Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Abstract

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.

Mind reading of the proteins: Deep-learning to forecast molecular dynamics

“…Deep learning has recently emerged as a popular paradigm for modeling dynamically evolving time series and predicting future events. These techniques have also been vastly studied in special application areas like business and finance [21] , healthcare [14] , power and energy [20].…”

Forecasting Financial Time Series through Causal and Dilated Convolutional Neural Networks

“…This despite the fact that time series does provide a suitable data representation for deep learning methods such as a convolutional neural network (CNN) [ 1 ]. Researchers and market participants (Market participants is a general expression for individuals or groups who are active in the market, such as banks, investors, investment funds, or traders (for their own account); often, we use the term trader as a synonym for market participant [ 2 ]) are still, to the most part, sticking to more historically well known and tested approaches, but there has been a slight shift of interest to deep learning methods in the past years [ 3 ]. The reason behind the shift, apart from the structure of the time series, is that the financial market is an increasingly complex system.…”

“…Omar Berat Sezer et al [ 3 ] gives a very informative review of the published literature on the subject between 2005 and 2019 and states that there has been a trend towards more usage of deep learning methods in the past five years. The review covers a wide range of deep learning methods, applied to various time series such as stock market indices, commodities and forex.…”

“…Upon reading this, it should be fairly clear that making predictions in the financial markets is no trivial task, and it should be approached with humility. However, one should not be discouraged since the models proposed in [ 3 ] do provide promising or, oftentimes, positive results.…”

Currency movement forecasting using time series analysis and long short-term memory

“…Forecasting on FOREX can be done by the method of Statistical Learning (time series analysis), Technical analysis (candle stick), and deep learning (Recurrent Neural Network, LSTM). There are some research about forecasting FOREX with any method such using deep learning (Czekalski et al, 2015;Korczak & Hemes, 2017;Nagpure, 2019;Sezer et al, 2020), ARIMA (Reddy SK, 2015), fuzzy neuron (Reddy SK, 2015) and neuro-fuzzy system (Yong et al, 2018). Forecasting provides factors to be able to predict further whether there will be a bullish or bearish.…”