Research on financial risk management model of internet supply chain based on data science

Yang, Qifeng; Wang, Yingying; Ren, Yidong

doi:10.1016/j.cogsys.2019.02.001

Cited by 68 publications

(34 citation statements)

References 6 publications

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“…The connotation of performance commitment is the amount of net profit attributable to the parent after the deduction of non-recurring losses after the upgrade. The purpose is to resolve the losses that the listed enterprise will ultimately bear due to the mismatch of information between the buyer and the seller during the M&A transaction [ 22 ]. Thanks to the basic national conditions in China, listed assets have a significant premium over non-listed assets, so that listed enterprises occupy a dominant position in the bargaining of purchases.…”

Section: Methodsmentioning

confidence: 99%

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Luo¹,

Da²

2021

PLoS ONE

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The purpose is to study the performance compensation of the bid purchased during the mergers and acquisitions (M&A) process. An intelligent model of enterprise performance appraisal is built to analyze the performances of the acquired enterprises. First, the evaluation indicators of enterprise performance are selected from both financial and non-financial aspects. An enterprise performance appraisal model is established based on the neural networks and optimized by the factor analysis method and Genetic Algorithm (GA). The principal factors affecting enterprise performance are analyzed. Then the M&A parties’ performances during the M&A commitment period under the earnings compensation mechanism are analyzed quantitatively. Corresponding hypotheses and evaluation indicators are established. Mean test results and regression analyses demonstrate that the hypotheses proposed are valid under particular circumstances. Introducing the earnings compensation mechanism during the M&A process can improve the enterprise performance effectively so that the earnings forecasted in the commitment period are significantly higher than the historical profitability. Hence, the earnings compensation mechanism plays a positive role in guiding enterprise performance. Comparison with models proposed in previous research reveals that the output error ratio of the designed corporate performance evaluation model is 1.16%, which can effectively evaluate corporate performance. The above results provide a reference for studying the impact of the earnings compensation mechanism on enterprise performance during the M&A process.

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Section: Methodsmentioning

confidence: 99%

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Luo¹,

Da²

2021

PLoS ONE

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“…Srinivasan and Kamalakannan (2018) proposed a multi-objective genetic algorithm (MOGA) which is used for financial data analysis, risk analysis and prediction for financial institutions. Yang et al (2019) proposed a mixed integer linear programming (MILP) formulation which maximizes the supply chain expected net present value while simultaneously minimizing the associated financial risk. Fisher and Yao (2017) discussed the gender differences in financial risk tolerance and mentioned that income uncertainty and net worth are the major variables that effects the relationship between gender and some risk tolerance The quantitative study of science is directed at the advancement of our knowledge and the bibliometric analysis play an important role in this field of research (Van Raan, 2004).…”

Section: Public Interest Statementmentioning

confidence: 99%

Recent trends in business financial risk – A bibliometric analysis

Syed

Bawazir

2021

Cogent Economics & Finance

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The finance literature is giving more attention to financial frisk aspects. However, in literature, only a fragmented comprehension is known about the contextual influence of financial risk aspects of businesses. In this article, we contribute by carving out the intersections between Finance and Risk and analyze the production as well as visualize the evolution and trends of this field. In our research approach we use bibliometric technique to analyze 10 years of publications in Web of Science (WoS) database and present a comprehensive contextual picture of financial risk research. We analyzed 3024 publications by identifying the most prominent journals, authors, articles, countries and collaboration among authors and countries. Moreover, a cooccurrence analysis between authors, keywords and journal was done along as well as cluster analysis and thematic analysis is also performed to find the evolutionary trends. Our results showed that credit risk is the most trend topic over the last 10 years and performance and risk taking are two most inter-connected words using co-occurrence citation analysis. Our analysis showed that the corporate governance, financial crisis, financial innovation, and entrepreneurship are evolving themes and their occurrence is increasing and become more prominent in the in the domain of financial risk.

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“…Lyu and Zhao [10] investigated the use of compressed sensing in risk evaluation of Internet finance based on big data. Yang et al [11] developed an Internet supply chain financial risk managing model through data science. Zhang [12] constructed a financial investment risk method with the help of an intelligent fuzzy neural network.…”

Section: Introductionmentioning

confidence: 99%

Research on Credit Risk Identification of Internet Financial Enterprises Based on Big Data

Peng

2021

Mobile Information Systems

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The advent of the era of big data has provided a new way of development for Internet financial credit collection. The traditional methods of credit risk identification of Internet financial enterprises cannot get the characteristics of credit risk zoning, leading to large errors in the results of credit risk identification. Therefore, this paper proposes a new method of credit risk identification based on big data for Internet financial enterprises. According to the big data perspective, the credit risk assessment steps of Internet financial enterprises are analyzed and the weight of assessment indicators is calculated using the improved analytic hierarchy process (AHP), and the linear weighted synthesis method is applied to comprehensively assess the credit of clients. Using the unique characteristics of big data credit risk region division, the big data credit risk is determined by rule-based matching method. The eXtreme Gradient Boosting (XGBoost) machine learning algorithm is used to establish a credit risk identification model of Internet financial enterprises. The kappa coefficient and ROC curve are used to evaluate the performance of the proposed method. Experimental results show that the proposed method can accurately assess the credit risk of Internet financial enterprises.

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Research on financial risk management model of internet supply chain based on data science

Cited by 68 publications

References 6 publications

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Recent trends in business financial risk – A bibliometric analysis

Research on Credit Risk Identification of Internet Financial Enterprises Based on Big Data

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