Novel volatility forecasting using deep learning-Long Short Term Memory Recurrent Neural Networks

The volatility is related to financial risk and its prediction accuracy is very important in portfolio optimisation. A large body of literature to-date suggests Support Vector Machines (SVM) as the "best of regression algorithms for financial data regression. Recent work however found that new deep learning-Long Short Term Memory Recurrent Neural Networks (LSTM RNNs) outperformed SVM for classification problems. In the present paper we conduct a new unbiased evaluation of these two modelling techniques for regression problems, and we also compare them with a popular regression model - Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model for financial volatility or risk forecasting. Our experiments using financial data show that the LSTM RNNs performed as good as v-SVR for large interval volatility forecasting and both performed much better than GARCH model for two financial indices (S&P 500 and AAPL). The LSTM RNNS deep learning method can learn from big raw data and can be run with many hidden layers and neurons under GPU to achieve a good prediction for long sequence data compared to the support vector regression. The deep learning technique - LSTM RNNs with big data can be used to improve the volatility prediction instead of v-SVR when the v-SVR does not predict well for some financial stocks of a portfolio. This will help investors to win the competition to maximize their profit. (C) 2019 Elsevier Ltd. All rights reserved.