BP-Neural Network Model for Financial Risk Warning in Medicine Listed Company

Shen, Hong; Cui, Juan; Zhou, Zhongbo; Min, Hao

doi:10.1109/cso.2011.97

Cited by 3 publications

(5 citation statements)

References 6 publications

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“…With the development of computer technology, some new methods based on artificial intelligence technology with distributed computing capabilities that can deal with problems of nonlinear systems are widely introduced into the field of financial distress prediction. These methods include neural network (NN), genetic algorithm (GA), rough set theory (RST), casebased reasoning (CBR), and support vector machine (SVM) [6][7][8][9][10][11][12][13][14]. Each model established for financial distress prediction, whether based on statistical methods or artificial intelligence methods, has advantages and disadvantages under different conditions.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Prediction of Financial Distress Based on Kalman Filtering

Zhuang

Chen

2014

Discrete Dynamics in Nature and Society

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The widely used discriminant models currently for financial distress prediction have deficiencies in dynamics. Based on the dynamic nature of corporate financial distress, dynamic prediction models consisting of a process model and a discriminant model, which are used to describe the dynamic process and discriminant rules of financial distress, respectively, is established. The operation of the dynamic prediction is achieved by Kalman filtering algorithm. And a generaln-step-ahead prediction algorithm based on Kalman filtering is deduced in order for prospective prediction. An empirical study for China’s manufacturing industry has been conducted and the results have proved the accuracy and advance of predicting financial distress in such case.

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Section: Introductionmentioning

confidence: 99%

“…BPNN is considered as an effective tool of pattern recognition for nonlinear systems. So many researchers have tried to apply triple BPNN in financial distress prediction, using the nonlinear pattern recognition capability of BPNN for classification of different financial state [7,8,15].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Prediction of Financial Distress Based on Kalman Filtering

Zhuang

Chen

2014

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

show abstract

“…With the development of computer technology, some new methods based on artificial intelligence technology with distributed computing capabilities that can deal with problems of nonlinear systems are widely introduced into the field of financial distress prediction. These methods include neural network (NN), genetic algorithm (GA), rough set theory (RST), case-based reasoning (CBR) and support vector machine (SVM), and so on [6][7][8][9][10][11][12][13][14]. Each model established for financial distress prediction, whether based on statistical methods or artificial intelligence methods, has advantages and disadvantages under different conditions.…”

Section: Introductionmentioning

confidence: 99%

“…BPNN is considered as an effective tool of pattern recognition for nonlinear systems. Therefore, many researchers have tried to apply triple BPNN in financial distress prediction, using the nonlinear pattern recognition capability of BPNN for classification of different financial state [7,8,15].…”

Section: Introductionmentioning

confidence: 99%

Application of Kalman Filtering in Dynamic Prediction for Corporate Financial Distress

Zhuang¹

2018

Kalman Filters - Theory for Advanced Applications

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This chapter aims to dynamically improve the method of predicting financial distress based on Kalman filtering. Financial distress prediction (FDP) is an important study area of corporate finance. The widely used discriminant models currently for financial distress prediction have deficiencies in dynamics. Based on the state-space method, we establish two models that are used to describe the dynamic process and discriminant rules of financial distress, respectively, that is, a process model and a discriminant model. These two models collectively are called dynamic prediction models for financial distress. The operation of the dynamic prediction is achieved by Kalman filtering algorithm, and further, a general n-step-ahead prediction algorithm based on Kalman filtering is derived for prospective prediction. We also conduct an empirical study for China's manufacturing industry, and the results have proved the accuracy and advance of predicting financial distress in such case.

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“…Jun Tang and Lei He [3] use BP neural network to excavate the pattern and rule of customer transaction behavior, and isolate suspicious financial transactions. Xiangguang Shen and Xiaozhong Song [4] build a business financial crisis prewarning model based on BP neural network to conduct empirical analysis of the financial situation in Chinese enterprises Hong Shen [5] researches the financial data of listed 71 medicine companies in order to establish a more accurate financial pre-warning model with the analysis of BP (Back Propagation)-neural network. Zhibin Liu and Shaomei Yang [6] propose the improved BP neural network imports the adjustable activation function and Levenberg -Marquardt optimization algorithm to forecast the financial risk of the power enterprises scientifically and accurately.…”

Section: Introductionmentioning

confidence: 99%