REGRESSION ANALYSIS

Using Regression Analysis



Regression Analysis

In this paper we would be predicting the stock price of General Electric based on the Dow Jones Index, for the purpose of this analysis we will imply Regression analysis on the . Dow Jones is the oldest of the existing U.S. market indices. It was created by the editor of The Wall Street Journal and founder of Dow Jones & Company by Charles Dow to track the development of the U.S. stock markets. For the first time the index was published on May 26, 1896. Initially, the index was calculated as the average share price of 12 largest companies. Now used to calculate the mean scaled to maintain consistency of the index to reflect changes in the internal structure of its constituent stocks.

Currently, the Dow Jones covers the 30 largest U.S. companies. They are the simplest and most general indicator of the U.S. economy. List of companies covered by the Dow Jones, reviewed the development of the situation on the stock market. The compilation of this list is the newspaper The Wall Street Journal.

Regression analysis enables to find a reasonable relationship between the input variables and output through empirical relationships. The data collection provides information about the nature of the relationship between variables and studies able to accommodate unexpected situations, such as variability in the stocks, temperature, and machine operators.

Prediction - Since we expect that much of the variation in the stocks variable is explained by the index variables, we can use the model to obtain values ??of General Electric Stocks corresponding to Dow Jones Index values ??that were not among the data.

DJI up by 15%

-4.237608

DJI down by 15%

-4.242392

SUMMARY OUTPUT

Regression Statistics

 

Multiple R

0.622387978

R Square

0.387366796

Adjusted R Square

0.377323628

Standard Error

4.586825139

Observations

63

The figure above shows the summary output for the Regression analysis that has been applied on the General Electric stock and Dow Jones Index; we have taken 63 historical prices from 2nd Jan, 2008 to 1st March, 2013. The magnitude of R-square i.e. 0.38 shows that the very low amount of variability is caused by the DJI, which means that correlation may be due to chance.

ANOVA

 

d.f.

SS

MS

F

Significance F

Regression

1

811.4767151

811.4767151

38.57018254

5.16109E-08

Residual

61

1283.376856

21.03896486

 

 

Total

62

2094.853571

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

-4.245107743

3.841644572

-1.105023555

0.273488925

-11.92695076

3.436735279

-11.92695076

3.436735279

DJI Close

0.002087008

0.000336046

6.210489719

5.16109E-08

0.001415043

0.002758973

0.001415043

0.002758973

From the above output the equation for regression can be obtained. The linear regression model is given by

Y = a + ß * X

Where,

a = intercept

ß= slope

DJI (X) = Explanatory Variable

General Electric Close (Y) = Response variable

Here the intercept of the model a =-4.24, and the slope of the model ß=0.00208, so the linear regression model to explain and predict the response variable (Y) through the relationship between (X) and (Y) variables is given by

GE Close = -4.24 + (0.00208*DJI)

The coefficient of determination provides information about the capacity of the explanatory variable (X) to explain the response variable (Y). In this linear regression the coefficient of determination =0.38. This means that approximately 38% of variability in the response variable (Y) i.e. General Electric Stocks is caused by the explanatory variable (X) i.e. DJI. The coefficient is not statistically significant so it is likely that this ...