Financial Early-warning Model Research Articles

Construction and Application of the Financial Early-Warning Model Based on the BP Neural Network.

In order to further improve the early-warning effect of enterprise financial crisis management and reduce the occurrence of enterprise financial crisis, by taking listed companies as examples and combining the operating conditions of listed companies, a financial crisis early-warning indicator system was built from five aspects of profitability, debt-paying ability, development ability, operation ability, and cash flow ability. In addition, a financial management early-warning model based on the BP neural network algorithm was built. Through the experimental prediction, it is showed that the financial crisis early-warning model of listed companies based on the BP neural network algorithm for crisis prediction accuracy was more than 75%. The accuracy of the first three years of model prediction was 93.33% and 72.34%, respectively. The accuracy of model prediction in the first two years was 94.67% and 82.98%, respectively. In the first year, the accuracy rate increased to 100% and 89.36%. Compared with the prediction accuracy of the logistic model (50%), it is fully reflected that the financial early-warning model proposed in the research had a good crisis prediction ability.

Construction of Enterprise Financial Early Warning Model Based on Intelligent Mathematical Model

In order to improve the ability of enterprises to cope with financial risks, this paper analyzes the financial early warning of enterprises combined with intelligent mathematical models and builds an intelligent financial early warning model to assist in analyzing the financial status of enterprises. Moreover, this paper combines the existing information technology and expenditure business scenarios to construct an intelligent early warning frame for enterprise financial control based on an intelligent mathematical model. In addition, this paper combines the K-means clustering algorithm to design a financial approval process integrity control early warning method to warn the integrity of the financial approval process. Finally, this paper presents the early warning of financial standard compliance control based on the C4.5 decision tree algorithm. The experimental research shows that the enterprise financial early warning model based on the intelligent mathematical model proposed in this paper can play an important role in the enterprise financial management and effectively improve the ability of the enterprise to cope with financial risks.

Evaluate the sustainable reuse strategy of the corporate financial management based on the big data model

PurposeThe purposes are to explore corporate financial management optimization in the context of big data and provide a sustainable financial strategy for corporate development.Design/methodology/approachFirst, the shortcomings of the traditional financial management model are analyzed under the background of big data analysis. The big data analytic technology is employed to extract financial big data information and establish an efficient corporate financial management model. Second, the deep learning (DL) algorithm is applied to implement a corporate financial early-warning model to predict the potential risks in corporate finance, considering the predictability of corporate financial risks. Finally, a corporate value-centered development strategy based on sustainable growth is proposed for long-term development.FindingsThe experimental results demonstrate that the financial early-warning model based on DL has an accuracy of 90.7 and 88.9% for the two-year financial alert, which is far superior to the prediction effect of the traditional financial risk prediction models.Originality/valueThe obtained results can provide a reference for establishing a sustainable development pattern of corporate financial management under the background of big data.

Model Construction of Enterprise Financial Early Warning Based on Quantum FOA-SVR

The sudden outbreak of COVID-19 has a great impact on human life security and global economic development. To deal with the rampant pandemic, many countries have taken strict control measures, including restricting gathering in public places and stopping the production of enterprises; as a result, many enterprises suffered great challenges in survival and development during the pandemic. In order to help enterprises monitor their own financial situation and realize their healthy development under the pandemic, this paper constructs an Enterprise Financial Early Warning Model, in which Quantum Rotation Gate is used to optimize four algorithms, namely, Fruit Fly Optimization Algorithm (QFOA), Bee Colony Optimization Algorithm (QABC), Particle Swarm Optimization (QPSO), and Ant Colony Optimization (QACO). The results show that the ability of the prediction model can be greatly improved by using the Quantum Rotation Gate to optimize these four algorithms.

An evaluation of the effectiveness of three early-warning models on financial indexes

ABSTRACT In this paper, we construct three financial early warning models, and evaluate and compare their early warning effects. These models are based on factor analysis, logistic regression, and Verhulst-BP neural network, taking Chinese listed mineral resource companies as samples. The results show that the model based on Verhulst-BP neural network has the best early warning effect among the three models, and the early warning model based on logistic regression is more accurate than the one based on factor analysis.

