Applications of deep learning in stock market prediction: recent progress Weiwei Jiang∗ Department of Electronic Engineering, Tsinghua University, Beijing 100084, China Abstract Stock market prediction has been a classical yet challenging problem, with the attention from both economists and computer scientists. With the purpose of building an effective prediction model, both linear and machine learning tools have been explored for the past couple of decades. Lately, deep learning models have been introduced as new frontiers for this topic and the rapid development is too fast to catch up. Hence, our motivation for this survey is to give a latest review of recent works on deep learning models for stock market prediction. We not only category the different data sources, various neural network structures, and common used evaluation metrics, but also the implementation and repro- ducibility. Our goal is to help the interested researchers to synchronize with the latest progress and also help them to easily reproduce the previous stud- ies as baselines. Base on the summary, we also highlight some future research directions in this topic. Keywords: Stock market prediction, deep learning, machine learning, feedforward neural network, convolutional neural network, recurrent neural network arXiv:2003.01859v1 [q-fin.ST] 29 Feb 2020 1. Introduction Stock market prediction is a classical problem in the intersection of finance and computer science. For this problem, the famous efficient market hypoth- ∗Corresponding author. E-mail address:
[email protected] Preprint submitted to Elsevier Journal March 5, 2020 esis (EMH) gives a pessimistic view and implies that financial market is ef- ficient (Fama, 1965), which maintains that technical analysis or fundamental analysis (or any analysis) would not yield any consistent over-average profit to investors.