Quantum mechanics

Volatility (finance)

Statistics

Support vector machine

Economics

Computer science

Physics

Regression

Mathematics

Long short term memory

Econometrics

Artificial intelligence

Deep learning

Finance

Term (time)

Autoregressive conditional heteroskedasticity

Artificial neural network

Recurrent neural network

Machine learning

Expert Systems with Applications

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.

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