Forecasting Financial Performance: A Comparative Study of Machine Learning Models in Accounting

This paper explores the application of different machine learning models in financial performance prediction and makes a comparative study. Machine learning models play an important role in financial performance prediction, and the commonly used models include linear regression, decision tree, random forest, support vector machines, and neural networks. Linear regression is suitable for handling linear relationships, but has limited effect for predicting complex nonlinear patterns. Decision trees and random forests are able to handle nonlinear relationships and are robust to feature selection and data incompleteness. Support vector machines perform well in handling high-dimensional data and non-linear patterns, while neural networks have the ability to process complex patterns and large-scale data. The choice of suitable model depends on the dataset, prediction target and business scenario, and to evaluate the model performance, accuracy, recall and F1 score. With the development of technology and the enrichment of data, we can expect more advanced models to apply in the field of accounting and finance in the future. To sum up, machine learning models provide a powerful tool for financial performance prediction, but in practical application, appropriate models need to be selected according to the requirements and data characteristics.