Improving Forecast Accuracy of Financial Vulnerability: Partial Least Squares Factor Model Approach

Ko, Kyunghwan

doi:10.2139/ssrn.2962775

Cited by 1 publication

(2 citation statements)

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“…The time series data of enterprise financial history and the current input data samples are used as the input of activation operation to improve the prediction accuracy of enterprise financial crisis. (2) The wolf pack algorithm is used to optimize the initial weight and bias parameters of LSTM, so as to avoid falling into the local minimum, thus improving its crisis prediction performance.…”

Section: Related Workmentioning

confidence: 99%

“…However, the amount of enterprise financial data is huge, and it will change rapidly with time, which makes the analysis of enterprise financial data very difficult. In recent years, due to the wide use of big data platform, the method of solving complex problems through big data analysis technology has been deeply applied in enterprise financial analysis [1][2][3][4][5]. Financial analysis based on big data learning can complete the training and analysis of high-dimensional financial data and obtain effective training results.…”

Section: Introductionmentioning

confidence: 99%

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Financial Crisis Prediction Based on Long-Term and Short-Term Memory Neural Network

Ling

Yin-ying

2022

Wireless Communications and Mobile Computing

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Enterprise financial crisis prediction analysis can predict the business process of enterprises, so that enterprises can take corresponding strategies in time. The financial crisis prediction of listed companies can effectively reflect the business situation, so as to give investors reasonable investment advice. In order to supervise the sustainable management ability of enterprises efficiently and accurately, this paper proposed a financial crisis prediction method based on long-term and short-term memory neural network, so as to provide valuable information for decision-makers. Firstly, the data in the enterprise financial system is analyzed and extracted, and the original data is cleaned and dimensionalized by normalization and feature selection. Then, the long-term and short-term memory neural network is used to build the financial early warning model, and the wolf pack algorithm is used to optimize the initial weight and bias parameters, so as to improve the efficiency of parameter optimization. Finally, the financial data of 20 large- and medium-sized enterprises from 2019 to 2021 are verified and analyzed. The experimental results show that compared with other common machine learning methods, the constructed wolf pack-optimized long-term and short-term memory neural network has the highest prediction performance in terms of root mean square error and goodness of fit, with the goodness of fit reaching 94.2%.

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Section: Related Workmentioning

confidence: 99%