Risk prediction in financial management of listed companies based on optimized BP neural network under digital economy

Li, Xuetao; Wang, Jia; Yang, Chengying

doi:10.1007/s00521-022-07377-0

Cited by 133 publications

(58 citation statements)

References 19 publications

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“…The selection of BP neural network training samples should be reasonable and general, and the data collection should be objective, scientific and respect the facts [ 26 ]. Since the data of B2B e-commerce listed companies are open and transparent, the data of B2B e-commerce listed companies selected in this paper are from the 2016 annual report and relevant statistical reports.…”

Section: Methodsmentioning

confidence: 99%

Innovation of Platform Economy Business Model Driven by BP Neural Network and Artificial Intelligence Technology

Zhou

Wang

2022

Computational Intelligence and Neuroscience

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In order to enhance the competitiveness of enterprises, how to evaluate and enhance the competitiveness of B2B e-commerce enterprises and promote the orderly and healthy development of B2B e-commerce industry are discussed. This paper puts forward the research on the innovation of platform economic business model driven by BP neural network and artificial intelligence technology. BP neural network is used to study and evaluate the competitiveness of B2B e-commerce companies. According to the B2B e-commerce company competitiveness theory and BP neural network algorithm, combined with BP neural network and B2B e-commerce company competitiveness evaluation index system, a BP neural network model is designed to analyze the competitiveness of B2B e-commerce enterprises. Determine the expected value of network samples, select G1 method to determine the subjective weight, and select entropy weight method to determine the objective weight. With the help of the function in the MATLAB neural network toolbox, the neural network is trained. The results show that when the training times reach 3297 times, the sample mean square error is 9.9869e − 06, and the training network reaches convergence. The samples of three enterprises test the trained neural network and input the data of three test samples into the trained BP neural network, and the output results are 0.1531, 0.1371, and 0.1557, respectively. The network model constructed in this paper is effectively close to the training samples. The established BP neural network has good performance and can be used to evaluate the competitiveness of B2B e-commerce companies. Accelerate technological change and realize innovation. Technological capability is the inexhaustible driving force for the development of enterprises. Only with the innovation of keeping pace with the times can application-oriented e-commerce enterprises meet the needs of customers and the market, form the difference between goods or services, and then enable enterprises to win more customers and market share.

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Section: Methodsmentioning

confidence: 99%

Innovation of Platform Economy Business Model Driven by BP Neural Network and Artificial Intelligence Technology

Zhou

Wang

2022

Computational Intelligence and Neuroscience

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“…Like standard neural network upgrade algorithms, traditional network modeling methods are not suitable for simple-scale networks. The starting point of the BP algorithm is randomly selected [ 2 ]. The depth of the network will make the learning of parameters fall into a local optimum.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things

Bai

2022

Computational Intelligence and Neuroscience

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The main goal of speech recognition technology is to use computers to convert human analog speech signals into computer-generated signals, such as behavior patterns or binary codes. Different from speaker identification and speaker confirmation, the latter attempts to identify or confirm the speaker who uttered the speech rather than the lexical content contained in it. The short-term idea is that it should be able to record the musical sound played by the user with a certain musical instrument, then extract the note and duration information from it, and finally generate the corresponding MID file according to the MIDI standard, which can set the type of musical instrument in advance to complete the function of musical sound transformation, such as playing with a harmonica, and playing the MID at the end is the piano sound. With the rapid development of the mobile Internet, fields such as machine learning, electronic communication, and navigation have placed high demands on real-time and standard text recognition technology. This paper merges the sound of visual music into text-based data set training, uses the exported scanner features for model training, uses the model to extract features, then uses the features for prior training, and then uses pretraining. DNN results show that the combined training of target prevention and expansion plans, by replacing long-term and short-term memory networks, end-to-end speech recognition programs, and behavioral tests organized by mobile devices, can provide a larger receptive field combined with expanded convolution instead of long and short periods. The experimental results show that when the input sampling point is 2400, it can be seen that the convergence speed of the model becomes slower with more than 90 iterations and the loss of the model on the verification set increases with the increase in the number of iterations. This shows that the model in this paper can fully meet the needs of speech recognition in piano music scenes.

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“…Khouri et al defined the operation failure of listed companies according to different research purposes as (1) enterprises unable to repay interest and principal, (2) enterprises with ROE lower than the bank interest rate, (3) ST enterprises, and (4) PT enterprises [11]. Li et al for financial risk warning put forward the optimized BP nervous system as a financial warning model and ensure its high prediction accuracy; through the analysis of the financial risk of listed companies in 2017-2020, we found that the accuracy of the BPNN has reached more than 80% and proved the effectiveness of the BPNN optimization [12].…”

Section: State Of the Artmentioning

confidence: 70%

Design and Application of a Financial Distress Early Warning Model Based on Data Reasoning and Pattern Recognition

2022

Advances in Multimedia

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Since the 1990s, emerging market financial crises have occurred frequently, causing huge damage to the real economy, and if we cannot find effective means of early warning and prevention of financial crises, the entire international economy and society will bear the high costs of crisis management. Difference nonparametric test and Spearman nonparametric correlation analysis were carried out with cash flow financial data, and 14 financial indicators with strong discriminant ability were selected from 28 financial indicators as the input variables of the model. Due to the limitations of traditional statistical methods, a BP neural network financial distress early warning model is established. Finally, a particle swarm optimization BP neural network financial early warning model is established for the shortcomings of the BP network. These 14 indicators can have strong information timeliness. The prediction accuracy rates of the two early warning models for the test samples are 80% and 85%, respectively. The empirical results show that the two models have good prediction effects. The prediction effect of the swarm optimization BP neural network model is better than that of the BP neural network model. Therefore, the particle swarm optimization BP neural network model proposed in this paper is suitable for solving the problem of discrimination and prediction of the financial distress of enterprises. The company’s financial distress early warning has good application prospects and application value. Therefore, it has very important research significance for the early warning of the corporate financial crisis.

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Risk prediction in financial management of listed companies based on optimized BP neural network under digital economy

Cited by 133 publications

References 19 publications

Innovation of Platform Economy Business Model Driven by BP Neural Network and Artificial Intelligence Technology

Innovation of Platform Economy Business Model Driven by BP Neural Network and Artificial Intelligence Technology

Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things

Design and Application of a Financial Distress Early Warning Model Based on Data Reasoning and Pattern Recognition

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