# Empirical Analysis of Enterprise Financial Management Risk Prediction in View of Associative Memory Neural Network

Authors: Huifang Cheng; Xishuan Zhang
Journal: Security and Communication Networks
 (2022)

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## Abstract

Enterprise financial analysis has a far-reaching impact on modern enterprise management decision-making and plays a role that cannot be ignored. Financial status is related to the life and death of an enterprise and is the lifeline of an enterprise. Fast and efficient financial analysis can provide reliable and accurate decision-making information support for enterprise investors, operators, creditors, and other organizations and individuals to understand and evaluate the enterprise status and future development potential. With the development of intelligent methods such as associative memory neural network, the research of financial analysis decision support based on artificial intelligence has been paid more and more attention by academia and management, and has made new progress. Efficient and accurate financial risk prediction can help enterprises predict the possible financial risks in the future earlier, facilitate the early detection of problems, and take effective measures to avoid risks or minimize losses. However, most of the existing mature financial risk prediction studies are based on balanced data sets. The research on the classification of unbalanced data sets is not mature and perfect and needs to be further studied. Generally speaking, this paper mainly adopts the research method of cross integration of various disciplines and organically integrates the key theories, methods, and technologies such as default risk management theory, financial index analysis theory, data mining principle, prediction and decision theory, computer technology, multiclassifier integration technology, a variety of enterprise financial risk early warning technology and statistical sampling, carrying out systematic research on enterprise financial risk prediction. This review constructs the enterprise financial risk prediction method system based on heterogeneous data mining technology, mainly including data preprocessing layer, improved nearest neighbor delta increment layer, heterogeneous nearest neighbor extraction layer, and case-based reasoning prediction layer, so as to improve the traditional financial risk prediction method and obtain a new risk classification prediction model. The case-based method has some significant advantages in risk prediction performance, and it also helps to reduce the probability of financial risk.


