Student Scholar Symposium Abstracts and Posters

Document Type


Publication Date

Fall 12-4-2019

Faculty Advisor(s)

Dr. Oliver Lopez


In an effort to learn more about the impact of certain economic variables on the stock market, I chose to analyze the impact that the Purchasing Managers’ Index and U.S. Gross Domestic Product have on three major stock indices: S&P 500, Dow Jones Industrial Average, and Nasdaq 100. The PMI is an index of the direction of economic trends in the manufacturing and services sector. Released on the first business day of every month, it consists of a diffusion index that summarizes whether market conditions are expanding, staying the same, or contracting. An index level greater than 50 percent suggests that the manufacturing sector generally expanded relative to the previous month, and an index level less than 50 indicates contraction. The index is weighted by each industry’s contribution to U.S. GDP. The purpose of the PMI is to provide information about current and future business conditions to company decision-makers, analysts, and investors. For my project, I will be looking at the monthly change in PMI relative to 50 compared to the percent change in closing prices for each stock index. Using this information, I will build a regression model to investigate their relationship. I will also be examining the relationship between U.S. real gross domestic product and stock closing prices. Since real GDP is a good indicator of economic growth, I would expect to see a significant positive result when GDP is regressed against each index. My hypothesis is that there will be a direct linear association between change in PMI and percent change in closing prices. Rstudio will be utilized to graph and discover the linear regression equations. The goal of this research is to determine if the PMI can offer real-time intelligence that indicates faster or slower growth before government data does, allowing analysts and investors to make more informed, timely decisions about different stocks and to predict future changes in prices.


Presented at the Fall 2019 Student Scholar Symposium at Chapman University.