Application of Regression Techniques on Designed Economic Data

Evaluating stock market data and public companies' performance is an overwhelming task for day traders and brokers in the United States and internationally. As a financial metric of a company's overall valuation, earnings per share is a commonly researched measure of a company's profitability. We investigate relationships between earnings per share, multiple financial measures reported from company income statements, and classifiers such as market capitalization and sector. Multiple linear regression models are developed and assessed for this data. Results conclude that there is a significant difference between sectors and earnings per share recorded for a given company. Individual stock analysis highlights that research and development and depreciation and amortization are significant predictors relevant to explaining earnings per share for companies with a market capitalization of greater than one trillion. This thesis concludes with a discussion of model results and potential avenues forward for analysis in this field.