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Financial time series prediction under Covid-19 pandemic crisis with Long Short-Term Memory (LSTM) network

Business

Financial time series prediction under Covid-19 pandemic crisis with Long Short-Term Memory (LSTM) network

M. Mroua and A. Lamine

Explore how Mourad Mroua and Ahlem Lamine leverage Long Short-Term Memory (LSTM) neural networks to predict financial time series during the COVID-19 pandemic. Their findings reveal that the LSTM model significantly outperforms traditional ARIMA models in accuracy, showcasing the potential of advanced machine learning in financial forecasting.... show more
Abstract
In this paper, we design and apply the Long Short-Term Memory (LSTM) neural network approach to predict several financial classes’ time series under COVID-19 pandemic crisis period. We use the S&P GSCI commodity indices and their sub-indices and consider the stock market indices for different regions. Based on the daily prices, the results show that the proposed LSTM network can form a robust prediction model to determine the optimal diversification strategies. Our prediction model achieved RMSEs and MAEs too small for the different selected financial assets, showing the predictive power of our LSTM network especially during the COVID-19 health crisis. In addition, our LSTM network outperforms ARIMA-type models for all selected assets.
Publisher
Humanities and Social Sciences Communications
Published On
Aug 25, 2023
Authors
Mourad Mroua, Ahlem Lamine
DOI
https://doi.org/10.2139/ssrn.4242252
Tags
LSTM
COVID-19
financial forecasting
commodity indices
stock market
ARIMA
time series analysis

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