Stock Investment Strategies with Time-Varying Co-Movements Between Energy Market and Global Financial Market

Xiang Liu; Xue Wang

doi:10.54097/hbem.v21i.14789

Authors

Xiang Liu
Xue Wang

DOI:

https://doi.org/10.54097/hbem.v21i.14789

Keywords:

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Abstract

One of central challenges in guiding investment mangement is how to intelligently identify investment strategies. This paper sets up a deeping learning model for realizing stock investment strategies with time-varying co-movements between energy market and global financial market: It uses LSTM neural network approach to predict energy stock price, and employs the improved time domain connectedness measures of Diebold and Yilmaz to test the spillover mechanism of market volatility shocks. By the propsed model, this paper explores the time pattern of volatility spillover between energy market and stock price in major global financial markets (mainly including stock market) from January 3, 2000 to December 31, 2020.

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