A Multi-Layer Perceptron Approach to Financial Distress Prediction with Genetic Algorithm

The ability to foresee financial distress has become an important subject of research as it can provide the organization with early warning. Furthermore, predicting financial distress is also of benefit to investors and creditors. In this paper, we propose a hybrid approach with Multi-Layer Perceptron and Genetic Algorithm for Financial Distress Prediction. There are numerous hyperparameters that can be tuned to improve the predictive performance of a neural network. We focus on genetic algorithm-based tuning of the main four hyperparameters namely Network depth, Network width, Dense layer activation function, and Network optimizer, which can make a difference in the algorithm exploding or converging. The main objective of this study is to tune the hyperparameters of the Multi-Layer Perceptron (MLP) model using an improved genetic algorithm. The prediction performance is evaluated using real data set with samples of companies from countries in MENA region. All the experiments in this study apply the technique of resampling using k-fold evaluation metrics, to get unbiased and most accurate results. The simulation results demonstrate that the proposed hybrid model outperforms the classical machine learning models in terms of predictive accuracy.