Improving Credit Protection Analysis Methods Reports of Main Agricultural Enterprises

Aleskerova, Yuliia; Mulyk, Tetiana; Fedoryshynа, Lіdiіa

doi:10.30525/2256-0742/2018-4-2-1-7

Cited by 14 publications

(2 citation statements)

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“…is is a non-financial indicator that is generally not analyzed and only qualitative [11,12]. A variety of methods are used to implement credit risk models of different levels of enterprises.…”

Section: Credit Risk Evaluation Indicatorsmentioning

confidence: 99%

Credit Risk Evaluation in Enterprise Financial Management by Using Convolutional Neural Network under the Construction of Smart City

Hui

2022

Security and Communication Networks

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Smart cities are the forward goals of future cities, and the development of small- and medium-sized enterprises (SMEs) plays a key role in it. To solve the financing difficulties of SMEs, it is necessary to scientifically, objectively, and accurately assess the credit risk of SMEs. Based on the current research and analysis of scholars in related fields, taking corporate financial data as the object, the SMEs’ credit risk is assessed through an adaptive and self-learning convolutional neural network (CNN) method. The GoogleNet method is further improved to reconstruct the credit risk evaluation model of SMEs. Finally, the influence of the convolution kernel depth on the accuracy is discussed through the optimization of the structure and the selection of the initial value of the learning rate. The results are verified by using the data in the 2016–2021 SME sector in Shanghai and Shenzhen in the wind database. It can be found that when the learning rate is finally set to 0.4 and the convolution kernel depth is set to 8 and 32 in turn, the evaluation accuracy of the test dataset is the highest, with a total accuracy of 96.8%. Therefore, it is believed that the newly constructed model has higher accuracy and more practical value than the other three typical risk assessment models.

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Section: Credit Risk Evaluation Indicatorsmentioning

confidence: 99%

Credit Risk Evaluation in Enterprise Financial Management by Using Convolutional Neural Network under the Construction of Smart City

Hui

2022

Security and Communication Networks

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“…erefore, the purpose of evaluation should become the starting point of the internal logic of evaluation [4]. Based on the theory of balanced scorecard and system theory, 12 specific and relatively independent indicators are selected from the four dimensions of beneficiaries, finance, management and operation and sustainability to form the project performance evaluation system [5]. e analytic hierarchy process is used to determine the weight of the performance evaluation index of land governance project funds [6].…”

Section: Related Workmentioning

confidence: 99%

Performance evaluation of agricultural financial funds based on smart big data analysis

Kuang

2022

Mobile Information Systems

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This paper attempts to build a performance evaluation system for agricultural financial project expenditures to make the performance evaluation of financial support for agriculture more maneuverable and provide a reference for the management of agricultural funds. Moreover, this paper combines the intelligent big data technology to construct the agricultural financial fund performance evaluation system, and describes the characteristics of the relevance of geographic elements in the entire spatial region through global spatial autocorrelation. Simultaneously, this paper uses different spatial weight matrices to examine the rationality and robustness of the results. In addition, this paper introduces covariates and removes trends through regression methods. Further, taking into account the characteristics, scientificity and operability of agricultural financial funds, the dimensions and structure of the performance evaluation of agricultural financial expenditures are drawn up. Finally, this paper verifies the effectiveness of this method through case studies, and gives several targeted suggestions.

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Profitability and financial performance of European Union farms: An analysis at both regional and national levels

Martinho

2022

Open Agriculture

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Agriculture has always been a sector with several specificities that call for adjusted interventions from public institutions through agricultural policies. This is not an exception for the context in the European Union where the Common Agricultural Policy has had more impact in some contexts than the national agricultural policies of the member-states. In turn, the profit margins are, in general, narrow and this needs specific financial and economic management. However, the financial, economic instruments, and indicators for farming are, often, ignored, or at least, not sufficiently analysed. From this perspective, the main objective of this study is to assess the net working capital framework across European Union countries and regions, including assessments through types of farming and economic size. Another objective is to analyse the impacts from financial indicators (current ratio, current assets-to-total assets ratio, current liabilities-to-total assets ratio, and debt-to-total assets ratio) on profitability (return on assets) and financial performance (return on equity). For this purpose, data from the Farm Accountancy Data Network were considered, for the period 2004–2018. These data were worked through descriptive analysis, spatial autocorrelation approaches, and panel data regressions. As main conclusions, it is worth noting the diversity of financial realities across the European farming sector and the null impacts from the liquidity ratio on the farms’ performance.

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Improving Credit Protection Analysis Methods Reports of Main Agricultural Enterprises

Cited by 14 publications

References 0 publications

Credit Risk Evaluation in Enterprise Financial Management by Using Convolutional Neural Network under the Construction of Smart City

Credit Risk Evaluation in Enterprise Financial Management by Using Convolutional Neural Network under the Construction of Smart City

Performance evaluation of agricultural financial funds based on smart big data analysis

Profitability and financial performance of European Union farms: An analysis at both regional and national levels

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