  Predicting the price correlation of two assets for future time periods is
important in portfolio optimization. We apply LSTM recurrent neural networks
(RNN) in predicting the stock price correlation coefficient of two individual
stocks. RNNs are competent in understanding temporal dependencies. The use of
LSTM cells further enhances its long term predictive properties. To encompass
both linearity and nonlinearity in the model, we adopt the ARIMA model as well.
The ARIMA model filters linear tendencies in the data and passes on the
residual value to the LSTM model. The ARIMA LSTM hybrid model is tested against
other traditional predictive financial models such as the full historical
model, constant correlation model, single index model and the multi group
model. In our empirical study, the predictive ability of the ARIMA-LSTM model
turned out superior to all other financial models by a significant scale. Our
work implies that it is worth considering the ARIMA LSTM model to forecast
correlation coefficient for portfolio optimization.
