Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model

TL;DR

This paper introduces a hybrid ARIMA-LSTM model for predicting stock price correlation coefficients, demonstrating superior accuracy over traditional models in empirical tests, which can enhance portfolio optimization strategies.

Contribution

The paper presents a novel hybrid ARIMA-LSTM approach that combines linear and nonlinear modeling for improved correlation prediction in financial data.

Findings

ARIMA-LSTM outperforms traditional models significantly

Hybrid model captures both linear and nonlinear dependencies

Empirical results show improved prediction accuracy

Abstract

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.