Regression analysis is a statistical tool used to "estimate the value of variable based on the value of another" (Chapter 13: Linear Regression and Correlation, 2007). Researchers have studied the unpredictable stock market and have attempted to predict the outcomes. Nevertheless, like any game, predicting the stock market is a game of chance. People look at the past performances in order to see which variables have influenced the market. In this paper we examined a list of variables and their affect on the NASDAQ and Standard & PoorÃÂÃÂs 500 (S&P 500). The following is a list of variables we have chosen to test their affect on each of these indexes: Producer Price Index, M1 Money Stock, all commodities, daily percentages; Currency in Circulation, not seasonally adjusted; Consumer Price Index for all urban consumers, all items seasonally adjusted; , millions of dollars seasonally adjusted; Civilian Unemployment Rate, Federal Funds Rate, percentage seasonally adjusted; seasonally adjusted; billions of dollars, Balance of Payment Basis, such as: trade balance, goods and services, billions of dollars, not seasonally adjusted; All Employees: finance, insurance, real estate, thousands, seasonally adjusted; Inventories, millions of dollars.
The two indexes selected for this paper are highly linked. Due to their high correlation, the economic indicators listed above we placed on a spreadsheet and ran through various tests as well as a regression analysis.
S&P 500 was established in 1860, to provide independent insight, analysis and information to the financial community to help them determine value in the marketplace. Approximately 140 years later, now, S&P500 is a most excellent global contributor of independent financial analysis and information and is still delivering on that original mission. The S&P 500 is a list of the top 500 companies in the stock market and NASDAQ is an index consisting mainly of technology firms. We...