Financial distress prediction in banks using Group Method of Data Handling neural network, counter propagation neural network and fuzzy ARTMAP

Ravisankar, P.; Ravi, Vadlamani

doi:10.1016/j.knosys.2010.05.007

Cited by 70 publications

(27 citation statements)

References 55 publications

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“…A number of statistical methods such as the simple univariate analysis [1], multivariate discriminant analysis technique [2], logistic regression approach [3] and factor analysis technique [4] have been typically used for financial applications including bankruptcy prediction. Recent studies in the AI approach, such as artificial neural networks (ANN) [5][6][7][8][9][10][11], rough set theory [12][13][14], support vector machines (SVM) [15][16][17], k-nearest neighbor method (KNN) [18][19][20], Bayesian network models [21,22] and other different methods such as hybrid methods and ensemble methods [23][24][25][26] have also been successfully applied to bankruptcy prediction (see [25,26] for detail). Among these techniques, ANN has become one of the most popular techniques for the prediction of corporate bankruptcy due to its high prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method

Chen

Yang

Wang

et al. 2011

Knowledge-Based Systems

166

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Section: Introductionmentioning

confidence: 99%

A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method

Chen

Yang

Wang

et al. 2011

Knowledge-Based Systems

166

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“…It is adaptive, has good learning capability and has tolerated errors. Many scholars used this method to construct an early-warning model in financial distress, for example Altman et al [1], Coats and Fant [12], Huang [25], Nien [44], Odom and Sharda [45], Ohlson [46], Pan [47], Ravisankar and Ravi [50], Tai [57], Tam and Kiang [58], Theodossiou [59], Wu [62], and Yang et al [63].…”

Section: Case Studymentioning

confidence: 99%

A novel 3D fruit fly optimization algorithm and its applications in economics

Lin

2015

Neural Comput & Applic

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Fruit fly optimization algorithm (FOA) is a method that we have previously developed from the foodfinding behavior of fruit flies to solve optimization problems. The advantage of FOA is simple and easy to understand compared to traditional stochastic algorithms. In this paper, we propose a modified algorithm called novel 3D-FOA. The performances of the 3D-FOA are far better than those of the original FOA. We select more than thirty different nonlinear functions as test vehicles to show that the search efficiency and/or quality of the 3D-FOA is superior to that of the genetic algorithm and particle swarm optimization algorithm. We also apply the 3D-FOA on some economics topics, two theoretic examples and a case study. Our results strongly suggest that the 3D-FOA can enhance capabilities in a variety of fields and future experiments.

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“…That is, the sample data of period tÀ1, tÀ2, … before financial distress are studied by BPNN, respectively, and the features are extracted, based on what the judgment for the financial state of next new period is made [16,17,20]. This treatment is a relatively complete cross-sectional analysis.…”

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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Financial distress prediction in banks using Group Method of Data Handling neural network, counter propagation neural network and fuzzy ARTMAP

Cited by 70 publications

References 55 publications

A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method

A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method

A novel 3D fruit fly optimization algorithm and its applications in economics

Application of Kalman Filtering in Dynamic Prediction for Corporate Financial Distress

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