Applications of deep learning in stock market prediction: recent progress
This is an incremental survey paper for researchers in finance and AI, summarizing existing work without introducing new methods.
This survey reviews recent deep learning applications for stock market prediction, categorizing data sources, neural network architectures, and evaluation metrics to help researchers synchronize with progress and reproduce studies.
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 reproducibility. Our goal is to help the interested researchers to synchronize with the latest progress and also help them to easily reproduce the previous studies as baselines. Base on the summary, we also highlight some future research directions in this topic.