Model Comparison for the Prediction of Stock Prices in the NYSE

The stock market is an integral part of investments as well as the economy. The prediction of stock prices is an exciting and challenging problem that has been considered by many due to the complexity and noise within the market and to the potential profit that can be yielded from accurate predictions. We aim to construct and compare models used for the prediction of weekly closing prices for some of the top stocks in the New York Stock Exchange (NYSE) and to discuss the relationship between stock prices and the predictor variables. Relationships explored in the study include that with macroeconomic variables such as the Federal Funds Rate and the M1 money supply and market indexes such as the CBOE Volatility Index, the Wilshire 5000 Total Market Full Cap Index, the CBOE interest rate for 10-year T-notes and bonds, and NYSE commodity indexes including XOI and HUI. Models are built using methods of regression analysis and time series analysis. Models are analyzed and compared with one another by considering their predictive ability, accuracy, fit to the underlying model assumptions, and usefulness in application. The final models considered are a pooled regression model involving the median weekly closing price across all stocks, a varying intercept model considering the weekly closing price for each individual stock, and an ARIMA time series model that predicts the median weekly closing stock price based on past prices.