Develop Multiple Regression Model to Predict Index Fund Performance
1. Choose your favorite Index fund (e.g., DJIA, S&P 500, Wilshire 5000) and get at least 25 years of
historical data of index performance.
2. Choose at least three market indicators (e.g., unemployment, GDP, 10-Yr. Treas. Rate, CPI, Initial
Jobless Claims, inflation of US dollar) and get at least 25 years of historical data.
3. Create a multiple regression model that predicts the value of the index fund based on the three
market indicators that you have chosen.
4. Perform a residual analysis to determine the validity of the regression model
5. Determine the usefulness of each of the independent variables using by performing a
hypothesis test on each of the slopes for the independent variables.
6. Use a Durbin Watson statistic to determine if autocorrelation is present in your data.
7. Check for interaction between your variables.
8. Predict the average index value based on a 95% confidence level for three different
combinations of values of your market indicators.
Report should include the following:
1.
2.
3.
4.
10 page limit, double spaced
Sections on Introduction, Methods, Results and Conclusions
Appropriate figures and tables to describe your data, analysis and results
List all assumptions made in your analysis
You may work with a partner, but you each must turn in your own report with your own analysis and
conclusions.
The following websites may be of use:
https://research.stlouisfed.org/fred2/
https://research.stlouisfed.org/fred2/series/DJIA/downloaddata
https://research.stlouisfed.org/fred2/series/SP500/downloaddata
https://research.stlouisfed.org/fred2/series/WILL5000INDFC/downloaddata