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## Body

## 1. Introduction

With the rapid development of China’s economy, the financial capital market has become more and more competitive, and the demand for enterprise financial risk analysis and prediction has become more and more urgent. The majority of managers and investors urgently hope to establish an effective financial risk intelligent analysis and prediction system through continuously improving the prediction mechanism. Analyze and predict the overall financial risk of the enterprise, intelligently. Many companies have been implicated in the world economic crisis, their operations have been frustrated, consumer demand and investor confidence have declined, and they are facing the business difficulties of capital chain rupture, financial deterioration, difficult operation, and even helpless bankruptcy, which affect the stability and development of the global economy. How to predict the financial distress, prevent the further deterioration of the enterprise’s financial situation, restructure the company’s resources and structure, improve the comprehensive operation level, enhance the confidence of consumers and investors, and help the enterprise avoid or get rid of the known or unknown risks [1–10].After years of practice, early excellent enterprises usually increase their profits by reducing costs and improving the durability of goods. However, this change has been unable to meet the competition mode under the new economic normal. Innovation has long been the secret of development and prosperity. It is undoubtedly the symbol of a new era. Therefore, in recent years, the research on quality and cost has been insufficient to meet the needs of some advanced enterprises. In order to surpass and distance themselves from other enterprises and compete for business opportunities, these enterprises focus on innovation and reducing their financial risks. Therefore, innovative enterprises were born. If innovation is separated from the enterprise, all the development strategies and projects about innovation will exist inefficiently. Shortening the innovation cycle and enhancing risk awareness are the key factors to enhance the competitiveness of enterprises. Compared with other types of enterprises, innovative enterprises have the characteristics of high risk, uncertainty, and high return. Therefore, compared with the financial risk management of ordinary enterprises, the financial risk management of innovative enterprises has both similarities and differences [11–15].Financial risk classification and prediction are an important part of risk management and also an effective means to avoid or reduce risks. Financial risk classification and prediction refers to the establishment of financial risk classification and identification system according to the risk factors of historical financial data, which provide some valuable reference opinions and measures to support the decision-making of senior management of enterprises. At present, the research on enterprise financial risk classification and prediction can be roughly divided into two categories. One is the research on qualitative financial risk classification and prediction, which mainly uses subjective and artificial evaluation systems such as decision-making evaluation, and brainstorming to judge and analyze new things and problems through the summary of past individual or group experience, which often has certain limitations. One is quantitative financial risk classification and prediction research, which mainly adopts quantitative evaluation systems such as mathematics and computer data mining. Based on the analysis and demonstration of a large number of data, it often lacks certain flexibility and needs to be further improved or improved by constructing corresponding mathematical analysis models to make decision analysis on new things. Based on various related theories and technologies such as financial risk management theory, financial index analysis theory, data mining principle, prediction and decision theory, computer technology, multimodel fusion technology, multiclassifier recognition and analysis integration technology, data statistical analysis technology, case-based reasoning, and its reuse technology, this paper will study the difficult problems of enterprise financial risk prediction and evaluation; so as to overcome the current research deficiencies in this field and help to further expand the enterprise financial risk assessment modeling theory and method system, which has important theoretical research value [16–22].Risk refers to the combination of the possibility and consequences of a specific adverse situation. In enterprise management, risk is divided into operational risk and financial risk. The financial risk refers to the possibility that the actual financial results of the enterprise deviate from the expected financial results and suffer losses in a certain period of time due to the internal and external environment and various unpredictable or uncontrollable factors in the process of various financial activities in the production and operation of the enterprise. At present, there are two main understandings of financial risk in academic circles: the first way of understanding holds that financial risk only exists in the process of enterprise financing, which is a narrow way of understanding. Under this way of understanding, the definition of financial risk refers to the risk caused by the failure of enterprises to repay their debts due to maturity. The other is a broad understanding, which holds that the financial risk of enterprises does not only exist in the financing process, but will exist as long as there is the flow of funds. Therefore, the financial risk in a broad sense is caused by the inconsistency between the actual income and the expected income in the whole process of capital operation due to a variety of unpredictable reasons.The operation process of any enterprise’s capital should include financing, investment, capital recovery, and capital distribution. There will be financial risks in each link of the capital operation process. The first is the financing link. When the enterprise raises funds in the early stage, due to