# The mechanism of dividend distribution and management equity ratio interaction based on wireless network mode

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## Abstract

In the stock market of China, the rights and interests of small- and medium-sized investors are frequently encroached upon. This criticism makes it an urgent task to effectively restrain the large shareholders and the insiders of the listed companies on the wealth and theft of small and medium investors. The trading data of listed companies show the characteristics of exponential explosion, rapid disclosure of information, and increasing information collection channels. Therefore, in this paper, the BP artificial neural network in data mining was introduced to study the proportion of dividend distribution and equity management. Based on the analysis of the principle and implementation process of BP neural network, the algorithm model was further optimized and updated, establishing a dividend policy identification model with the purpose of protecting the interests of small- and medium-sized investors. The simulation results show that this research has opened up a new research path for the small and medium investors’ equity interests and related policy research.

## Keywords

Wireless network Dividend distribution Management equity Research## Abbreviation

- BP
Back propagation neural network

## 1 Introduction

Although the domestic stock market has been developing for decades, the mechanism of dividend policy is still a stumbling block to the development of the stock market (Ernayani R et al. 2017) [1]. The irregular fluctuations and ups and downs of relevant policies in the stock market, the unrestricted dividend distribution of the listed companies, the characteristics of low dividend payback, or higher non-violation of dividends, greatly affect the healthy and sustainable development of the stock market and hit the investor’s investment confidence and enthusiasm. In order to protect the rights and interests of small and medium investors who are in a weak position in the stock market, a good atmosphere and environment must be established for the management of stock market investment with Chinese characteristic (Chen Q et al. 2017) [2]. Although in recent years, the national legal level, the introduction of the LLSV index system has a clear evaluation and management of investor rights and interests. Internationally, the law and regulations are also advanced. Within the listed companies, there is no effective maintenance of the rights and interests of small and medium investors, and even some companies are still trying to avoid the legal rights and interests of legal compliance (Nielsen T et al. 2016) [3]. In particular, the large shareholders in the listed companies are often more rapid and ahead of the company’s information sources. In this convenient condition, the large shareholders adopt various kinds of practices of irregularities for favoritism and occupy the “oil and water” of small and medium investors from various levels (Zhang Z et al. 2016) [4]. Many scholars have studied the stock market data of the past 10 years, and found that in the listed companies with the same dividend distribution, the more the ownership of the stock, the announcement of the dividend policy will be more welcomed by the investors (Qiu C et al. 2016) [5]. When the listed companies carry out the same dividend distribution, the more the mutual supervision and restriction among all the shareholders is standardized, the more successful the dividend policy is to get the good evaluation of the investors (Yang B et al. 2016) [6]. Under the support of this research result, the research on the interaction mechanism of dividend distribution and equity proportion is not only very necessary, but also has a positive scientific guidance role.

## 2 State of the art

The popularity of wireless network also promotes the prosperity of domestic stock market and ushering in new development space. With the exponential increase of Internet data, the channel for small and medium investors to get dividend policy is wider and more efficient. In this paper, the BP artificial neural network in data mining is introduced to establish a dividend policy identification model for the purpose of protecting the interests of small and medium investors, which explore a new way for the study of equity interests and related dividend policies for small- and medium-sized investors. The study of artificial neural networks began in the 40s of last century, which was originally used to study the function of the brain and the electrophysiological function of neurons. The initial mathematical model is a form neuron (Ji H et al. 2017) [7]. Neural network is composed and connected by many simple neurons. The combination of different rules can form a neural network with different structure and complexity. The non-linear characteristics of this network can achieve parallel and adaptive processing, which has great structural and algorithmic advantages in the operation of complexity understanding (Gao H et al. 2017) [8].

