Prediction on the Price of Technology Stock and COVID-19 Based on Multiple Linear Regression

Abstract

The outbreak of COVID-19 hit the world economy. Represented as an ownership share in the company, stock is one of the most common financial products and economic indicator. However, the stock market is known as being volatile, which makes it hard to predict. Many factors may affect the stock market, e.g., interest rate, trade wars, political scandals, and widespread pandemic such as COVID-19. While stock prediction is extremely challenging, the passage uses multiple linear regression model to predict the price of three technology stock: Apple, Amazon, and Google. The author builds the linear model based on the 5 factors of stock price, including VR (Volume Variation Index), WR (Williams Overbought/Oversold index), ATR (Average True Range), TRIX (Triple Exponential Average) and Log Return of the Close. From the website, yahoo finance, the author obtained the raw data of these three technology companies in recent five years. Compared the final linear regression model with the real adjusted closing price of the stock, there is a clear difference between them. Additionally, the passage finds that TRIX, ATR and Log Return of the Close have larger influence on the predicted closing price as they have larger coefficient. According to the regression model, one can predict the price of the stock in the future. Based on the predicted stock price, it is feasible to investigate the future influence of COVID-19 on the technology price. These results shed light on guiding further exploration of impact of COVID-19 on the stock market.