the wrong selection of financing quantity or financing method, it has no way and ability to repay the loan after maturity, and the risk will exist in the financing link. The second is the investment link. When an enterprise invests, if it fails to make a feasibility analysis or makes mistakes in investment decision before investment, its actual rate of return will easily deviate from the expected return after maturity. This uncertainty of investment return is investment risk. The third is the recovery of funds. When the cash outflow generated by the enterprise in the process of operation cannot be recovered on time or directly, there will be a risk of fund recovery. Finally, the capital distribution link. When the enterprise makes profits after paying taxes at the end of the year, the capital distribution is unreasonable, which cannot meet the expected requirements of shareholders or ensure the development needs of the enterprise. At this time, the risk will inevitably exist in the capital distribution link. Therefore, financial risk exists in every link in the process of enterprise capital flow. As long as there is a risk in one link, it is likely to cause financial crisis [23–34].As a branch of risk management, financial risk management, as an exante management function, is a management science developed on the basis of predecessors’ rich risk management experience and modern scientific and technological achievements. Financial risk management means that enterprises or other business entities analyze and study various possible risks in advance in their respective business processes, preset corresponding countermeasures according to various risks, prevent and control them, solve them with economic and reasonable methods, and ensure the normal development of business activities, management process to ensure that their own economic interests are protected from loss. Financial risk management is mainly composed of risk identification, measurement, prevention, and control, the core of which is to effectively identify and measure all kinds of risks encountered. All these works are done to reduce financial risks and reduce enterprise losses as much as possible. Therefore, when making decisions on financial risk management, deal with the relationship between cost and efficiency, first consider solving the risk from the most economic and reasonable point of view, and formulate financial risk response strategies. The dynamic characteristics of risk determine that the management of financial risk is always a dynamic process. Due to the changing internal and external environment faced by the enterprise, in the implementation of financial risk management, the management plan to deal with financial risk should be adjusted in time in combination with the real-time change of financial risk status, and the behavior deviating from financial risk management should be continuously corrected. The following is a brief description of each link of financial risk management.The rise of innovative enterprises is not accidental, but thanks to its strong technical strength. The rapid development of innovative enterprises has promoted the economic development of enterprises, China, and even the world. Its advantages and disadvantages coexist. Compared with traditional enterprises, innovative enterprises will encounter more risks. Because its main work is innovation, it will encounter many risks in the process of innovation. In order to eliminate the factors affecting the development of enterprises, to understand the risks from the root, and prevent and deal with various risks in time to prevent the occurrence of crises.Enterprise financial risk management mainly relies on cutting-edge technologies such as computer and data mining to carry out real-time and systematic tracking and feedback on the internal financial operation status of the enterprise, monitor the possible financial risks, so that the enterprise can quickly and accurately respond to various financial risks, so as to avoid unnecessary or irreparable losses, and promote the enterprise to change the organizational structure, further promote organizational innovation and development. For any enterprise, financial problems are related to the life and death of the enterprise and are of great concern to the enterprise. Effectively predicting and avoiding enterprise financial risks is one of the important indicators to evaluate the operation and management level of an enterprise. The survival of enterprises, especially listed companies, directly or indirectly affects the interests of related parties. Strengthening the management of enterprise financial risk has become a common concern of contemporary business and academic circles. According to the theory of problem case similarity knowledge, this paper attempts to effectively predict enterprise financial risk through multimodel case-based reasoning reuse technology and its new fusion method.By studying various factors that may make innovative enterprises fall into risk, hope to give enterprise managers some reference basis and help them formulate convenient, practical, and correct financial risk management methods. This paper uses specific innovative enterprises to carry out research. From the perspective of enterprise finance, this paper makes an in-depth exploration on the financial risks of innovative enterprises, studies and analyzes the occurrence mechanism, occurrence and future prevention measures of financial risks in innovative enterprises from the perspectives of the causes, conditions, and future management methods of financial risks. The global exponential stability of neural networks is analyzed and studied, and the effects of time delay and random disturbance on the stability of neural networks are fully considered. The global exponential stability of continuous time neural networks with constant time delays and their corresponding discrete models is studied. The conditions for the global exponential stability of the studied system and the necessary assumptions are given, and the vertex Lyapunov functional for each node is constructed. Based on the properties of equilibrium graph and topological property of network, the system is proved to be globally exponentially stable.