The neural network is also a bionic model, which can simulate the function of the human brain neural system and can realize the same function as human, such as the ability of storing and processing information, the ability to analyze and processing, and the function of simplifying, summarizing, and simulating the information, making this algorithm more intelligent. Neural network uses a large number of neuron nodes to represent the structure and function of human brain. Use inductive learning method and large-scale empirical research to repeat learning. In the process of continuous adaptation, the weights of the interconnections between the neurons are corrected, so that the mutual structure and weights of the neural network are distributed steadily. The whole process is the learning process of human knowledge acquisition (Yang W R et al. 2016) [9]. From the current statistical data, most of the neural network models are based on BP neural network. The BP neural network adapts to the change of the system by learning autonomously to change the connection value of the internal network, which has better fault tolerance and robustness. The multi input and output structure model can be better used to study and analyze the system parameters of multivariable (Guanzhou D U et al. 2017) [10].

## 3 Methodology

### 3.1 BP artificial neural network

*w*and

*θ*are initialized. The connection weight of the input layer to the hidden layer neuron is

*w*

_{ij}, and the connection weight of the hidden layer to the output layer is

*w*

_{jk}. The threshold of the hidden layer is set to

*θ*

_{j}, giving the output threshold

*θ*

_{k}a smaller number of more than 0 and less than 1. Then, the input vector

*x*

_{i}= (

*x*

_{1},

*x*

_{2}, ⋯,

*x*

_{m}) is determined and instead of the corresponding expected output vector \( \overset{\wedge }{Y_i}=\left(\overset{\wedge }{Y_1},\overset{\wedge }{Y_2},\cdots, \overset{\wedge }{Y_n}\right) \). The value of the

*x*

_{i}is input to the neuron node of the input layer. The forward calculation is carried out according to the \( {x}_j^i=f\left(\sum \limits_{i=0}^n{W}_{ij}{x}_i-{\theta}_j\right)\kern0.5em \left(j=1,2,\cdots, u\right) \), or the inverse calculation is carried out according to the \( {y}_k=f\left(\sum \limits_{k=0}^n{V}_{jk}{x}_j-{\theta}_k\right)\kern0.5em \left(k=1,2,\cdots, n\right) \). Then, the output value of the output layer neuron is compared with the expected output value, which gets a numerical value called the error value. If the result of the error is expected, the algorithm is finished. If the error exceeds expected, it re-enters the reverse calculation link of the model calculation. Finally, after revising the function calculation, the weights that meet the requirements are obtained. The end of the model calculation is carried out to output the signals. Therefore, the BP neural network learning algorithm flow is shown in Fig. 2.

So how many hidden layers need to be determined through experiments.

### 3.2 Optimization strategy of BP artificial neural network

*m*

_{c}equals zero, the weight is adjusted according to the gradient descent method. In

*m*

_{c}= 1, the adjustment of the new weight value is the same as the change of the last weight, which not account for the adjustment amount brought by the gradient descent method. After adding momentum into calculation, the network weight is very small in the bottom of the error surface.

*∇f*(

*w*(

*n*)) becomes very small, so

*w*(

*n*+ 1) ≈

*w*(

*n*). To prevent the occurrence of

*w*(

*n*+ 1) = 0 and help the network to get out of the local minimum, the weight adjustment formula is optimized as a Eq. (1).

*n*is the number of training.

*m*

_{c}is the momentum coefficient.

*η*represents the learning rate. Let us set

*m*

_{c}∈ [0, 1]. Assuming that the error precision is 0.0008, the nonlinear function is calculated by the additional kinetic energy method. Set the learning rate randomly and calculate the average of 45 times. The graph between kinetic energy coefficient and learning time is obtained, as shown in Fig. 4. After introducing momentum, the speed of network learning is improved. This adjustment method can effectively avoid the occurrence of network local minimum and reduce the recurrence of errors.

*η*(

*n*) is the learning rate at the time of the

*N*iteration.

