Financial Time Series Models—Comprehensive Review of Deep Learning Approaches and Practical Recommendations

Abstract

There have been numerous advances in financial time series forecasting in recent years. Most of them use deep learning techniques. We identified 15 outstanding papers that have been published in the last seven years and have tried to prove the superiority of their approach to forecasting one-dimensional financial time series using deep learning techniques. In order to objectively compare these approaches, we analysed the proposed statistical models and then reviewed and reproduced them. The models were trained to predict, one day in advance, the value of 29 indices and the stock and commodity prices over five different time periods (from 2007 to 2022), with 4 in-sample years and 1 out-of-sample year. Our findings indicated that, first of all, most of these approaches do not beat the naive approach, and only some barely beat it. Most of the researchers did not provide enough data necessary to fully replicate the approach, not to mention the codes. We provide a set of practical recommendations of when to use which models based on the data sample that we provide.

Keywords

• financial forecast
• deep learning
• reproducibility
• forecast comparison