Financial Early Warning of Listed Companies Based on Fireworks Algorithm Optimized Back-Propagation Neural Network

Wang, Chunzhi; Wang, Yichao; Yan, Lingyu; Ye, Zhiwei; Cai, Wencheng; Pan, Wei‐Ping

doi:10.1109/idaacs.2019.8924376

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Zhu and Wu established a new risk model by adding three accounting ratio components of three market-driven variables on the basis of the univariate model and confirmed that it is better than the univariate model through classification accuracy and bankruptcy prediction test [ 8 ]. Wang et al used five statistical techniques to predict banks' financial strength ratings (FSRs) [ 9 ]. In the aspect of early warning index research, it has experienced the process from the simple financial performance index to comprehensive financial, nonfinancial, and other multidimensional index system.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] BP Neural Network Based on Simulated Annealing Algorithm Optimization for Financial Crisis Dynamic Early Warning Model

Chen

2021

Computational Intelligence and Neuroscience

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Financial early warning mechanism is of great significance to the long-term healthy development and stable operation of listed enterprises. This paper adopts the logistic regression early warning model and BP neural network early warning model. Based on the BP neural network t early warning model optimized by the simulated annealing algorithm, the prediction effects of the model are compared from the perspectives of model accuracy and variable importance. Through the comparative analysis of the empirical results of the three methods, it can be seen that the simulated annealing algorithm has many advantages. The combination of the simulated annealing algorithm with multithreading, data compression, and segmentation greatly improves the efficiency of the algorithm and shortens the running time. Using the logistic regression early warning model and BP neural network early warning model and based on the BP neural network t early warning model optimized by the simulated annealing algorithm, the prediction effects of the model are compared from the perspective of model accuracy and variable importance. The results show that the three index dimensions of the BP neural network optimized by the simulated annealing algorithm have good discrimination ability to financial status. The BP neural network early warning model optimized based on the simulated annealing algorithm has good prediction accuracy and good practical significance.

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Section: Related Workmentioning

confidence: 99%

[Retracted] BP Neural Network Based on Simulated Annealing Algorithm Optimization for Financial Crisis Dynamic Early Warning Model

Chen

2021

Computational Intelligence and Neuroscience

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“…When investors make investment decisions, they need more information in advance. erefore, if we can establish an effective financial crisis early warning model, it is of great significance to investors' investment decision-making [5]. It is conducive to the management of the enterprise to take preventive measures.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Comprehensive Evaluation and Random Forest Model for Financial Account Audit Early Warning

Zhu¹

2022

Mobile Information Systems

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Financial risk is objective, developmental, and predictable and has an important impact on the development and operation of enterprises. China’s economy is currently facing major changes, and with the introduction of the “Made in China 2025” plan, deepening supply-side reform, and the rapid development of artificial intelligence, blockchain, and big data technology, the importance of enterprise financial early warning is becoming increasingly prominent. Therefore, by establishing and studying the changes in some financial indicators and establishing an effective enterprise financial early warning model, the signals of financial crisis can be found in time, to prevent and eliminate the hidden danger of enterprise financial crisis and ensure the financial security of the enterprise. The enterprise financial system and business management system are running well. Based on this, a financial early warning model is proposed in this study. First, financial early warning indicators are constructed, and the existing financial indicators are used to establish an early warning indicator system that can detect and identify the financial risks of the enterprise. Then, the financial early warning model based on the fuzzy comprehensive evaluation model and random forest algorithm for fuzzy comprehensive evaluation is constructed using the advantages and noise resistance of the integrated model of fuzzy comprehensive evaluation model and random forest algorithm. The financial data set is used to verify the model constructed in this study. The experimental results show that the model in this study not only is beneficial for enterprises to control financial crisis but also plays a financial early warning role.

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Recent Developments of Fireworks Algorithms

Tan

2020

Handbook of Research on Fireworks Algorithms and Swarm Intelligence

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Fireworks Algorithm has been proposed for almost 10 years. Because of its basic but profound collaborative searching manner and advantages of universal effectiveness, hundreds of scholars have conducted and published a wide range of work on it. This chapter serves as a background description of fireworks algorithms' developments by introducing its detailed status of researches and applications. Specifically, it gives a brief summarization and analysis of published researches on Fireworks Algorithms since 2010 to clarify characteristics of its historical progress and future trend in detailed fields like algorithms improvements, theoretical analysis, and practical applications.

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Financial Early Warning of Listed Companies Based on Fireworks Algorithm Optimized Back-Propagation Neural Network

Cited by 3 publications

References 15 publications

[Retracted] BP Neural Network Based on Simulated Annealing Algorithm Optimization for Financial Crisis Dynamic Early Warning Model

[Retracted] BP Neural Network Based on Simulated Annealing Algorithm Optimization for Financial Crisis Dynamic Early Warning Model

A Fuzzy Comprehensive Evaluation and Random Forest Model for Financial Account Audit Early Warning

Recent Developments of Fireworks Algorithms

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