A Survey of Forex and Stock Price Prediction Using Deep Learning

TL;DR

This survey reviews deep learning methods applied to Forex and stock price prediction, highlighting recent trends, datasets, models, and performance metrics used in the field.

Contribution

It provides a comprehensive classification and analysis of deep learning techniques and their effectiveness in financial prediction tasks based on recent literature.

Findings

LSTM combined with other methods like DNN is popular.

Reinforcement learning shows promising performance.

Deep learning usage in financial modeling is rapidly increasing.

Abstract

The prediction of stock and foreign exchange (Forex) had always been a hot and profitable area of study. Deep learning application had proven to yields better accuracy and return in the field of financial prediction and forecasting. In this survey we selected papers from the DBLP database for comparison and analysis. We classified papers according to different deep learning methods, which included: Convolutional neural network (CNN), Long Short-Term Memory (LSTM), Deep neural network (DNN), Recurrent Neural Network (RNN), Reinforcement Learning, and other deep learning methods such as HAN, NLP, and Wavenet. Furthermore, this paper reviewed the dataset, variable, model, and results of each article. The survey presented the results through the most used performance metrics: RMSE, MAPE, MAE, MSE, accuracy, Sharpe ratio, and return rate. We identified that recent models that combined LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning method yielded great returns and performances. We conclude that in recent years the trend of using deep-learning based method for financial modeling is exponentially rising.