基于Lasso方法与Logistic回归的上市公司财务预警分析

一般情况下，上市公司财务数据指标越多，预警效果越好，但由于多种因素影响，财务指标过多会导致变量间具有多重共线性。本文提出一种基于Lasso方法的Logistic回归上市公司财务预警模型。首先应用Lasso法对高维数据进行变量选择，达到降低数据维度和消除变量间共线性的目的，再用Logistic回归法实现对上市公司财务状况的预警。仿真实验结果表明，本文提出的方法能够有效地消除数据的冗余性，提高预警的精确性，为企业经营者提供有效的参考意见。 Generally, the more the financial data indicators of listed companies are, the better the early warning is; However, due to a variety of factors, excessive financial indicators lead to multiple collinearity among variables. This paper presents a financial early-warning model of Logistic regression listed companies based on Lasso method. Firstly, the Lasso method is used to select variables for high-dimensional data, which can reduce the data dimension and eliminate the collinearity between variables. Then, the Logistic regression method is used to predict the financial status of listed companies. Simulation experiment shows that the method proposed in this paper can effectively eliminate the redundancy of data, improve the accuracy of early warning, and provide effective reference for enterprise operators.

SOM-BP Neural Network-Based Financial Early-Warning for Listed Companies

To prevent and reduce corporate financial risks, this paper builds a financial early-warning model for listed companies based on a combination of SOM and BP neural networks focusing on short-term financial forecasting and monitoring. Firstly, SOM network is utilized to allow self-modification of unit connection weights according to the feature information of input data and enable the weight vector distribution to be similar to the distribution of sample data, thereby obtaining relatively optimal training samples among all training samples. Then, a short-term financial early-warning monitoring model is created through iterative BP training with the relatively optimal samples extracted as the input information of the BP neural network model. The results show that the proposed financial earlywarning system has higher recognition accuracy than the direct use of Logistic model, BP model or SVM model in term of short-term forecasting and monitoring. Furthermore, our model requires less amount of data while ensuring the validity. Therefore, it can monitor financial crises in real time for listed companies, so as to effectively prevent and resolve their financial risks and crises.

Research on Financial Early Warning of Listed Corporation Based on SOM Fusion BP Neural Network

Combining with the special environment of Chinese market, this paper defines the listed Corporation’s financial crisis, and analyzes the shortcomings of the existing financial early warning model. In order to further improve the accuracy of the financial early warning, and adaptively select optimal training samples, short-term forecasting model of listed corporations based on the SOM network fusion BP network is proposed. The model firstly extracts the initial training samples relying on the SOM network and obtains the optimum ST samples and non ST samples in all training samples. Furthermore, the extracted samples are utilized to construct the financial early warning system of five different levels based on SOM network. Finally, the model is compared with other model algorithms. The results show that the financial early-warning model proposed in this paper possesses higher recognition accuracy on short term forecasting and monitoring of enterprise finance compared with other recognition models. Moreover, smaller data size is needed in this model on the premise that the effectiveness is guaranteed. Therefore, the early warning model proposed in this paper can better realize enterprise financial monitoring, so as to effectively prevent and defuse financial risks and crises.

Research of Financial Early-warning Model for the Listed Electric Power Companies on Evolutionary Support Vector Machines Based on Genetic Algorithms

The development of the electric power enterprise concerns the national economic lifeline. In this paper, the Support Vector Machines (SVMs) early-warning model which is based on Genetic Algorithm (GA) optimization is established, with GA’s ameliorating SVMs. Using the penalty parameters and the kernel parameters of the process of GA’s optimizing SVMs, this paper gives full play to the global searching ability of GA and overcomes the problems generated from the selection of the SVMs model parameters. As a result, it is possible to initiate the financial risk analysis of the electric power enterprises and enable them to take timely measures to deal with issues that have emerged during the process of their development. It is displayed in the instance verification results of the listed companies in the electric power industry that SVMs which are based on GA optimization can predict the financial risks of the listed companies in the electric power industry accurately and effectively.

