High-Frequency Data and Machine Learning

摘要

Forecasting financial asset prices has always been a challenging task due to a vast array of factor. Forecasting stock prices using econometric models is satisfactory when the data exhibits a certain statistical regularity, however, this regularity is not standard for this type of data. Considering that the advancement of computer technology, making them increasingly faster, the popularization of high-speed internet, the development of secure trading platforms, and the interconnection of markets have significantly increased the volume of transactions. With the increase in the number of transactions per unit of time, it became impossible for a human trader to keep up with this volume of buying and selling transactions of financial assets, thus giving rise to trading robots. In this chapter is made a comparision between traditional methods and machine learning methods to forecast time series