BP neural network-based early warning model for financial risk of internet financial companies

Song, Xiao; Jing, Yage; Xuan, Qi

doi:10.1080/23322039.2023.2210362

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Song et al ( 2023) utilized the K-means clustering algorithm to categorize a company's financial condition as either ''healthy'' or ''early warning.'' They employed the BPNN algorithm to construct an early warning model, confirming that the model achieved accuracy, precision, recall, and specificity rates of 99.51%, 99.71%, 99.71%, and 98.30%, respectively [14]. Zhao created a financial risk evaluation model that relies on principal component analysis and RBF neural networks, effectively assessing financial risk for port enterprises [15].…”

Section: Literature Reviewmentioning

confidence: 93%

Financial Risk Early Warning Model for Listed Companies Using BP Neural Network and Rough Set Theory

Liu,

Yang

2024

IEEE Access

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In current financial environment, listed companies are facing increasingly complex markets and ever-changing financial risks. The companies deeply recognize the crucial role of financial risk management in the sustainable development of enterprises. The challenge lies in rapidly changing market conditions, and traditional methods are difficult to predict risks in a timely and accurate manner. Therefore, improving the accuracy and timeliness of financial risk prediction has become an urgent need in the current field to reduce potential losses and maintain the financial health of enterprises. This work aims to enhance the accuracy and timeliness of predicting financial risks for listed companies and reduce potential losses caused by these risks. In the research process, a large volume of data is initially collected, including financial statements, market data, and financial risk event data. Subsequently, the Rough Set Theory (RST) is employed for feature selection to identify financial indicators and market factors highly relevant to financial risk. Finally, a financial risk early warning model on the basis of the Back Propagation Neural Network (BPNN) is built, and then trained and optimized using historical data. Cross-validation analysis is employed to assess the model's performance, and the model is compared with traditional financial risk early warning methods. The findings reveal that the financial risk early warning model based on RST and the BPNN demonstrates high accuracy and reliability in predicting financial risks for listed companies. The model exhibits excellent performance in terms of accuracy, recall, and F1 score, achieving rates of 96%, 95%, and 95.50%, respectively. These research findings are expected to positively impact the financial sector and provide financial decision-makers with more accurate risk early warning and decision support.

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Section: Literature Reviewmentioning

confidence: 93%

Financial Risk Early Warning Model for Listed Companies Using BP Neural Network and Rough Set Theory

Liu,

Yang

2024

IEEE Access

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Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

Wang,

Chen,

Zhang

et al. 2023

Energy Science & Engineering

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The hydraulic turbine in turbine mode (TMHT) has significant advantages in residual energy recovery, but rapid optimization of its runner parameters has always been a challenge. To address this issue, the optimization algorithm ISMA‐BPNN is proposed based on the improved slime mold algorithm (ISMA) and back propagation neural network (BPNN). First, an efficiency characteristic neural network (ECNN) and a water head characteristic neural network (HCNN), which take the geometric parameters of the runner as input, and the efficiency and water head as outputs, respectively, are constructed by combining the orthogonal test‐based sample data, computational fluid dynamics (CFD), and BPNN. Then, the slime mold algorithm is improved and the optimal runner geometric parameters are obtained based on the ISMA, ECNN, and HCNN, to achieve rapid optimization of the TMHT runner. Finally, the CFD‐based numerical calculation accuracy is verified through real machine tests, and the feasibility of the ISMA‐BPNN‐based rapid optimization strategy for TMHT performance is further verified through CFD numerical simulation.

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Multidimensional Financial Metrics for Corporate Financial Risk Assessment and Early Warning Mechanisms

Wei,

Wong,

Sun

et al. 2024

Journal of Organizational and End User Computing

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By constructing a comprehensive multi-dimensional financial index evaluation system, this study effectively identifies, evaluates, and forewarns the financial risks of enterprises. Utilizing public financial data from S&P Global and Alpha Vantage, a BP neural network-based enterprise financial risk assessment framework is built, and the KMO method analyzes different risk factors. This comprehensive evaluation model, paired with a risk warning mechanism, assesses financial situations through financial leverage, profitability, solvency, operating efficiency, growth indicators, and cash flow indicators. The example analysis shows: 1) The RMSE value ranges from 46.71 to 64.94, indicating high accuracy and stability in predicting financial risk; 2) The R value is very close to 1, demonstrating high stability.

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BP neural network-based early warning model for financial risk of internet financial companies

Cited by 3 publications

References 28 publications

Financial Risk Early Warning Model for Listed Companies Using BP Neural Network and Rough Set Theory

Financial Risk Early Warning Model for Listed Companies Using BP Neural Network and Rough Set Theory

Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

Multidimensional Financial Metrics for Corporate Financial Risk Assessment and Early Warning Mechanisms

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