# Construction of Early Warning Model for Financial Economic Cycle Using Genetic Algorithm

Authors: Lulu Liu
Journal: Mobile Information Systems
 (2022)

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## Abstract

Economic crises occur when the economy experiences drastic fluctuations, which in turn cause heavy losses to society and the economy. Economic crises happen when there are significant losses to society and the economy as a result of the economy experiencing abrupt changes or deep recessions. The term “economic cycle early warning mechanism” refers to a collection of theories and techniques for keeping track of, assessing, forecasting, and choosing policies regarding finances in light of the particular economic phenomenon known as economic cycle fluctuations. The traditional early warning mechanism has been unable to keep up with the demands of economic cycle early warning due to the growth of economic globalisation, and the phenomenon of incomplete early warning and poor early warning accuracy frequently occurs. A genetic algorithm simulates the natural selection and genetic mechanisms of Darwin’s theory of biological evolution. It is a computational model of the biological evolution process. It is a technique for looking for the best answer by simulating the course of natural evolution. Genetic algorithms have advanced incredibly quickly in recent years. It has been widely applied in machine learning, neural networks, control system optimization, and the social sciences as an effective, useful, and reliable optimization technique. In order to optimize the early warning mechanism and increase the thoroughness and accuracy of early warning, this paper investigates the early warning mechanism of financial and economic cycles by combining genetic algorithms. A mathematical model of the economic cycle was built during the experiment and tested in accordance with the genetic algorithm’s basic operating principle. The findings demonstrate that the genetic algorithm-based early warning system for the financial economic cycle is more complete and accurate, with a 6.4% increase in accuracy.


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## Body

## 1. Introduction

In the context of economic globalisation, all sectors of society are paying more and more attention to the economic situation. The production and operation of enterprises are affected not only by their own conditions, but also by external financial and economic markets. As an enterprise manager, you must have an accurate and comprehensive grasp of the fluctuations of the economic cycle and propose relevant strategies to deal with the fluctuations in the economic cycle; otherwise the initiative will be lost in the economic fluctuations. The economic cycle early warning mechanism can simulate and analyze the economic operation process on the basis of the economic monitoring index system and make macroscopic judgment and early warning. Applying the increasingly mature genetic algorithm to the financial and economic cycle early warning mechanism is beneficial to improve the efficiency of the early warning mechanism.As a high-performance optimization method, genetic algorithm has been widely used in various fields. Tavakkoli-Moghaddam used a genetic multiobjective optimization algorithm to establish a new automatic culling model. The results showed that the method is reasonable and effective and is more user-friendly and intelligent than the traditional method [1]. Dawid uses genetic algorithm to analyze the clustering situation in the feature space. The test results confirm that the algorithm can find suitable cluster centers in the feature space and optimize the measure of cluster similarity [2]. Qiang designed a new method to solve the optimization problem by combining monarch butterfly optimization (MBO) and harmony search (HS). Experimental results show that the search performance of this method outperforms the basic MBO and other algorithms [3]. Volkanovski established a real underwater camera calibration image dataset and then integrated the inverse learning strategy based on the slime mold algorithm (SMA) to optimize the calibration results of Zhang’s traditional calibration [4]. Gong utilizes the moth search algorithm (MSA) to solve the multidimensional knapsack problem (MKP). The experimental results show that the algorithm has a good effect in solving the MKP problem [5]. Lin constructed a fuel cell model based on the Hunger Games Search (HGS) algorithm and finally found that the numerical output accuracy of the fuel cell model based on HGS is much higher than other models [6]. Keshtkar applied the Colony Predation Algorithm (CPA) to clinical research. Practice has shown that the algorithm can effectively distinguish the severity of virus absorption in patients [7]. These studies on the genetic algorithm are relatively specific, but they have not applied the genetic algorithm to the early warning mechanism of the financial economic cycle.In order to stop problems before they start, the early warning system can provide early feedback to real-time information. There is currently a lot of academic research on the early warning system. The team of Bo and Li talked about how the early warning theory for financial risks came to be and created a calculation model for it. Experiments have demonstrated that the model can reduce local financial risks and aid in the development of early warning systems by local governments [8]. They talked about early detection systems for cascading outages. Fast computing methods and analogue identification systems have been shown to be effective in providing early warning of cascading outages [9]. Cooper covered the precise details and procedure of the pharmaceutical industry’s patent early warning system. The analysis has demonstrated that pharmaceutical companies’ early warning research can use the patent early warning mechanism as a reference [10]. Yumei examined how risk early warning systems function in nursing security work. Early warning systems can help nursing staff better recognise and address nursing risks, lessen negative nursing events, and increase nursing satisfaction, according to research findings [11]. Wang proposed the power quality prediction and early warning mechanism and later proved through examples and application analysis that the power quality prediction and early warning mechanism has strong applicability and effectiveness [12]. Tu discussed a water supply system early warning mechanism before putting forth a suggestion to construct a new water supply system by analyzing the current water supply system [13]. Zhu investigated the early warning system of network public opinion, outlined the early warning system’s fundamental structure, and offered the government a guide for handling public opinion [14]. Although the earlier research on the early warning system is still fairly in-depth, neither does it mention the early warning system for the financial and economic cycle, nor does it advocate using a genetic algorithm as an early warning system.It is crucial to have a firm understanding of the financial market’s economic cycle fluctuations given the market economy’s quick development. This paper first introduces the definition and operation of genetic algorithms and then builds a financial economic cycle early warning system based on genetic algorithms. The study’s findings demonstrate how the genetic algorithm can improve the thoroughness and precision of early warning work by modifying and innovating the original financial and economic cycle early warning mechanism.


