Abstract
Financial bankruptcy prediction is crucial for financial institutions in assessing the financial health of companies and individuals. Such work is necessary for financial institutions to establish effective prediction models to make appropriate lending decisions. In recent decades, various bankruptcy prediction models have been developed for academics and practitioners to predict the likelihood that a loan customer will go bankrupt. Among them, Artificial Neural Networks (ANNs) have been widely and effectively applied in bankruptcy prediction. Inspired by the mechanism of biological neurons, we propose an evolutionary pruning neural network (EPNN) model to conduct financial bankruptcy analysis. The EPNN possesses a dynamic dendritic structure that is trained by a global optimization learning algorithm: the Adaptive Differential Evolution algorithm with Optional External Archive (JADE). The EPNN can reduce the computational complexity by removing the superfluous and ineffective synapses and dendrites in the structure and is simultaneously able to achieve a competitive classification accuracy. After simplifying the structure, the EPNN can be entirely replaced by a logic circuit containing the comparators and the logic NOT, AND, and OR gates. This mechanism makes it feasible to apply the EPNN to bankruptcy analysis in hardware implementations. To verify the effectiveness of the EPNN, we adopt two benchmark datasets in our experiments. The experimental results reveal that the EPNN outperforms the Multilayer Perceptron (MLP) model and our previously developed preliminary pruning neural network (PNN) model in terms of accuracy, convergence speed, and Area Under the Receiver Operating Characteristics (ROC) curve (AUC). In addition, the EPNN also provides competitive and satisfactory classification performances in contrast with other commonly used classification methods.
Highlights
The overwhelming 2007/2008 financial crisis led to the bankruptcy of many large-scale financial institutions and made some subject to takeover by their government
Inspired by the mechanism of biological neurons, we propose an evolutionary pruning neural network (EPNN) model to conduct financial bankruptcy analysis
We build up the EPNN, which has a dendritic structure and which is trained by JADE, to achieve a high bankruptcy classification accuracy
Summary
The overwhelming 2007/2008 financial crisis led to the bankruptcy of many large-scale financial institutions and made some subject to takeover by their government. The MLP’s learning algorithm implements a gradient search to minimize the squared error between the realized and desired outputs This type of threelayer MLP is a commonly adopted ANN structure for binary classification problems such as bankruptcy prediction [11]. Many researchers have focused on making improvements to resolve these shortcomings of BP, but each method has its disadvantages [31, 32] These disadvantages make them unreliable for risk classification applications and inspire us to adopt other algorithms to train the neural model to avoid the computational inefficiency and local minimum problems. Our main contributions are clarified as follows: first, a novel EPNN model is proposed in this paper which can adopt synaptic and dendritic pruning to simplify its neuron morphology during the training process.
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