Research on Intelligent Prediction Method of Financial Crisis of Listed Enterprises Based on Random Forest Algorithm

Jiang, Mingxia; Wang, Xuexia

doi:10.1155/2021/3807480

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…Models established for NMA, UCA, UPA, and UEA are designated as model I, III, IV, and IV, respectively. According to the established RF model [49][50][51][52][53][54][55][56][57][58], the R 2 of each level can be calculated. e numb of ntree is set as 100 as a parameter input.…”

Section: Leading Factors Of Carbon Reserves Distribution In Nmamentioning

confidence: 99%

Deducing Leading Factors of Spatial Distribution of Carbon Reserves in Nanjing Metropolitan Area Based on Random Forest Model

Xue

Yan

2022

Computational Intelligence and Neuroscience

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Improving carbon reserves is considered to be an important way to alleviate global warming. However, there is a lack of research work based on the perspective of metropolitan area, and there is also a lack of analysis on the leading influencing factors of spatial distribution of carbon storage in subregions of metropolitan area. In this study, Nanjing metropolitan area (NMA) is taken as the research area, and the InVEST model is used to calculate the spatial distribution of regional carbon reserves, and the evolution of carbon reserves distribution in recent 20 years is analyzed. Then, based on the random forest (RF) model, taking the whole study area and subareas as the research scope, a regression model of each selected impact factor and carbon reserves is established, and the leading factors of spatial distribution of carbon reserves in NMA are obtained. The results show that the overall carbon reserves level in the study area is in a downward trend. Through the application of the RF model, the leading factors of the spatial distribution of carbon reserves in NMA and its subareas are derived. The research proves that the application of the RF model in the analysis is helpful for city planners and governments to make plans and improve regional carbon storage more effectively.

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Section: Leading Factors Of Carbon Reserves Distribution In Nmamentioning

confidence: 99%

Deducing Leading Factors of Spatial Distribution of Carbon Reserves in Nanjing Metropolitan Area Based on Random Forest Model

Xue

Yan

2022

Computational Intelligence and Neuroscience

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“…To evaluate the financial security status of coal enterprise mergers and acquisitions, the following financial crisis index [22][23][24][25] of the related companies in the coal industry is established.…”

Section: Financial Crisis Index Definition and Data Preparationmentioning

confidence: 99%

Integrated Evaluation of Financial Risk in Coal Industry Restructuring: A Model of Linear Regression and PCA

Yuan

Zhang

2022

Computational Intelligence and Neuroscience

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Aiming at the integrated evaluation problem of financial risk in coal industry restructuring, a model of linear regression and PCA is put forward. This paper studies the univariate correlation and multivariable mixed correlation between the main business income and the book value of fixed assets and nonfixed assets in the statements of coal listed companies and gives the correlation function between the variables by using a variety of univariate linear, univariate nonlinear, and binary linear regression methods. It also points out that the coal enterprises in China are basically in the stage of increasing scale income at the present stage and can continue to achieve rapid increase in profits through mergers and acquisitions and other expansion methods. At the same time, it is also concluded that nonfixed assets, namely, intangible assets and human capital, contribute more to the main business income of coal enterprises in China, which objectively proves the correctness of our thinking of developing knowledge economy.

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“…The key idea is that public information of corporations comprises significant data and information that could be used by investors to asses financial status, which may be a major reason to cause bankruptcy [2]. Financial crisis prediction indicators included Profitability, Solvency, Growth ability, Cash flow and Capital structure [3]. Enhanced prediction accuracy is bound to increase the earnings to shareholders by improving financial risk management inside rising markets [4].…”

Section: Introductionmentioning

confidence: 99%

Improving Prediction Accuracy using Random Forest Algorithm

Elsayed,

Elaleem,

Marie

2024

IJACSA

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One of the latest studies in predicting bankruptcy is the performance of the financial prediction models. Although several models have been developed, they ofte n do not achieve high performance, especially when using an imbalanced data set. This highlights the need for more exact prediction models. This paper examines the application as well as the benefits of machine learning with the purpose of constructing pre diction models in the field of corporate financial performance. There is a lack of scientific research related to the effects of using random forest algorithms in attribute selection and prediction process for enhancing financial prediction. This paper tes ts various feature selection methods along with different prediction models to fill the gap. The study used a quantitative approach to develop and propose a business failure model. The approach involved analyzing and preprocessing a large dataset of bankrupt and non-bankrupt enterprises. The performance of the model was then evaluated using various metrics such as accuracy, precision, and recall. Findings from the present study show that random forest is recommended as the best model to predict corporate bankruptcy. Moreover, findings write down that the proper use of attribute selection methods helps to enhance the prediction precision of the proposed models. The use of random forest algorithm in feature selection and prediction can produce more exact and more reliable results in predicting bankruptcy. The study proves the potential of machine learning techniques to enhance financial performance.

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Research on Intelligent Prediction Method of Financial Crisis of Listed Enterprises Based on Random Forest Algorithm

Cited by 9 publications

References 5 publications

Deducing Leading Factors of Spatial Distribution of Carbon Reserves in Nanjing Metropolitan Area Based on Random Forest Model

Deducing Leading Factors of Spatial Distribution of Carbon Reserves in Nanjing Metropolitan Area Based on Random Forest Model

Integrated Evaluation of Financial Risk in Coal Industry Restructuring: A Model of Linear Regression and PCA

Improving Prediction Accuracy using Random Forest Algorithm

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