The project combines ARIMA and LSTM neural networks to forecast correlation coefficients between asset pairs. ARIMA handles linear patterns in historical price data while LSTM captures temporal dependencies and nonlinear relationships. The hybrid model outperformed traditional financial prediction methods including the full historical model, constant correlation model, single index model, and multi group model in empirical testing.