Date Presented

Spring 4-27-2018

Document Type

Thesis

Degree Name

Bachelor of Arts

Department

Business Administration

First Advisor

Dr. Laura Kicklighter

Second Advisor

Dr. Gerald Prante

Third Advisor

Dr. Eric Kyper

Abstract

Finance and Economics are central to all business functions including management of money/wealth, business management, production, and consumption. Business and corporate operations are predicated on accurate comprehension of these key aspects, both to predict changes in the economy, and understand the constant changes in their environment. This study showed how well correlated the overall S&P 500 is with the economy, as well as each individual sector; are some sectors better indicators than others? Identifying the best correlation to use as a prediction model will allow policy makers, businessmen, and investors to use this information to make more educated business decisions such as predicting future sources of tax revenue, whether to purchase assets with cash or debt, or the deciding to invest in equities or fixed income markets. This model can also help with deciding how far in advance these changes should be made. Historical S&P 500 and GDP data was collected for comparison. Statistical analyses utilized regression models that revealed a moderate positive correlation between them. The model was used to track the economy and the stock market to see how well and how far in advance the prediction holds true, if at all. The hope is that the model will be able to correctly make predictions a couple of quarters in advance, and describe why the changes are occurring. This study will be unique because rather than focusing on when to invest it is focusing on how policy makers, businessmen, and investors can use the model.

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