## 2. Genetic Algorithm

### 2.1. Principle of Genetic Algorithm

Genetic algorithm is a search algorithm based on natural selection and genetic population, which imitates the reproduction, exchange, and mutation events in natural selection and human genetic process. At the beginning of a genetic algorithm, several individuals are always randomly created, each of which is evaluated and assigned a qualification value according to a given function. Based on this skill score, some individuals are selected for the next generation, and the selection function embodies the principle of “survival of the fittest” [15].Its working principle is shown in Figure1. First, the individual qualifications associated with each chromosome are evaluated, and two individuals are selected as the father and mother according to the principle that the higher the performance, the greater the possibility of selection. Then, the chromosomes of both parents are extracted and crossed to produce offspring. Eventually, the offspring’s chromosomes are mutated. The above steps are repeat until a new population is generated, and the cycle is completed.Figure 1
Basic principles of genetic algorithms.


### 2.2. Genetic Manipulation

The basic operation process of the genetic algorithm is shown in Figure2, which mainly includes selection, exchange, and mutation.Figure 2
Genetic operations of genetic algorithms.


#### 2.2.1. Choose

The selection operation is to achieve the purpose that the group can survive and evolve by itself and to improve the convergence speed and search efficiency of the group. The outstanding individuals, the mothers, are then selected from the group and allowed to produce offspring. The excellence level of each individual is quantified by its own fitness value; the higher the fitness, the better the individual. The selection work is carried out according to Darwin’s survival of the fittest condition [16], which means that when the fitness of individuals is relatively high, their probability of being selected will be higher, and the chances of producing offspring will also increase. The selection process is to first estimate the number of individuals in the group, calculate the sum of the fitness of all individuals in the group, and then select according to the proportion of the fitness of each individual, and decide that each individual should replicate itself in the next generation according to the fitness.


#### 2.2.2. Exchange

The significance of the exchange operation is to create new individuals, continuously expand the solution search space, and improve the overall search ability of the algorithm. Its specific concept refers to the operation of exchanging part of the structure of the two parent individuals to generate a new individual. The steps of the exchange operation are as follows: first, all the mating individuals are randomly paired, secondly, the exchange positions are randomly assigned to the mating individuals, and finally the mating individuals exchange some information according to the exchange probability to form a new individual. The exchange operation is the most important step in the genetic algorithm, and a new generation of individuals must be generated through the exchange operation.