Management ownership and corporate performance

This paper primarily used statistical methods to establish financial early-warning models that made it possible to predict in advance the probability of a company experiencing financial distress, and raised corporate performance. In the empirical analysis, there may be the first study that attempted to use financial ratios and non-financial ratios such as ratio of director and supervisor ownership stakes after pledging of shares 25% as variables to analyze cross-holding groups. The study used the K-S, M-U tests and Logit regressions model. When the ratio was less than 5%, the main indicators showing the impact of dispersion of equity ownership upon corporate performance were the following factors, financial structure, solvency, and operating performance indicators. At 5 - 25%, the significant variables were ROS, ROE, and EPS influenced the corporate performance, while ownership stake of directors and supervisors was concentrating, ownership stake of executive officers would increase. To increase ownership stake of institutions, and avoid switch of CPAs and establishing independent directors and supervisors, may strengthen corporate governance. Beyond 25%, establishing independent directors and supervisors may strengthen corporate governance. At 5 - 25%, ownership stake of directors and supervisors, and ownership stake of executive officers were concentrating, establishing independent directors and supervisors may be lower to the likelihood of financial distress, and raised the corporate performance. Empirical test of a managerial implication on non-financial variable acted as an observation corporate performance. It can provide a reference for stockholders to observe corporate performance, and then to decide investment strategies. Corporate ownership and management changed, the mean contribution of this paper was that switch of CPAs and establishing independent directors and supervisors may be lower the likelihood of financial distress. This paper would be useful to researchers or practitioners who were focusing on management ownership and corporate governance implementation. Key words: Corporate ownership, corporate governance, corporate performance, financial risk management.

財務比率、投資決策與公司績效之實證研究－全球金融海嘯前後比較

本文研究係首次嘗試探討全球金融海嘯發生前後，台灣上市公司之存活分析，找出影響公司績效之顯著變數並比較其差異，建立財務預警棋型，期能在公司發生財務危機前，提前預測出可能發生之機率，降低公司發生財務危機之可能性，進而提高公司績效，達事前預警與風險管理之效。實證分析上，利用財務比率變數作為解釋變數，以統計科學方法羅吉斯迴歸模型，建構出企業財務預警模式。實證結果發現，預測金融海嘯前後企業之財務危機，其前一至前三年金融海嘯前的預測竿確率分別為91.62%，93.55%，90.38%；金融海嘯後的預測準確率分別為92.90%，91.67%，100.00%。除前二年外，其餘兩個年度金融海嘯後之預測準確率較金融海嘯前顯然有提高，財務比率變數可為投資人做投資決策分析之參考指標；就金融海嘯後之財務資訊觀察，企業適度增加負債比率、降低自有資金比率，可改善財務結構；增加總資產週轉率，可增強企業之經管能力；增加借款依存度，可提升現金流量；增加內部保留比率，可提高公司獲利能力，則發生財務危機的機率將下降，進而提高公司績效。準此，本文之實證價值與管理意涵為財務風險管理、投資決策與財務預測。

Research of Financial Early-Warning Model on Evolutionary Support Vector Machines Based on Genetic Algorithms

A support vector machine is a new learning machine; it is based on the statistics learning theory and attracts the attention of all researchers. Recently, the support vector machines (SVMs) have been applied to the problem of financial early-warning prediction (Rose, 1999). The SVMs-based method has been compared with other statistical methods and has shown good results. But the parameters of the kernel function which influence the result and performance of support vector machines have not been decided. Based on genetic algorithms, this paper proposes a new scientific method to automatically select the parameters of SVMs for financial early-warning model. The results demonstrate that the method is a powerful and flexible way to solve financial early-warning problem.