Forex Trading Volatility Prediction using Neural Network Models

In this paper, we investigate the problem of predicting the future volatility of Forex currency pairs using the deep learning techniques. We show step-by-step how to construct the deep-learning network by the guidance of the empirical patterns of the intra-day volatility. The numerical results show that the multiscale Long Short-Term Memory (LSTM) model with the input of multi-currency pairs consistently achieves the state-of-the-art accuracy compared with both the conventional baselines, i.e. autoregressive and GARCH model, and the other deep learning models

A Survey of Forex and Stock Price Prediction Using Deep Learning

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

Essays on Hybrid Modeling of Machine Learning Algorithms and Financial Time Series Models

This thesis attempts to model and forecast realized volatility and stock market tail risk using hybrid models integrating Machine Learning algorithms with Financial Time Series models. One of the advantages of Machine Learning approaches is that it can well approximate a wide range class of linear and nonlinear functions, forming the input-output map by learning the data rather than assuming the data generating process. Traditional Time Series models, however, focus on reproducing the stylized facts of target variables through statistical modeling. By hybriding these two types of models, we find that Machine Learning approaches well complement Financial Time Series models in variable screening, complex relationship detection and nonlinearity modeling. In addition, it is found that instead of using raw data in the Machine Learning algorithms, Financial Time Series models generate more effective features that significantly improves learning ability of those algorithms