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Finance and Economics Vision

Volume 7 nos.1 July 2024 ISSN 2755-3272

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Emerging from the Pandemic: A Study on Global Stock Index Return Trend Using a Hybrid Approach of Linear Regression and LSTM Models

  • Jinghan Ji* , Jingxi Feng† , Shengran Huang‡ , Xutong Zhu§
    *Business School, University of Edinburgh, Edinburgh, EH3 9DH, United Kingdom, jijinghan05@gmail.com
    †“University of California, Los Angeles” , Address: 11050 Strathmore Drive, 90024. kinkikinsey@gmail.com
    ‡“Beijing Forestry University” , Address: Qinghua East Road No.35-5, Haidian Beijing China. huangshengran530@gmail.com
    §“University of California, San Diego” , Address; 9500 Gilman Dr, La Jolla, CA 92093, USA. zxtcjs@gmail.com

  • The sudden outbreak of the COVID-19 pandemic has brought about an undeniable impact on the national economy. In order to delve into how to more efficiently prevent the risks associated with major public health emergencies in the future, this paper focuses on the stock market’s return rate. By integrating a multiple linear regression model with an LTSM model, a comparative assessment of the stock market’s impact before and after the pandemic is conducted. The research findings indicate that there is no significant difference in stock market return rates between the two periods. Furthermore, a comparison is made between the improved model and the Purely Linear Regression Model, ARIMA, Prophet, and ESM, revealing that the improved model provides more accurate predictions of stock market return rates. This discovery holds the potential to significantly reduce the latent risks brought about by major events and crises, enabling governments to make relevant policy adjustments based on the independent variables mentioned in this paper, thus mitigating potential economic losses.

      Key words: Stock Index Returns, COVID-19 pandemic, Post-Pandemic Dynamics, Trend Prediction, Long Short-Term Memory (LSTM), Regression Analysis.

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  • The sudden outbreak of the COVID-19 pandemic has brought about an undeniable impact on the national economy. In order to delve into how to more efficiently prevent the risks associated with major public health emergencies in the future, this paper focuses on the stock market’s return rate. By integrating a multiple linear regression model with an LTSM model, a comparative assessment of the stock market’s impact before and after the pandemic is conducted. The research findings indicate that there is no significant difference in stock market return rates between the two periods. Furthermore, a comparison is made between the improved model and the Purely Linear Regression Model, ARIMA, Prophet, and ESM, revealing that the improved model provides more accurate predictions of stock market return rates. This discovery holds the potential to significantly reduce the latent risks brought about by major events and crises, enabling governments to make relevant policy adjustments based on the independent variables mentioned in this paper, thus mitigating potential economic losses.