Financial time series forecasting with deep learning : A systematic literature review: 2005–2019 - Sezer, Omer Berat; Gudelek, Mehmet Ugur; Ozbayoglu, Ahmet Murat (Applied soft computing, 2020)
Financial time series forecasting is undoubtedly the top choice of computational intelligence for finance researchers in both academia and the finance industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers have created various models, and a vast number of studies have been published accordingly. As such, a significant number of surveys exist covering ML studies on financial time series forecasting. Lately, Deep Learning (DL) models have appeared within the field, with results that significantly outperform their traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting, there is a lack of review papers that solely focus on DL for finance. Hence, the motivation of this paper is to provide a comprehensive literature review of DL studies on financial time series forecasting implementation. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, and commodity forecasting, but we also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), and Long-Short Term Memory (LSTM). We also tried to envision the future of the field by highlighting its possible setbacks and opportunities for the benefit of interested researchers. •We reviewed all searchable articles of deep learning (DL) for financial time series forecasting.•RNN based DL models (LSTM and GRU included) are the most common.•We compared DL models according to their performances in different forecasted asset classes.•To best of our knowledge, this is the first comprehensive DL survey for financial time series forecasting.•We provided current status of DL in financial time series forecasting, also highlighted the future opportunities.