#### 2.2.3. Variation

Mutation can also be called mutation, and this step is very important for optimization and evolution [17]. When the optimization process is trapped in a local optimal solution, mutation can be used to avoid the local optimal prediction solution trap. The mutation method is implemented bit by bit, that is, the information of a certain position is mutated in real time. The mutation operation is performed after the exchange operation is completed. It can randomly select one of the paired individuals and then mutate according to the mutation probability. The introduction of mutation into the genetic algorithm has two purposes: the first is to make the genetic algorithm have the ability of local random search. When the genetic algorithm approaches the optimal predicted solution through exchange, the use of mutation local search can improve the convergence rate of the optimal predicted solution. Another is to make the genetic algorithm maintain the characteristics of group diversity at all times and prevent it from showing earlier convergence phenomenon.


## 2.1. Principle of Genetic Algorithm

Genetic algorithm is a search algorithm based on natural selection and genetic population, which imitates the reproduction, exchange, and mutation events in natural selection and human genetic process. At the beginning of a genetic algorithm, several individuals are always randomly created, each of which is evaluated and assigned a qualification value according to a given function. Based on this skill score, some individuals are selected for the next generation, and the selection function embodies the principle of “survival of the fittest” [15].Its working principle is shown in Figure1. First, the individual qualifications associated with each chromosome are evaluated, and two individuals are selected as the father and mother according to the principle that the higher the performance, the greater the possibility of selection. Then, the chromosomes of both parents are extracted and crossed to produce offspring. Eventually, the offspring’s chromosomes are mutated. The above steps are repeat until a new population is generated, and the cycle is completed.Figure 1
Basic principles of genetic algorithms.


## 2.2. Genetic Manipulation

The basic operation process of the genetic algorithm is shown in Figure2, which mainly includes selection, exchange, and mutation.Figure 2
Genetic operations of genetic algorithms.


### 2.2.1. Choose

The selection operation is to achieve the purpose that the group can survive and evolve by itself and to improve the convergence speed and search efficiency of the group. The outstanding individuals, the mothers, are then selected from the group and allowed to produce offspring. The excellence level of each individual is quantified by its own fitness value; the higher the fitness, the better the individual. The selection work is carried out according to Darwin’s survival of the fittest condition [16], which means that when the fitness of individuals is relatively high, their probability of being selected will be higher, and the chances of producing offspring will also increase. The selection process is to first estimate the number of individuals in the group, calculate the sum of the fitness of all individuals in the group, and then select according to the proportion of the fitness of each individual, and decide that each individual should replicate itself in the next generation according to the fitness.


### 2.2.2. Exchange

The significance of the exchange operation is to create new individuals, continuously expand the solution search space, and improve the overall search ability of the algorithm. Its specific concept refers to the operation of exchanging part of the structure of the two parent individuals to generate a new individual. The steps of the exchange operation are as follows: first, all the mating individuals are randomly paired, secondly, the exchange positions are randomly assigned to the mating individuals, and finally the mating individuals exchange some information according to the exchange probability to form a new individual. The exchange operation is the most important step in the genetic algorithm, and a new generation of individuals must be generated through the exchange operation.


### 2.2.3. Variation

Mutation can also be called mutation, and this step is very important for optimization and evolution [17]. When the optimization process is trapped in a local optimal solution, mutation can be used to avoid the local optimal prediction solution trap. The mutation method is implemented bit by bit, that is, the information of a certain position is mutated in real time. The mutation operation is performed after the exchange operation is completed. It can randomly select one of the paired individuals and then mutate according to the mutation probability. The introduction of mutation into the genetic algorithm has two purposes: the first is to make the genetic algorithm have the ability of local random search. When the genetic algorithm approaches the optimal predicted solution through exchange, the use of mutation local search can improve the convergence rate of the optimal predicted solution. Another is to make the genetic algorithm maintain the characteristics of group diversity at all times and prevent it from showing earlier convergence phenomenon.


