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Stock Price Prediction of E-commerce Platforms under COVID-19’s Influence Based on Machine Learning

After the COVID-19, the global economy was hit greatly. Due to various reasons, the real economy has been seriously damaged, and whether the e-commerce platform will benefit from it is under debate. This paper will study this issue and give an argument. The stock price is a simple and intuitive reflection of the value of the company, which often reflects the company’s situation. As the world’s largest economies, China and the United States have mature e-commerce conditions and huge E-commerce markets, which are representative and more applicable to macro laws. Therefore, to study the impact of the epidemic on the e-commerce industry, this paper selects the five most representative e-commerce enterprises in China and the United States, collects their stock price information in recent five years, uses machine learning (LSTM neural network and GRU neural network) to predict their stock price trend, evaluates the results and gives a conclusion. According to the results, it is found that although the share prices of three Chinese companies may fall in the short term, the positive effect of the epidemic on ecommerce platforms is greater than its negative effect.