Performance Evaluation of Classifiers Based on Stock Prediction
Nowadays, machine learning methods are widely used for prediction. In this paper, stock prediction performance of classifiers is evaluated. It can be concluded that the accuracy of classification is influenced by the selected algorithms as well as other factors (e.g. the input form) Besides, the appreciation of assets cannot be neglected, and using multiple categories to describe the movement of stock markets may be helpful.
2020 International Conference on Computing and Data Science (CDS)