## 2.2.1. Choose

The selection operation is to achieve the purpose that the group can survive and evolve by itself and to improve the convergence speed and search efficiency of the group. The outstanding individuals, the mothers, are then selected from the group and allowed to produce offspring. The excellence level of each individual is quantified by its own fitness value; the higher the fitness, the better the individual. The selection work is carried out according to Darwin’s survival of the fittest condition [16], which means that when the fitness of individuals is relatively high, their probability of being selected will be higher, and the chances of producing offspring will also increase. The selection process is to first estimate the number of individuals in the group, calculate the sum of the fitness of all individuals in the group, and then select according to the proportion of the fitness of each individual, and decide that each individual should replicate itself in the next generation according to the fitness.


## 2.2.2. Exchange

The significance of the exchange operation is to create new individuals, continuously expand the solution search space, and improve the overall search ability of the algorithm. Its specific concept refers to the operation of exchanging part of the structure of the two parent individuals to generate a new individual. The steps of the exchange operation are as follows: first, all the mating individuals are randomly paired, secondly, the exchange positions are randomly assigned to the mating individuals, and finally the mating individuals exchange some information according to the exchange probability to form a new individual. The exchange operation is the most important step in the genetic algorithm, and a new generation of individuals must be generated through the exchange operation.


## 2.2.3. Variation

Mutation can also be called mutation, and this step is very important for optimization and evolution [17]. When the optimization process is trapped in a local optimal solution, mutation can be used to avoid the local optimal prediction solution trap. The mutation method is implemented bit by bit, that is, the information of a certain position is mutated in real time. The mutation operation is performed after the exchange operation is completed. It can randomly select one of the paired individuals and then mutate according to the mutation probability. The introduction of mutation into the genetic algorithm has two purposes: the first is to make the genetic algorithm have the ability of local random search. When the genetic algorithm approaches the optimal predicted solution through exchange, the use of mutation local search can improve the convergence rate of the optimal predicted solution. Another is to make the genetic algorithm maintain the characteristics of group diversity at all times and prevent it from showing earlier convergence phenomenon.


## 3. Financial Economic Cycle and the Establishment of Early Warning System Based on Genetic Algorithm

The financial economic cycle is the movement of the entire economy, with expansions and contractions, and constantly changing peaks and troughs. As shown in Figure3, the economic cycle includes four phases: recovery, boom, recession, and depression. These four stages reflect the ebb and flow of the economy in dynamic growth, and the cyclical ups and downs of the economy are not random, but recurring [18]. Generally speaking, economic cycles can be divided into four types according to the degree of impact of economic cycle fluctuations on economic growth and the length of time for the formation of the cycle:(1)
Short cycle: the duration of the cycle is about 40 months or 3.5 years. The short-term fluctuation of the economy is mainly due to temporary fluctuations caused by changes in investment caused by the increase in corporate inventory, also known as the inventory cycle.(2)
Medium cycle: the duration of the cycle is 8–10 years. It is generally believed that this cycle is caused by the constant fluctuation of fixed investment, so it is also called the fixed investment cycle.(3)
Medium and long-term cycle: the length of the cycle is generally 15–30 years. This cycle is the result of changes in construction investment, also known as the construction cycle.(4)
Long cycle: the length of the cycle is generally about 54 years. There are many reasons for the formation of this long cycle, but it is generally believed that technological innovation and industrial development are the main reasons for the formation of this cycle.Figure 3
Financial and economic cycle stages.Accurately determining the cycle stage of economic fluctuations is necessary for financial economic cycle early warning [19]. It is clear that the financial economy is developing in a cyclical fashion. The government benefits from early financial and economic cycle warnings because it can better understand the state of the economy. The primary approach for developing a genetic algorithm-based early warning system for financial economic cycles is as follows: each stage of economic development is represented by an index value that reflects the financial economic cycle, according to the real situation of economic fluctuations and stage characteristics of the financial economic cycle. The index value that influences how the financial and economic cycle fluctuates is then encoded as the genetic algorithm’s mutation input. Finally, the cycle prediction result code is created by using the time series analysis method to predict the values of various indicators that are influenced by the financial and economic cycle during a specified time period. This method is provided as a training component.The structure of the economic cycle early warning system is shown in Figure4. First, the financial and economic time information, financial sector early warning information, monitoring information, and expert opinions are gathered together. Then the genetic algorithm is used to process and integrate the data, and then experts will simulate and predict the economic cycle and make a comprehensive evaluation. Finally, the early warning classification and early warning issuance are carried out.Figure 4
Framework of the early warning system for financial economic cycle.


