Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019
It offers a comprehensive resource for researchers and practitioners in finance and machine learning by organizing existing DL studies and identifying future opportunities, but it is incremental as it synthesizes prior work without new empirical results.
This paper provides a systematic literature review on deep learning (DL) models for financial time series forecasting from 2005 to 2019, categorizing studies by forecasting areas and DL model types to address the lack of focused reviews in this field.
Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.