Fourier transform based LSTM stock prediction model under oil shocks

IF 3.2 Q1 BUSINESS, FINANCE Quantitative Finance and Economics Pub Date : 2022-01-01 DOI:10.3934/qfe.2022015
Xiaohang Ren, Weixin Xu, Kun Duan
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引用次数: 5

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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石油冲击下基于傅里叶变换的LSTM库存预测模型
本文利用基于傅立叶变换的长短期记忆(LSTM)模型分析了各种石油冲击对股票波动率预测的影响。石油冲击根据个别石油价格变化指标分解为五个部分。通过采用标准普尔500指数和WTI原油期货合约的日常数据,我们的研究结果表明,不同的石油冲击对股票价格波动动态的影响是不同的。利用石油冲击的作用,我们进一步发现基于傅立叶变换的LSTM技术从统计和经济角度提高了股票波动动态的预测精度。进一步的分析证实了我们发现的稳健性。对未来股票市场动态的清晰理解对明智的金融风险管理具有重要意义。
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来源期刊
CiteScore
0.30
自引率
1.90%
发文量
14
审稿时长
12 weeks
期刊最新文献
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