Essays on Stock Returns for IBM, Test Hypothesis Assignment

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The paper "Stock Returns for IBM, Test Hypothesis " is a great example of a business assignment.   It can be depicted that the stock returns for IBM are declining while for S$P 500 is growing while for GE is increasing but below those of IBM and S& P 500. The implication is that the returns of the stock for IBM is recommended for investment since it will lead to high positive returns within the shortest time possible. Question two Stock returns Analysis   GE IBM S& P 500 Mean 0.068 0.006 0.008 Median 0.063 0.007 0.008 Standard Deviation 0.041 0.031 0.031 Sample Variance 0.002 0.001 0.001 Kurtosis 266.603 836.012 880.676 Skewness -10.247 -26.090 -27.104 Range 1.211 1.082 1.067 Minimum -0.923 -0.986 -0.987 Maximum 0.288 0.096 0.080 Sum 85.352 7.778 9.798 Count 1257.000 1257.000 1257.000 Confidence Level (95.0%) 0.002 0.002 0.002 From the histogram above, it can be observed that the stock returns for GE are riskier unlike the stock returns for IBM as depicted by the standard devastation of the returns.

The implication is that an investor should consider investing in less volatile stocks which is IBM for our case since investment risk will be minimal and with positive high returns on investment is guaranteed with regards to IBM stocks. IBM is relatively less risky unlike GE returns since the sample variance of the returns between the stocks would depict the stock for IDM to with less variance as compared to stock returns for GE. 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 t-test         Variable 1 Variable 2 Mean 0.03666 0.00257 Variance 0.00008 0.00012 Observations 16 16 Hypothesized Mean Difference 0   df 29   t Stat 9.6527   P(T< =t) one-tail 0.0000   t Critical one-tail 1.6991   P(T< =t) two-tail 0.0000   t Critical two-tail 2.0452   From the above table of t statistics assuming unequal; variance, it can be observed that p-values are less than twp tail since, the value is 2.0452 hence, we reject the null hypothesis and accept the alternative that, the returns for IBM is not same to returns for GE since 2.0452 is greater than 0.05 Question four Whether both stocks have the same population average return Question five Scatter plot of each of the two returns series against market return. Scatter graph of GE and S$P 500 Correlation       0.00170   0.001702 1         Covariance       GE S& P 500 Column 2 5.7927E-05 0.000127167 From the scatter graph, it can be observed that the stock returns for GE is above those of S& P but declining.

The two stocks depict a correlation of 0.001702 and a standard deviation of 0.000127. This would mean that the two stocks depict a week correction and wide standard deviation and thus, investment in GE stocks is not recommended since it is very risky and volatile. 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 of IDM returns and S& P 500, it is evident that the stocks of IDM are declining but above the S$P 500.

The correlation and covariance of the twp stock returns are 0.0094 and 0.001 which would imply that the two stock returns have a weak association and hence, cannot be relied upon in making an investment decision. In this regards, an investor will consider making a correlation and covariance between the two stock returns of IBM and GE in which case, the stock returns of IBM would be recommended since it is less volatile and guarantees high returns on investment due to high value of the stock unlike those of GE.

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