Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods

Investors collect information from trading market and make investing decision based on collected information, i.e. belief of future trend of security’s price. Therefore, several mainstream trend analysis methodology come into being and develop gradually. However, precise trend predicting has long been a difficult problem because of overwhelming market information. Although traditional time series models like ARIMA and GARCH have been researched and proved to be effective in predicting, their performances are still far from satisfying. Machine learning, as an emerging research field in recent years, has brought about many incredible improvements in tasks such as regressing and classifying, and it’s also promising to exploit the methodology in financial time series predicting. In this paper, the predicting precision of financial time series between traditional time series models ARIMA, and mainstream machine learning models including logistic regression, multiple-layer perceptron, support vector machine along with deep learning model denoising auto-encoder are compared through experiment on real data sets composed of three stock index data including Dow 30, S&P 500 and Nasdaq. The result shows that machine learning as a modern method actually far surpasses traditional models in precision.