Financial Crisis Early Warning Research Articles

Improved SVM Algorithm Financial Management Model for Data Mining

Financial crisis early warning is an important link in financial management. With its relatively simple structure and excellent classification performance, support vector machine is often used in intelligent warning of financial crises. A financial warning model based on improved support vector machine is proposed to address the shortcomings of current financial warning methods that cannot handle large-scale data well, and the accuracy and efficiency are not ideal. In response to the excessive indicators in the financial early warning indicator system, the principal component analysis method is studied for dimensionality reduction, reducing the computational complexity of the model, and improving the training and operational efficiency of the model. In response to the limited practicality of support vector machine, which can only search for the optimal solution under constraint conditions, the use of smooth support vector machine to replace support vector machine for predictive classification is studied. Finally, a financial crisis early warning model is constructed based on smooth support vector machine using principal component analysis for dimensionality reduction of input data. The performance of the model is tested using an internet listed company as an example. The findings demonstrate that Model 1 outperforms the other three models in terms of accuracy rate (95%), MAE, MSE and [Formula: see text] values (0.162, 0.174 and 0.169, respectively). Therefore, the financial crisis early warning model constructed by the research can properly forecast the company’s financial situation, thereby helping the enterprise to develop better.

Financial crisis early warning of Chinese listed companies based on MD&A text-linguistic feature indicators.

Nowadays, the international situation is severe and complex, and the structural issues within the Chinese economy are prominent. Consequently, the financial risks faced by Chinese listed companies continue to escalate. Hence, it is of great practical significance to furnish effective early warnings for financial crises in listed companies. In this paper, we first employ web crawler technology and natural language processing technology to assess four text-linguistic features in the Management Discussion and Analysis (MD&A) section of the annual financial reports of listed companies in China from 2011 to 2020. These features are text tone, forward-looking, readability and similarity. Subsequently, we combine these features with traditional financial indicators and explore thirteen mainstream machine learning models to comparatively analyze their effectiveness in predicting financial crises in listed companies. The empirical findings of this research reveal that MD&A text readability and similarity indicators contribute valuable incremental information to prediction models, whereas text tone and forward-looking indicators exhibit the opposite effect. The latter two indicators can be manipulated more effortlessly by management, as this study's empirical findings indicate no evidence of their contributions in incremental informational value. In fact, the forward-looking indicator even introduces additional noise to the prediction. Finally, by comparing the early warning effects of thirteen machine learning models, it is found that RF, Bagging, CatBoost, GBDT, XGBoost and LightGBM models maintain stable and accurate sample recognition ability. In general, this paper constructs a more effective financial crisis early warning model by exploring the MD&A text-linguistic feature indicators, thereby offering a fresh research perspective for further investigations in this field.

The design of early warning software systems for financial crises in high-tech businesses using fusion models in the context of sustainable economic growth.

Enterprises are urged to continue implementing the sustainable development strategy in their business operations as "carbon neutrality" and "carbon peak" gradually become the current stage's worldwide targets. High-tech businesses (HTE) need to be better equipped to manage financial risks and avoid financial crises in the face of severe market competition. The most popular machine learning models-logistic regression, XGBoost, and BP neural networks-are chosen as the base models in this study. The three models are combined using the stacking method to train and forecast the fusion models while offering other researchers some basic model research ideas. The financial crisis early warning (FCEW) of HTE is built concurrently by contrasting the fusion of various quantitative basis models and the fusion procedures of voting and averaging. The outcomes demonstrate that the fusion model outperforms the single model in terms of performance, and the stacked fusion model has the best early warning impact. By comparing and comparing the effect of three fusion models on financial crisis warnings of high-tech enterprises, it makes up for the defect of low accuracy of traditional forecasting methods. It improves the sustainable development path of enterprises.

Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs)

This work aims to improve the accuracy of financial crisis early warnings for small and medium-sized enterprises (SMEs) to help them uncover hidden dangers during the latent period of a crisis and to respond effectively. First, this work analyses the development characteristics of SMEs, the crises they face, and a financial crisis early warning system. Then, a statistical method is used to determine which indicators are significant to implement the model for an early warning financial crisis to determine the index system. To do this, relevant machine learning (ML) algorithms are introduced to realize a corporate financial crisis management and early warning system (CFCM-EWS) using the stacking fusion method. The CFCM-EWS can mine relationships among the data and analyze nonlinear and difficult-to-explain problems. A comparison of classical financial early warning models (backpropagation neural network (BPNN) and logistic regression models) with the XGBoost model highlights the advantages of the XGBoost model. The model's discrimination results show that its prediction accuracy for special treatment and nonspecial treatment corporates is 85.8 % and 81.9 %, respectively. The logistic regression, XGBoost, and BPNN models are fused using the stacking method. ML provides a more practical prediction method with higher efficiency and accuracy than does traditional econometric models. The final fusion model outperforms the voting and averaging methods in prediction performance. The CFCM-EWS based on intelligent computing discussed here is of great value for SMEs to accurately predict financial crises and adopt timely countermeasures.

Application of PCA-SVM Model in Financial Crisis Early Warning System of Listed Manufacturing Companies

Manufacturing industry has always occupied this very important proportion in the national economy. However, with the economic problems in the past two years, some enterprises began to have a deficit crisis. In order to fundamentally alleviate and solve the financial problems of enterprises, this paper studies the financial model prediction of enterprises based on principal component analysis and support vector machine. The multiple financial analysis trajectories are constructed by using PCA-SVM model, and the results are compared with those of logistic model, BP neural network model, and single support vector machine. Experiments show that the prediction level of PCA-SVM model is the best, and the accuracy of the second model is not as good as this model. The error rates of the second type are 14.81%, 14.54%, and 7.67%, respectively, which are higher than those of PCA-SVM model. After comparing the models in this paper, it is found that the components of the model should be extracted first, and then the data operation should be carried out. Such calculation trajectory has a high level of accuracy. The research of this paper provides reference value for solving the financial problems of manufacturing enterprises.

The Application of the Data Mining Method Based on Ensemble Learning to the Research on Early Warning of Financial Crisis in Small- and Medium-Sized Enterprises

In the big data environment, the factors affecting the operation of small- and medium-sized enterprises are becoming more and more complex. In order to more accurately measure the financial crisis of small- and medium-sized enterprises, from the perspective of multisource information fusion, based on the traditional financial data reflecting the private information of enterprises, the public information data for measuring the macroeconomic and market environment are integrated and analyzed. Considering the multidimensional heterogeneity of multisource information data, BPNN, SVM, KNN, LOG, and MDA are introduced to build models, and ensemble learning is used to integrate the results of different early warning models to reduce the risk of inconsistent results from different models. The empirical results show that the early warning model integrating multisource information can improve the early warning accuracy. The research results show that the financial crisis early warning of small- and medium-sized enterprises should not only pay attention to financial indicators but also pay attention to the impact of corporate governance, industry, and the overall economic environment of the country; the accuracy of the model in this paper is significantly higher than that of the other models in terms of the accuracy of financial crisis early warning. It shows that the model in this paper can better predict the real financial status of small- and medium-sized enterprises.

Research on Early Warning of Financial Crisis of Listed Companies Based on Random Forest and Time Series

The global economy has entered a new normal, and the economic environment is evolving at a rapid pace. This requires the establishment of a financial crisis early warning system that can be dynamically analyzed based on historical data information. To address this research objective, this study proposes a k-fold random forest algorithm combined with a time series analysis model as an early warning algorithm for corporate financial crises. The algorithm takes advantage of the ability of the time series analysis model to make short-term forecasts of historical data and uses the time series analysis model to make forecasts of newly constructed financial index data. The k-fold random forest is used to analyze the financial situation of the predicted financial data and achieve the purpose of dynamic financial crisis early warning. The experimental results show that the prediction accuracy of the financial crisis early warning model based on the random forest algorithm and time series is 89%, which indicates that the model is effective and feasible.

Analysis of Financial Risk Early Warning Systems of High-Tech Enterprises under Big Data Framework

