Fourier transform based LSTM stock prediction model under oil shocks

Xiaohang Ren; Weixi Xu; Kun Duan; Xiaohang Ren; Weixi Xu; Kun Duan

doi:10.3934/QFE.2022015

Quantitative Finance and Economics

2022, Volume 6, Issue 2: 342-358. doi: 10.3934/QFE.2022015

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Research article

Fourier transform based LSTM stock prediction model under oil shocks

1.
School of Business, Central South University, Changsha, 410083, China
2.
School of Economics, Huazhong University of Science and Technology, Wuhan, 430074, China

Received: 13 May 2022 Revised: 13 June 2022 Accepted: 15 June 2022 Published: 20 June 2022
JEL Codes: G01, G10, G12, G15

This paper analyses the impact of various oil shocks on the stock volatility prediction by using a Fourier transform-based Long Short-Term Memory (LSTM) model. Oil shocks are decomposed into five components following individual oil price change indicators. By employing a daily dataset involving S & P 500 stock index and WTI oil futures contract, our results show that different oil shocks exert varied impacts on the dynamics of stock price volatility by using gradient descent. Having exploited the role of oil shocks, we further find that the Fourier transform-based LSTM technique improves forecasting accuracy of the stock volatility dynamics from both statistical and economic perspectives. Additional analyses reassure the robustness of our findings. Clear comprehension of the future stock market dynamics possesses important implications for sensible financial risk management.
- Fourier transform,
- LSTM,
- stock prediction,
- oil shock,
- stock volatility
Citation: Xiaohang Ren, Weixi Xu, Kun Duan. Fourier transform based LSTM stock prediction model under oil shocks[J]. Quantitative Finance and Economics, 2022, 6(2): 342-358. doi: 10.3934/QFE.2022015

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Abstract

This paper analyses the impact of various oil shocks on the stock volatility prediction by using a Fourier transform-based Long Short-Term Memory (LSTM) model. Oil shocks are decomposed into five components following individual oil price change indicators. By employing a daily dataset involving S & P 500 stock index and WTI oil futures contract, our results show that different oil shocks exert varied impacts on the dynamics of stock price volatility by using gradient descent. Having exploited the role of oil shocks, we further find that the Fourier transform-based LSTM technique improves forecasting accuracy of the stock volatility dynamics from both statistical and economic perspectives. Additional analyses reassure the robustness of our findings. Clear comprehension of the future stock market dynamics possesses important implications for sensible financial risk management.

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