Statistic for Business and Finance - Assignment Example

The paper "Statistic for Business and Finance" is a great example of a finance and accounting assignment. It can be observed the graph of stock returns for GE, IBM and S&P 500 that, the stock performance is above those of GE but below the S&P 500 and hence it implies that an investor will venture in stock for IBM since, an investor will be guaranteed high returns as compared to returns of GE.

Question two

Stock returns Analysis

Mean

0.07

0.01

0.01

Median

0.06

0.01

0.01

Standard Deviation

0.04

0.03

0.03

Sample Variance

0.00

0.00

0.00

Kurtosis

266.60

836.01

880.68

Skewness

-10.25

-26.09

-27.10

Range

1.21

1.08

1.07

Minimum

-0.92

-0.99

-0.99

Maximum

0.29

0.10

0.08

It can be depicted in the above graph that the stock returns for GE is risky unlike those of IBM. This observed by the trend performance of the two stocks and their standard deviation. The implication is that an investor will consider making an investment in less volatile returns in order to minimize the risk and maximize returns on investment. In this regards, it is worth investing in IBM stock.

Question three; Test hypothesis

Test hypothesis;

H0 returns for IBM is same as returns for GE,

HI; returns for IBM is not same to returns for GE

Significance less 5%

Test statistics are the t-test

Regression Statistics

0.473

Multiple R

0.224

R Square

0.030

Adjusted R Square

0.003

Standard Error

Observations

6

ANOVA

df

SS

MS

F

Significance F

Regression

1

1.38E-05

1.38E-05

1.15E+00

3.43E-01

Residual

4

4.77E-05

1.19E-05

Total

5

6.14E-05

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

0.036126

0.001412

25.584811

0.013900

0.032206

0.040046

X Variable 1

0.279743

0.260329

1.074575

0.343065

-0.443045

1.002531

The regression output above depicts P. value of 0.3431 which is more than 0.05 and hence we reject the null hypothesis and accept the null alternative hypothesis that the stock returns for IBM are not the same as the stock returns for GE.

Question four; whether both stocks have the same population average return

Test hypothesis;

H0; both stocks have the same population average return

HI; both stocks do not have the same population average return

Significance less 5%

Test statistics are; T-test

GE

IBM

Mean

0.03666

0.00257

Variance

0.00008

0.00012

Observations

16.00000

16.00000

Hypothesized Mean Difference

0.00000

df

29.00000

t Stat

9.65267

P(T<=t) one-tail

0.00000

t Critical one-tail

1.7000

P(T<=t) two-tail

0.00000

t Critical two-tail

2.04523

It is evident in the above t-test statistic that, the stock returns for GE and those of IBM have same population average returns because the T critical one tail depicts the value of 1.7000 which is not more than the t critical two-tail implying that the null hypothesis is rejected and concluded that both stocks have similar average population average returns

Question five; Scatter plot of each of the two returns series against market return.

Scatter graph of GE and S$P 500

Correlation (GE and S$P 500)

0.002

0.001702

1

Covariance

GE

S&P 500

Column 2

5.7927E-05

0.000127167

From the above scatter graph for GE and S$P 500, it can be observed that the stock returns for S$P 500 are below those of GE while it is declining. The stock correction is weak since depicting a correlation of 0.0002. This would mean that an investor would consider venturing in stock returns for GE.

The scatter graph of IDM and S&p 500

Correlation

0.00094

0.00094

1

Covariance

S&P 500

IBM

Column 1

0.0007

Column 2

0.0001

0.0001

From the above scatter graph, it can be depicted that the stock returns for IBM are reducing above those of S$P 500. This would mean that the two stock returns portray a weak correlation since; the value of the correlation between the two stocks is 0.001. In this regards, an investor would consider making an investment ion stock returns of IBM scone, returns will be guaranteed from less volatile returns such as for IBM.