With the further development of China's market economy, the competition faced by companies in the market has become more intense, and many companies have difficulty facing pressure and risks. Among the many types of enterprises, high-tech enterprises are the riskiest. The emergence of big data technologies and concepts in recent years has provided new opportunities for financial crisis early warning. Through in-depth study of the theoretical feasibility and practical value of big data indicators, the use of big data indicators to develop an early warning system for financial crises has important theoretical value for breaking through the stagnant predicament of financial crisis early warning. As a result of the preceding context, this research focuses on the influence of big data on the financial crisis early warning model, selects and quantifies the big data indicators and financial indicators, designs the financial crisis early warning model, and verifies its accuracy. The specific research design ideas include the following: (1) We make preliminary preparations for model construction. Preliminary determination and screening of training samples and early warning indicators are carried out, the samples needed to build the model and the early warning indicator system are determined, and the principles of the model methods used are briefly described. First, we perform a significant analysis of financial indicators and screen out early warning indicators that can clearly distinguish between financial crisis companies and nonfinancial crisis companies. (2) We analyze the sentiment tendency of the stock bar comment data to obtain big data indicators. Then, we establish a logistic model based on pure financial indicators and a logistic model that introduces big data indicators. Finally, the two models are tested and compared, the changes in the model's early warning effect before and after the introduction of big data indicators are analyzed, and the optimization effect of big data indicators on financial crisis early warning is tested.

Research on Early Warning Model of Financial Crisis Based on SCAD-SVM

After more than half a century of development, there are numerous empirical analyses on financial early warning models, most of which take listed companies as the research object. The financial crisis early warning of listed companies is a small sample, and the application of neural network and logistic regression model has the problem of over-fitting, which leads to the effect of the model is not obvious. In recent years, the support vector machine method given risk minimization criteria has been widely used in financial crisis early warning, but few scholars combine SVM and penalty letter SCAD methods for financial early warning analysis. Therefore, this paper proposes an improved variable selection method Scad and support vector machine (SVM) combined model algorithm to select the best indicators and used for financial crisis early warning, and then based on the Shanghai and Shenzhen A-share listed companies as the research sample comparison Empirical effects of SVM, Lasso-SVM and Dantzig-SVM, the research results show that the selection of variables can greatly improve the accuracy of the financial crisis early warning model, and the SCAD method in the variable selection method has more advantages than Lasso and Dantzig. The SCAD-SVM model proposed has an accuracy rate of over 96% on both the training set and the test set in this paper. The model has a good classification effect and a strong economic interpretation ability. The research results of this paper not only enrich the financial early warning model, but also greatly improve the accuracy of financial crisis early warning, and provide a theoretical basis for the scientific decision-making of enterprises and shareholders.

Establishment of a Financial Crisis Early Warning System for Domestic Listed Companies Based on Three Decision Tree Models

The financial crisis is a realistic problem that the general enterprise must encounter in the process of financial management. Due to the impact of the COVID-19 and the Sino-US trade war, domestic companies with unsound financial conditions are at risk of shutdowns and bankruptcies. Therefore, it is urgently needed to study the financial warning of enterprises. In this study, three decision tree models are used to establish the financial crisis early warning system. These three decision tree models include C50, CART, and random forest decision trees. In addition, the ROC curve was used for comprehensive evaluation of the accuracy analysis of the model to confirm the predictive ability of each model. This result can provide reference for domestic financial departments and provide financial management basis for the investing public.

A k-means++-improved radial basis function neural network model for corporate financial crisis early warning: an empirical model validation for Chinese listed companies

An early warning of corporate financial crises has long been the focus of investors and enterprises. Integrated early warning models for financial crises perform better than normal models, but most integrated models are very complex, elusive and computationally time-consuming. This paper aims to simplify the early warning model for financial crises by collecting and analyzing the financial data of Chinese special treatment (ST) companies, normally listed companies and cancel special treatment (CST) companies. To further predict the financial risks of companies, we put forward a finance-predicting model based on the k-means++ algorithm and an improved radial basis function neural network (RBF NN), and we compare their respective statistics. We indicate by experiment that combining k-means++ with the improved RBF NN helps to better predict financial risks for companies, which is effective in the risk control of financial management.

The Establishment of a Financial Crisis Early Warning System for Domestic Listed Companies Based on Two Neural Network Models in the Context of COVID-19

In the context of COVID-19, many companies have been affected by the financial crisis. In order to carry out a comparative study on the accuracy of the company’s financial crisis early warning method, this study used RPROP artificial neural network and support vector machine, with 162 listed companies’ two-year panel financial indicator data as a model sample, and the test sample established a financial crisis early warning model. The theory of comprehensive evaluation combining two kinds of neural network methods is put forward innovatively. The predicted results can strengthen the supervision of the listed companies with risks by themselves and others and have important economic and social significance to ensure the stable operation of the listed companies, the securities market, and the national economy.

