  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.