## 4. Application of Genetic Algorithm in the Mathematical Model of Financial and Economic Cycle Mechanism

The economic cycle theory points out that when the entire economic system cannot reach the potential output level, changes in comprehensive demand, especially changes in investment demand, play a key role in overall output fluctuations [20]. The supply curve in Figure 5 shows very clearly that real output changes with inflation, causing product prices to rise. The vertical and horizontal axes represent the price level and initial aggregate demand, respectively. The intersection of the dotted line and the curve is the starting position E0 of the economy where price level P0 is located, and actual output Y0 is determined by the supply curve Y=AS0P,P0 and the demand curve AD0.Figure 5
Supply curve.Figure6 shows the application process of the genetic algorithm in the mathematical model. According to the income identity equation Y=C+I+G and the working principle of the genetic algorithm, the mathematical model of economic operation can be obtained:(1)Yt=c+αYt−1−αYt−2+cz+iz+Gt+It.Figure 6
The application process of genetic algorithm in the mathematical model.In the formula,c represents consumption intention, Ct is consumption in period t, Yt is output in period t, It is investment in period t, Gt is government consumption in period t, and cz and iz represent consumption and investment, respectively.The basic structure of the economic cycle early warning mathematical model is composed of people’s behavioral preferences, resource constraints, capital accumulation methods, production functions, and technological shocks [22]. Think of the entire economy as consisting of many rational actors and governments with infinite lifetimes who make decisions about inputs, consumption, and labor supply. Assuming that the numbers of people in the model are all variable and behave the same, it is possible to find models that represent reasonable characters. The preferences representing rational agents can be represented by the utility functionUCt,Lt for consumption and leisure. For both variables have U′>0,U″<0,U′0∞,U′∞=0, choose consumption sequence Ct∞t=1 and leisure sequence Lt∞t=1 to maximize utility; then,(2)E0=∑t=0∞βtUCt,Lt,where β is the discount factor, and the range is 0<β<1, which reflects the preference of rational actors for consumption, and E0 represents the mathematical expectation formed according to the information at 0 moments.In resource constraints, the model assumes that the sum of labor timeNt and idle time Lt available to each agent is 1, that is, Nt+Lt=1, and the model is a closed model, with products for consumption or investment, that is, Yt=Ct+It.The capital accumulation equation is(3)Kt+1=1−δKt+It,0≤δ≤1,where δ is the depreciation rate, It is the investment in period t, and K is the amount of assets in period t.For the technical shock,A¯t represents the technical shock at time t, and the motion conditions are as follows:(4)A¯t=γA¯t−1+t.Among them,γ is the parameter 0<γ<1 of technical change.In a highly competitive economic market, the production function of a representative firm is as