*E*(

*n*) and

*E*(

*n*+ 1) are the values of the two error functions of the front and back times. The constant value is set in the initial setting. When

*E*(

*n*) <

*E*(

*n*+ 1), the error is reduced. The learning rate increases to a time of the past, and the convergence speed is accelerated. When

*E*(

*n*) >

*E*(

*n*+ 1) indicates an increase in the error, at this time the weight is over-adjusted in the iteration, it is necessary to reduce the learning rate to the original b times, which avoid crossing the gradient and cause the local optimum weight. In order to reduce the error of BP neural network in training, the value of directivity function indicating the direction of search can be reduced. This situation slows down the convergence rate of the network and searches results for local minimums do not meet the needs. At the same time, conjugate gradient algorithm is introduced to provide direction vectors for search. This algorithm takes the error function of the weight setting range as the two line function and can calculate the accurate approximation at one time. The implementation process is first setting the target function =min

*E*(

*w*)

*w*∈

*R*. When the minimum value of the error function is searched according to the gradient direction, the Eq. (3) can be obtained, and the network is corrected.

In order to solve the problem that BP neural network is not easy to be determined by network structure, it is easy to cause local minimum and so on. In this paper, genetic algorithm is introduced to optimize it to improve the performance of neural network. Genetic algorithm has great advantage in global search. Based on population, it uses individual fitness as a standard to judge the subsequent legacy operation. Genetic algorithm not only has good global search ability, but also improves the local search ability in the presence of mutation operator. The main forms of genetic algorithm for neural network optimization are as follows: first, optimize the topological structure of each layer in the neural network and the various parameters of the neuron. In order to solve the problem that the hidden layer and node number of the neural network cannot be accurately determined, the genetic algorithm is used to optimize the topology structure and then the network parameters are optimized. Second, if the neural network structure is clear, the genetic algorithm is used to update the threshold and weight of the neural network.

## 4 Result analysis and discussion

In order to verify the research on the share distribution and policy based on BP neural network, the relevant data of the listed companies of steel industry from 2010 to 2017 are selected to carry out the simulation experiment. The database from 2010 to 2015 served as training data. Using 2016 to 2017 data as test data, the accuracy of neural network is evaluated. First, the model design is carried out. The related parameter design is that the transfer function of the hidden layer and the output layer uses the Tansig function, the training function is purelin function, the actual interval is set to 16, the learning rate of the network is 0.0012, the maximum training time is 360 times, the target error is 0.6*10^ (− 11).

Comparison between predicted and actual values

Particular year | Predicted value (%) | Real value (%) | Relative error (%) | |||
---|---|---|---|---|---|---|

Managers’ proportion | The proportion of researchers | Managers’ proportion | The proportion of researchers | Managers’ proportion | The proportion of researchers | |

2016 | 3.35 | 62.21 | 3.31 | 61.79 | 0.56 | 0.04 |

2017 | 3.22 | 64.34 | 3.42 | 64.05 | 0.61 | 0.52 |

## 5 Conclusion

As an evaluation model, BP neural network performs accurately, qualitatively, and efficiently, making it very capable of dealing with nonlinear problems. As an important representative of the efficient mathematical model, the model has made positive contributions to the management of all aspects of human society. Therefore, in this paper, BP neural network mathematical model is proposed using to study the proportion of dividend distribution and equity management. After in-depth analysis of the structure of BP neural network, it leads to over fitting phenomenon for the network to determine weights and thresholds randomly. Human factors affect the number of nodes, which bring about long network learning time, too many iterations and poor learning rate. The optimization is carried out to improve the accuracy and efficiency of the BP neural network prediction model. The weights of isoparametric values are improved to optimize the structure of BP neural network. The network learning rate formula is improved to help the network reduce the correction and overcome the slow convergence of the algorithm. The genetic algorithm is introduced to update the algorithm flow. After improving the performance of neural network, the optimized BP neural network is simulated. From the result of verification, the evaluation model based on BP neural network is successful and can help small and medium investors effectively identify dividend policies of listed companies. However, there are still some improvements in this study. The next step is to further study the prediction accuracy of neural models.

## Notes

### Funding

No Funding

### Author’s contributions

DF has made many contributions to the interactive mechanism of dividend distribution and management equity ratio of wireless network model. The author read and approved the final manuscript.

### Author’s information

Daihong Fu, Master of management, Associate professor. Graduated from Xi’an Jiaotong University in 2004. Worked in China University of Petroleum. Her research interests include Accounting and Audit issues of listed companies.

### Competing interests

The author declares that he is no competing interests.

### Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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