Prediction of Enterprise Financial Health Using Machine Learning and Financial Reasons for Taiwan Economic Companies

Abstract

In actuality, the financial investment in Enterprises of the world is common. This investment is performed using internet platforms and value markets. This can generate a loss for many investors due to the uncertainty of the future financial health of the Enterprise. A comparison for the prediction of financial health based on algorithms of machine learning, particularly Support Vector Machine (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Adaptive Neuro-Fuzzy Networks (ANFIS) is presented. The database of the Taiwan Economic Journal from 1999 to 2009 is used, with 95 financial ratios of enterprises financially healthiest and with bankruptcy problems. In the ANN, the epoch number, numbers of neurons, activation functions in each layer, loss function, and learning rate are tested; also, an architecture of a Convolutional Neural Network (CNN) is implemented. In SVM, the experiments are performed using different kernels, polynomial, RBF, linear. Besides, the size of C, size of gamma, and size of the polynomial are varied. In KNN, experiments with different numbers of neighbors, types of weight, and values of P are realized. In ANFIS, experiments with variants of the numbers of fuzzy rules, quantity and type of membership functions, number of epochs, and input dimensions are performed. Optimization using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) of the three models with the best results are performed; the optimization is based on the search for the best hyperparameters that would provide a higher accuracy. The neural network models presented the best average for all the proposed tests.