follows:(5)Yt+1=AtfKt,Nt,where Kt and Ntare the capital and labor input in period t, respectively, and At is the productivity at time t, in the following form:(6)At=A¯t+A^.The utility function in periodt can be set as UTCt,Lt, and rational actors need to choose effective consumption sequence Ct∞t=1 and idle sequence Lt∞t=1. When the utility is maximized, then(7)Yt=∑t=0∞βtUTCt,Lt,where β is the subjective discount factor, its range is0<β<1, Lt is the leisure factor in period t, and TCt is the consumption situation in period t. Combined with the resource constraints above, the resource constraint equation is(8)Yt=Ct+It+Gt,where Gt is government consumption. As a new variable, government consumption takes the form of(9)lnGt=1−θglnG¯+θglnGt−1+g.Among them,G¯ is a constant value, and θg∈0,1 is a certain constant.The capital accumulation equation is(10)Kt−1=G1+δKt−It,0≤δ≤1,where δ is the depreciation rate and It is the investment in period t.The consumption function is used to connect residents’ consumption with government consumption, andϕ∈0,1 represents the value of government consumption relative to residents’ consumption. The consumption function is as follows:(11)TCt=Ct+ϕGt.The technical shock must meet the following rules:(12)A¯t=γA¯t−1−∀t,where ∀ is the newly added change parameter.Then the new production function form is(13)Yt+1=AtfKt,Nt⋅∀.The productivity level ofAt is expressed as(14)At=A−A^t.The above two models are based on a closed economy. If capital and market flows between different countries are considered, new mathematical models are required. Assuming that there are two countries in the economy, each country is composed of many consumers and enterprises, each country produces different commodities, and laborers cannot move between countries, then the utility function of countryii=1,2 is(15)Ui=∑t=0∞βtUCit,1−Nit.Among them,Cit represents the consumption of country i in period t, the total utility of the two countries is αU1+1−αU2, and α∈0,1 is the given utility weight.Assuming that each country can only produce one kind of product, denoted asa and b, respectively, and the total sum of labor time Nt and idle time Lt that people in each country can control is 1, that is, Nt+Lt=1, then the resource constraint equation is(16)a1t+a2t=A1tfK1t,N1tb1t+b2t=A2tfK2t,N2t,where a1t and a2t represent the quantities of product a consumed by the two countries, respectively; b1t and b2t represent the sum of the quantities of product b consumed by the two countries, respectively; A1t and A2t, respectively, represent the productivity of the two countries at time t. Consumption and investment are composed of domestic and foreign commodities, which satisfy the following equation:(17)C1t+I1t+G1t=Ha1t,b1tC2t+I2t+G2t=Ha2t,b2t.Among them,Git is the government consumption of the ith country, and H is the Hamiltonian total.The capital accumulation equation is(18)Kit+1=1−δKit+Iit.Among them,δ∈ is the depreciation rate, and Iit is the investment of the ith country in the t period.The technical shock at this stage can be expressed as(19)A^it=γA^it−1+∀it,whereA^it represents the technological shock of the ith country at time t.