RETRACTED ARTICLE: Research on early warning and monitoring algorithm of financial crisis based on fuzzy cognitive map

Because the financial crisis early warning and monitoring is too complex, most of the current implementation can only alternately choose early warning in accordance with the criteria of monitoring the financial crisis. As a result, the security measures are not in accordance with the actual situation of the system, and cannot be adjusted quickly according to the changes of the system. This paper presents a financial crisis early warning detection algorithm based on fuzzy cognitive map. The relationship between financial data is obtained by fuzzy cognitive map, and the system crisis value is calculated by fuzzy cognitive map reasoning process. Finally, through the financial data of listed companies, the early warning and monitoring effect of the algorithm is verified. The experimental results show that the method is efficient, cost-effective, and can timely and reasonably reflect the crisis state of financial data.

一种粒子群优化贝叶斯网络的财务预警方法

群智能预测及其在企业财务危机预警中的应用研究具有重要的理论意义和实用价值。文中在在构建上市公司财务能力评价指标体系的基础上，提出一种粒子群优化贝叶斯网络参数学习的财务预警方法，经选取一组上市公司某三年数据分析，实验表明提出的算法在公司财务危机预警的平均正确率可获得较好的预测效果。 It has important theoretical significance and practical value to swarm intelligent forecasting and its applications in financial early warning of enterprises. On basis of the construction of the evaluation index system of financial capability of corporation, this paper proposes a new method of financial early warning by cooperating particle warm optimization into the parameter learning of Bayesian network. The experimental results on the data of a group of listing companies and comparisons have shown that the proposed algorithm has better effectiveness and the average correct rate in the financial crisis early warning.

Currency crisis early warning systems: Why they should be dynamic

Traditionally, financial crisis Early Warning Systems (EWSs) have relied on macroeconomic leading indicators when forecasting the occurrence of such events. This paper extends such discrete-choice EWSs by taking the persistence of the crisis phenomenon into account. The dynamic logit EWS is estimated using an exact maximum likelihood estimation method in both a country-by-country and a panel framework. The forecasting abilities of this model are then scrutinized using an evaluation methodology which was designed recently, specifically for EWSs. When used for predicting currency crises for 16 countries, this new EWS turns out to exhibit significantly better predictive abilities than the existing static one, both in- and out-of-sample, thus supporting the use of dynamic specifications for EWSs for financial crises.

Groups Wisdom - 3D Fruit Fly Optimization Algorithm for Financial Distress Forecasting

In recent years, the global economic recession, followed by failure of business due to poor management, has resulted in a domino effect that occurred throughout the financial system. To avoid the expansion of loss, the issue of business failures should be seriously considered. In this paper, firstly, to reduce the time and space spent in our models learning and prediction, we use the data mining methods -stepwise regression, genetic algorithms and self-organizing map network for pre-processing data. Secondly, to match with the food searching behavior of the fruit fly, we modify Pan’s optimization algorithm to a three-dimension space for the General Regression Neural Network (FOAGRNN), then, compared it with the Backpropagation Neural Network, Genetic Programming, General Regression Neural Network (GRNN),and the traditional Least Square method for financial distress forecasting models. Finally, through a substantial number of experiments, we realized that if we wanted to study the company's financial crisis early warning, in addition to considering the company's financial variables, corporate governance variables should not be neglected. Besides, we also found that our modified 3D-FOAGRNN outperformed the General Regression Neural Network, Genetic Programming, Backpropagation Neural Network and the Least Square method in terms of forecasting accuracy.

An Empirical Study on Predicting Financial Risk of Listed Companies

Financial crisis early warning analysis is a matter of grave social and economic concern. It is important for enterprises, commercial banks and various investors. This is an exploratory study to determine if financial ratios of crisis companies differ from those of no crisis companies. The crisis firms (n=63) were then matched with no crisis firms on the basis of firm size, time period, and industry. Using this matched-pairs design, choose 63 listed companies, which are marked ST companies because of abnormal financial standing in Shanghai and Shenzhen in 2006, form the financial crisis samples, and choose some similar sized listed companies in same industry as matching samples, Taking the index of property liabilities ratio, audit opinion, finance lever ratio, gross property net profit ratio, sales revenue growth ratio and cash flux to current liability ratio as the final variants, set up the discriminant model by Fisher’ coefficient, conduct the case analysis of financial crisis early warning. These results provide empirical evidence of the limited ability of financial ratios to detect and predict crisis financial reporting.