## 2. Associative Memory Neural Network

A bidirectional associative memory neural network model is established, and the nonlinear neuron excitation function in the model is neither bounded nor smooth. A sufficient condition for the exponential input-state stability of the bidirectional associative memory neural network model is obtained by using Lyapunov functional and linear matrix inequality [35–38]. The stability of associative memory neural network is obtained by Lyapunov functional and linear matrix inequality. In the later stage, the state stability of neural network with pulse and random will be studied by constructing new differential inequality methods.The following bidirectional associative memory neural networks were studied:(1)dxitdt=−aixit+∑j=1nuijfjyjt−τj+sit,i=1,2,⋯,n,dyjtdt=−bjyjt+∑j=1nuiigjxit−σi+hjt,j=1,2,⋯,n,where xit, yjt are the form states of the divine element, fj, gj are continuous, τj, σi are positive constants, ai, bj are nonnegative constants, and uij, si, uii, hj are constants.Each layer of the neural network model containsL neurons. Each neuron in the first layer is connected to the neuron in the second layer, and signals are transmitted bidirectional between the two layers. It is derived from a simple neural network in which neurons do not signal each other. Discrete time neural networks with constant time delays have a wide application background. Up to now, there are few research achievements. In recent years, the research in this field has been paid more and more attention. In this part, the discrete model corresponding to the continuous time neural network model with constant delay will be established. There are many discretization methods for continuous time model. In this paper, a semidiscretization technique is used to obtain the corresponding discrete model of the system through two approximations, and it is proved that this discretization method can keep the stability of the original equilibrium point, which has strong practical significance. The sufficient criterion to ensure the global exponential stability of continuous time neural networks with constant time delays is established based on Lyapunov method and graph theory knowledge. Through practical application in real life, its stability has been effectively tested.The output produced by neurons in one domain is immediately transmitted to neurons in the other domain as its input signal. This signal processing method may be consistent with the actual situation when the number of neurons is small, but if the number of neurons in the network is very large and the axon length of neurons is long, the signal will be delayed when it is transmitted from one neuron to other neurons.(2)dxtdt=−Axt+Wfyt−τ+st,dytdt=−byt+Vgxt−σ+ht,where xt and yt are the form states of the divine element. A, W, b, and V are the Weight matrices.The time delay represented by the neural network model is a constant time delay, that is to say, the state of the neuron at any timef is only related to the state before a fixed time length, which is obviously inconsistent with the actual situation.The conventional methods to discretize a continuous system include Euler and Runge. Runge–Kutta method, etc. However, the dynamic properties of the discretized system may be quite different from those of its corresponding continuous system, which may lead to some conclusions obtained in the study of continuous dynamic system, such as the existence of equilibrium point and the criterion of stability cannot be used. The phenomenon that an exponentially stable equilibrium