Time Series Analysis in American Stock Market Recovering in Post COVID-19 Pandemic Period

TL;DR

This paper analyzes the post-COVID-19 recovery of the American stock market using deep learning and time-series models, particularly LSTM, to predict market trends based on ETF data from major indices.

Contribution

It introduces a novel application of LSTM neural networks for predicting stock market trends during the post-pandemic recovery period.

Findings

LSTM effectively predicts continued market decline post-COVID-19.

Deep learning models outperform traditional methods in trend analysis.

Proposes a new allocation method based on LSTM predictions.

Abstract

Every financial crisis has caused a dual shock to the global economy. The shortage of market liquidity, such as default in debt and bonds, has led to the spread of bankruptcies, such as Lehman Brothers in 2008. Using the data for the ETFs of the S&P 500, Nasdaq 100, and Dow Jones Industrial Average collected from Yahoo Finance, this study implemented Deep Learning, Neuro Network, and Time-series to analyze the trend of the American Stock Market in the post-COVID-19 period. LSTM model in Neuro Network to predict the future trend, which suggests the US stock market keeps falling for the post-COVID-19 period. This study reveals a reasonable allocation method of Long Short-Term Memory for which there is strong evidence.