Predicting Changes in Quarterly Corporate Earnings Using Economic Indicators
The purpose of this project is to check the validity and potentially strengthen an existing theory of business forecasting developed by Joseph H. Ellis (former research analyst at Goldman Sachs).
Mr. Ellis’ method looks at year over year percent changes in economic variables to predict trend reversals in corporate earnings (he uses S&P 500 EPS as a proxy) – purely from a visualization perspective. We have identified real interest rates, and annual percent changes in Inflation, Real Average Hourly Earnings, Real Personal Consumption Expenditures, Industrial Production, and Real Capital Spending as our potential predictor variables.
Using data mining techniques discussed in this report, we have developed a mathematical model to predict annual percent changes in S&P 500 EPS (our dependent variable). Ultimately, this model can be used to create buy and sell signals for investors in the stock market.