point of continuous neural network is no longer exponentially stable after being discretized by standard Euler method. If this happens, it becomes meaningless to use discrete systems to simulate, experiment, or calculate continuous systems. Therefore, to discretize a continuous system, it must be required that the discrete system inherit the dynamic properties of its corresponding continuous system in Figure1. From the figure, we know that the dynamic characteristic refers to the relationship between the output and input of the system when the input of the detection system is a signal that changes with time. The main performance indexes of dynamic characteristic include unit step response performance indexes in time domain and frequency characteristic performance indexes in frequency domain.Figure 1
Equilibrium index of neural network.
(a)(b)(c)The output information of the neural network is in the form of grasping, that is to say, these periodic solutions of the joint complaint memory are required to be in stable state. Associative memory can be realized only when the neural network can stabilize on the periodic solution of associative memory. If the periodic solution of the neural network associated with memory is stable, then the neural network can eliminate these noises or make the fuzzy information clear, and finally associate with the stable periodic solution. Otherwise, the network output information may not be the information we need to associate. Stability is one of the core problems of neural network theory and application. It is the theoretical basis of associative memory and also provides a reliable guarantee for practical application. Therefore, the study on the existence and stability of periodic solutions of neural networks is of great guiding significance to researchers applying MAM neural networks in designing neural network systems:(3)Lit=Uit+∑j=1lbijLjeaσij∫t−σijtVjsds+Vit+∑j=1ldijMjeaσij∫t−τijtUjsds.The above neural network is approximated twice, and the following discrete neural network model is obtained:(4)Uin+1=Uine−aih+θih∑j=1lbijfjVjn−wij+θihIi,Vin+1=Vine−cih+φih∑j=1ldijgjujn−kij+φihJi,θih=1−e−aihai,φih=1−e−cihci.The vertex Lyapunov functional of the i-th node of the discrete-time neural network is constructed as follows:(5)Hin=Uin+∑j=1lbijLjφjhλwij+1∑m=n−wijn−1Vjm+Vin+∑j=1ldijMjθjhλkij+1∑m=n−kijn−1Ujm.Under the same constraints, it is proved that the discrete-time neural network is globally exponentially stable and has the same equilibrium point as the continuous time neural network, that is, the discrete-time neural network obtained by this discrete-time method inherits the stability state of the continuous time neural network. The global moment exponential stability of stochastic neural networks with time-varying delays is studied. The conditions for the exponential stability of the global moment of the studied system are given. Combining various inequalities, the neural network is established on the strongly connected graph, and the appropriate vertex Lyapunov functional is constructed. It is proved that the system is exponentially stable:(6)dxkt=−akxkt+∑h=1lbkhfhyht+∑h=1lekhfhyht−τkhtdt+σ1xkt,yktdw1t,dykt=−ckykt+∑h=1ldkhghxht+∑h=1lpkhghxht−σkhtdt+σ2xkt,yktdw2t,duitdt=−aiuit+∑j=13bijarctanvjt−σij+Ii,dvitdt=−civit+∑j=13dijsinujt−τij+Ji,ai, ci, bij, dij are nerve parameters, Ii, Ji are external input values.The general conditions for the p-order exponential stability of the neural network are given, and the applicability of the conditions is proved by combining the inequality.


