以其公式严谨、可操作性强等特点，被广泛应用。然而，Z分数模型是否适用于预警中国企业的财务危机仍存在争议。为此，归集了沪市上市公司2007~2013年的财务报表数据，实证发现ST公司与非ST公司的Z值线性不可分。揭示了：本质上为线性区分方法的Z分数模型存在局限性。因此，利用Z分数模型原有指标，提出了基于决策树的财务风险预警模型，解决Z分数模型失效问题。实证研究表明：所提出的预警模型提前3年预测ST公司财务危机的准确率达75.37%，提前1年预测的准确率达95.45%。 Financial risk early warning model is one of the primary means of forecasting financial crisis. Z-score model has been applied widely since it has precise formula and good practicability. However, as regard to the adequacy of using Z-score model to forecast financial crisis for domestic companies, there is a dispute. Therefore, we collect financial statements data from 2007-2013 of Shanghai listed companies, and prove that Z scores between ST companies and non-ST companies are linearly inseparable. It reveals that Z-score model with linear separable principle in nature, has its own limitation. Hence, this paper brings up a financial risk early warning model of decision tree based on Z-score indicators and it solves the problem of Z-score model. The empirical research shows that the decision model given by this paper can forecast financial crisis three years before it happens with 75.37% accuracy, and with 95.45% accuracy one year ahead of crisis occurance.