## 5. Experimental Results of the Early Warning Mechanism of Financial Economic Cycle Based on Genetic Algorithm

In order to verify the feasibility of the early warning mechanism, the year-on-year growth changes of the bank loan balance Loan, the narrow money supplyS1, and the broad money supply S2 in a country from 1990 to 2010 were selected for testing.The level value and the first-order variance oflnloant, lnS1t, and lnS2t sequences are tested for unified root, and the test results are shown in Tables 1 and 2. As can be seen from the following table, the level values of lnloant, lnS1t, and lnS2t series have a clear unified root, and they are all nonstationary time series. At the 3% significance level, lnloant, lnS1t, and lnS2t series are converted into fixed time series after first-order difference.Table 1
Level values and unit root test results 1.
SequenceLevel value testFirst-difference testStatistics TProbability value PStatistics TProbability value Plnloant−0.81790.8246−2.96750.0125lnS1t−1.35480.6124−4.28440.0036lnS2t−0.73430.7383−4.08960.0947Table 2
Level values and unit root test results 2.
SequenceLevel value testFirst-difference testStatistics TProbability value PStatistics TProbability value Plnloant−0.63150.7965−3.04050.0174lnS1t−1.04120.6728−3.98270.0028lnS2t−0.82370.8195−4.27630.0103Figure7 shows the annual growth trends of bank loan balance Loan, narrow money supply S1, and broad money supply S2 from 1990 to 2010. As shown in Figure 7, the change of bank loan balance is roughly consistent with the change of money supply, and the correlation between Loan and S1 and S2 is relatively high. Especially after 2020, due to the improvement of the market economic system, the phenomenon of administrative intervention in bank assets will gradually disappear. The lending behavior of commercial banks has been determined by the operating conditions of the market economy, and this synchronization trend is still relatively obvious.Figure 7
1990–2010 S1, S2, and the changing trend of loan balance growth rate.Real-time monitoring of economic cycle fluctuations is an important part of the early warning mechanism, and the monitoring status of GDP growth under this mechanism is selected to analyze.Figure8 shows the changes in GDP growth from 1990 to 2010. It can be seen from the histogram that the last complete economic cycle fluctuation occurred in the period 1990–2002, of which the economic fluctuation was the largest from 1992 to 1993, and the cyclical trend was obvious. It entered an upward phase after 1996, but structural imbalances between capital stock and income flows slowed economic growth in 2003.Figure 8
Nominal GDP growth 1990–2019.The financial and economic cycle consists of many parts. To verify the accuracy of the early warning mechanism, the actual value and early warning value of the total commercial output value, total retail sales of goods, regional fiscal revenue, and resident consumption index in the 12 months from January to December were compared, as shown in Figures9 and 10.Figure 9
Comparison between the actual value of the total commercial output value and the total retail sales value and the early warning value.Figure 10
Comparison of the actual value and early warning value of regional fiscal revenue and resident consumption index.From Figures9 and 10, the actual value of the total commercial output value, total retail sales of commodities, regional fiscal revenue, and resident consumption index is not much different from the early warning value. The trend of the early warning value basically reflects the actual fluctuation trend well, which also reflects the relatively accurate early warning of the economic fluctuation trend by this mechanism. It shows that the construction of economic cycle early warning mechanism based on genetic algorithm is relatively successful.Figure11 shows the comparison of the accuracy of the traditional early warning mechanism and the genetic algorithm-based early warning mechanism for the financial economic cycle in 12 months. It can be seen from the line chart that the early warning accuracy rates of the two early warning mechanisms fluctuate in each month. However, the accuracy rate of the financial and economic cycle early warning mechanism based on genetic algorithm has been higher than that of the traditional early warning mechanism, and the accuracy rate is 6.4% higher, which also confirms that the mechanism has an important reference value in the field of financial and economic cycle early warning.Figure 11
Comparison of the accuracy of the two early warning mechanisms.


## 6. Discussion

In order to improve and innovate the early warning system, the genetic algorithm is applied in this work to the early warning system of the financial economic cycle. This work develops an early warning system based on the genetic algorithm principle and employs mathematical models to combine and analyze pertinent data, along with the research status both domestically and internationally. Finally, a test is planned to validate the new early warning system for financial and economic cycles. In general, even though the genetic algorithm-based financial and economic cycle early warning mechanism described in this study lacks some professional judgment, it has significant reference value for the early warning mechanism’s future development.


## 7. Conclusion

Economic problems are increasing at all levels in the context of economic globalisation, which also presents significant challenges to the financial and economic markets and could result in financial concerns. An excellent early warning system is crucial for precisely understanding the financial and economic cycle’s fluctuation trend and developing a response strategy when no financial risk materialises. It is vital to innovate and adapt the early warning mechanism since it gradually becomes unable to fulfil the demands of social, financial, and economic development. The early warning system’s comprehensiveness and accuracy can be improved by using the genetic algorithm, a new optimization technique, to precisely calculate the data and information. For risk warning work in the financial industry, a genetic algorithm-based early warning mechanism for financial cyclical cycles will be crucial.



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