## 3. Empirical Analysis of Enterprise Financial Management Risk Prediction

By integrating the basic category prediction results in a weighted way, the prediction results of the financial management system on this test sample can be obtained, and then the test accuracy of the group on this test sample can be obtained. This process is repeated until each sample has performed a training and testing process. The accuracy of prediction is verified by averaging the above accuracy.As shown in Figures2 and 3, for both N2 and N3, the left side of Figure 2 is the relationship between sample points and prediction accuracy, and the right side of Figure 2 is the relationship between sample points and personal optimal values. The prediction accuracy generally increases first and then decreases with the increase of the basic number, but it is not stable and monotonous, which proves the correctness of the forward search and backward pruning strategies. The prediction accuracy of the system is higher than or equal to the optimal single classifier, which proves the effectiveness of the proposed method. In practice we can according to the need, if you need the most concise combination system, all can choose a combination of 3, but if you want to get the highest prediction accuracy, can choose 7, which is composed of seven basic classifier combination system, so the method of this section makes it practical in the process of financial distress prediction obtained greater flexibility. To some extent, it proves the importance and effectiveness of feature selection in classifier construction. In many cases, it indicates how well a model is performing, but in some cases, the number of subclassifiers cannot be too high or too small. In particular, the number of subclassifiers should be appropriately selected in the integration algorithm to achieve the optimal classification accuracy.Figure 2
System prediction accuracy of N2.Figure 3
System prediction accuracy of N3.In order to further analyze the advantages and disadvantages of the forward greedy search strategy proposed in this paper, it is compared with several other commonly used selective integration strategies. The experimental results are shown in Figures4 and 5. Note that the left side of Figure 5 is the relationship between sample points and prediction accuracy, the right side of Figure 5 is the relationship between sample points and personal optimal values. Among them, ascending integration means that basic classifiers are sorted from low to high according to individual performance. Starting from the worst single classifier, each basic classifier is added successively until all of them are added. Meanwhile, the combined performance of the new system after each addition is recorded. On the contrary, the descending ensemble sorts basic classifiers according to individual performance from high to low, and adds each suboptimal single classifier in turn starting from the optimal single classifier. Random search is a process in which each single classifier in the basic classifier library is added into the system one by one by selecting one from the basic classifier library as the starting point and adopting the sampling method without putting back. The system consisting of forward greedy search occupies the highest position, followed by descending integration and random search, and ascending integration occupies the lowest position. Although the trend of ascending integration shows a trend of rapid rise, due to its low starting point, although its performance is greatly improved compared with the starting point, it can only reach or approximate the level of individual optimal single classifier, and even worse than the prediction performance of random combination. Therefore, if the prediction ability of a single classifier outside the system can be achieved, even if the prediction ability of the combined system has been improved to some extent compared with that inside the system, such improvement is meaningless, because it does not conform to the principle of cost-effectiveness. Therefore, it is meaningful to promote on the basis of the optimal individual, which proves the correctness of selecting the individual optimal basic classifier in this paper and constructing the combined system from it. For forward greedy search and descending integration, although both of them start from the individual optimal basic classifier, it can be seen that the performance of descending integration is still very unstable. The performance of the combined system vibrates up and down the individual optimal line, while the performance of forward search is significantly higher than the individual optimal line. The combined performance of the first few basic classifiers with better performance may not be better, or even worse, indicating the importance of differences between basic classifiers for system construction. In each step, the forward greedy search retains the best combination prediction performance in the current situation (given number of basic classifiers), and its work starts from the difference. Although it has not gone through large-scale system combination trial calculation, its simple optimization of the current “greedy” thought, or provides a large number of satisfactory solutions for the postpruning work.Figure 4
Performance comparison of combinations of different fusion sequences.Figure 5
Different fusion sequences.For model in Figure6, static model in the first three year of testing accuracy is higher than the fixed window model, may be due to a few year of annual training samples and testing samples are relatively close, concept drift phenomenon is not obvious, but with the passage of time, the sample of old phenomenon is more serious, and its testing accuracy drop is more obvious. However, the test accuracy of the fixed time window method is relatively stable over time, which further illustrates the importance of updating the model. The dynamic model with fixed time window is superior to the static model. Built in a certain period of time and, therefore, is not too up to date, and the static model often cannot meet the needs of financial distress prediction, even if the enterprise financial predicament definition is unchanged under the premise of characteristics of the concept of financial distress, and the changes are with the change of economic environment, and the financial difficulties of the virtual concept drift, but the enterprise is necessarily based on the current available economic information. Dynamically update the financial distress prediction model in time to continuously meet the needs of future financial distress prediction.Figure 6
Line chart of under different characteristics.Dynamic selection and dynamic integration model accuracy is mostly higher than the window size adaptive method in Figure7, although this is because the window size adaptive method can be found out with the current financial difficulties concept features and the most relevant data sample subset, but in financial distress prediction for future samples, build a single financial distress prediction model, The dynamics is reflected in the selective adjustment of modeling training samples. And dynamic selection and dynamic integration model is first of all, using the method of adaptive window size selection of the training sample library building basic classifier, completion of the current financial difficulties concept features of different information levels of digging, and according to the specific characteristics of the future to predict a single sample, dynamic selecting one or a few of the most relevant for decision-making. This dynamic is described for a single sample level, and the study is more detailed and a more scientific theory. Of the dynamic selection and dynamic integration methods, no one method is always optimal over the years advancing over time, and all methods have their advantages and relative limitations. It can be seen that simple combination integration prediction cannot effectively improve the accuracy of financial distress prediction facing concept drift, which proves the effectiveness and necessity of dynamic selection and dynamic integration from another perspective.Figure 7
Accuracy rate.


