Using the Nasdaq Index to Predict AAPL Price by Linear Regression Analysis

Abstract

In this project, we want to predict AAPL’s stock price by the NASDAQ index by the regression model. The dependent variable is AAPL’s stock price, and the independent variable is the NASDAQ index. First, we do some descriptive statistics for the two variables and measure the distribution from the central tendency, variation tendency, and distribution to acknowledge the character of distributions. Based on the strong linear relationship between AAPL stock price and the NASDAQ index, we constructed a simple linear regression model. Considering the scale of the two variables, we tried the other three models with log transformation. And then, it shows that the log-log model has the best performance. However, in the residual analysis of the log-log model, it shows an autocorrelation in the residual, then we generate a new variable that is the one-order term for AAPL and add it into the model, and it surprisingly performs very well, whose R square is up to 99.72%. Therefore, we think combining the linear relationship with the market and the autocorrelation itself can construct a good model, and it can apply to predict much other stock's prices in the market.