## 4. Construction of Enterprise Financial Risk Management and Risk Control Mode

Establishing an innovative enterprise financial risk management model can sort out the financial risks in the process of enterprise development, formulate and implement prevention and control measures, especially strengthen the management of the enterprise’s major strategy formulation, important domestic and foreign investment, and the company’s operation and management objectives, so as to effectively prevent the occurrence of major financial risks. To make innovative enterprises, we must have plans and treatment measures for major and sudden financial risk events, so that they can deal with financial risks in an emergency, so as to prevent huge losses to the company. We must establish a financial risk management system that basically covers all businesses and management and carries out financial risk management around the company’s strategic development objectives, business management objectives, and subobjectives, so as to make risk management an effective way for the company to enhance execution, improve comprehensive strength, improve operation quality, create value and make scientific decisions, and also continuously improve the company’s operation and management level and profitability as in Figure8.Figure 8
Predictive model test.The premise of building an innovative enterprise financial risk management model is to ensure the effectiveness of the model. Only if the model is effective, the follow-up work is meaningful. Before the construction of this model, we must have an in-depth understanding of the characteristics of enterprises and departments to ensure that the designed management framework is in line with the studied enterprises and can enable enterprises to successfully avoid risks. At the same time, the system involved in the management mode should also conform to the characteristics of the enterprise itself, rather than a simple system design for all enterprises as a whole. Effectiveness is the core feature of risk management model. With more and more innovative enterprises in China, there will be more and more types of management modes, which requires R&D personnel to devote more efforts to the research of target enterprises. The construction mode should fully consider the relevant factors that need to be paid attention to as much as possible.The financial risk of innovative enterprises is often inseparable from personnel. Innovative enterprises mainly rely on innovation. Only be people will be innovative. Products depend on people to design, and follow-up work such as production and sales also need people to complete. Enterprises at the cultural level need to rely on people to formulate overall strategies and set overall goals. However, due to different opportunities, different ideas, different levels of education, and different knowledge reserves for R&D, the awareness of financial risk of innovative enterprises is also different. Therefore, it is necessary to improve the risk awareness of R&D personnel when building an innovative financial risk management framework. At the same time, the construction of risk management framework needs to pay attention to human nature and the staff unite as one to make the model achieve the best effect.When constructing the financial risk management model for enterprises, the framework is appropriately adjusted in combination with the characteristics of innovative enterprises, and some factors such as its two dimensions are extended. The implementation of financial risk management is promoted according to the idea of “overall planning and step-by-step implementation” in accordance with the reality, seeking practical results, combined with its own business positioning and operating characteristics. And continuously update the financial risk management with the development of the company and the continuous improvement of operation and management requirements.After the enterprise passes the risk identification, the next task is to evaluate the output risk index results. The output results of the risk management process can be divided into three levels. The first is low risk, which can be appropriately ignored, and the risk management mechanism does not need to give an alarm. The second is the general risk alarm. In this case, enterprises will receive the alarm to remind them that they should take necessary measures to resist the occurrence of risks. The third is high risk. Issuing a high risk alarm indicates that the enterprise is already quite hazardous and needs to solve the risk as soon as possible. At this time, the committee needs to check the existing scheme library to see whether there is a scheme to deal with the risk. If not, it needs to carefully study the risk according to the risk situation and formulate effective schemes to deal with the risk in time, At the same time, it is stored in the scheme Library in case of being caught off guard in similar situations next time. The following is the risk handling flow chart set by the enterprise project risk management committee in Figure9.Figure 9
Flow chart of risk control.


## 5. Conclusion

(1)
The analysis framework breaks the financial distress modeling idea of giving a static model structure in advance on the static data set and emphasizes the role of data, that is, the classification and prediction knowledge of financial distress. The flow sample model further breaks the limitation of the static expansion period and ensures the prediction effectiveness of the financial distress prediction model over time through the dynamic update of the model, which is more scientific and meets the needs of practical application. In terms of static data, taking the system prediction accuracy as the optimization goal, two dynamic selective integration methods of multiclassifier systems are proposed from the perspective of local optimization and global optimization, which breaks the static limitation of previous combined systems.(2)
The experimental results show that based on the greedy search method designed at each step and the overall pruning strategy, the prediction performance of the combined system obtained by dynamic mining is significantly better than that of the static fully integrated system and the individual optimal single classifier. The effectiveness of this method is further illustrated in the subsequent comparative study of various search sequences. A dynamic selective integration financial distress prediction method based on genetic search is designed. The experimental results show that without considering the time cost, the performance of the optimal prediction system obtained by this global search selective integration method is better through large-scale optimization trial calculation in the feasible solution space.(3)
Financial risk exists objectively and plays an important role in the profit and loss of the enterprise and how it operates. It is unrealistic to completely eliminate the risk and its impact. This objective existence determines that we fully understand and avoid it in the investment process. Financial risk runs through the enterprise’s financial system and is reflected in various financial relations. The financial system is affected by many uncertain factors.



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*Source: 7825000-2022-05-11.xml*