BUS105 Computing Assignment

Answers:

Introduction

Investments has become part and parcel of many households. This study sought to analyse and understand the investment behaviours of various groups of people. The idea was to run a number of analysis using some given set of data. This report therefore presents the results and findings. I have subdivided the whole report in the following sections;

• Section 2: Literature review
• Section 3: Simple bivariate analysis
• Section 4: Investigating the variable return per $1000
• Section 5: Advice to the new employees at the XYZ Company
• Section 6: Conclusions and recommendations.

Reviewing academic source(s) or website(s) relevant to investors

Several scholars have spent much time researching on investments. A number of studies have therefore been published relating to investments. In my review, I sought to understand how demographic attributes affects the investments of the people. A study by (Arano, Parker, & Terry, 2010) concluded that number of children that a person has impacts greatly on the income the person gets. He went ahead to say that the more the children an individual has, the more the income the person will have since he/she will be compelled by natural forces to work extra hard to cater for the many kids.

A simple Bivariate analysis of the investor data and Hypothesis test

After reviewing the literature, I decided to focus on analysing the relationship between having children and the amount of income earned. The hypothesis I sought to answer was whether individuals with children have higher income as compared to individuals without children. 

H₀: The mean income level is same for both individuals with children and those without children (i.e. )

H₀: The mean income level is higher for both individuals with children as compared to those without children (i.e. )

Using independent t-test I tested this hypothesis at 5% level of significance. The output of the independent t-test is presented below;

The group statistics table given below shows that the mean annual income for individuals with children is $92666.67 while that of individuals who do not have children is $91846.15. 

As mentioned, I conducted the independent sample test to test the hypothesis that the mean annual income for individuals with children is significantly higher than that of individuals who do not have children. As predicted, individuals with children (M = 92666.67, SD = 17405.34, N = 87) had a much higher income than individual without children (M = 91846.15, SD = 18169.78, N = 13). However, though the individuals with children were found to have more income than those without children, the difference was not significant at 5% level of significance, t (98) = 0.158 p > .001, one-tailed. The difference of 820.51 indicated a small difference. Essentially results showed that there is no significant difference in the mean income level for individuals with children and those without children.

Investigate the variable return per $1000 

Another analysis that I did was investigating the variable return per $1000. The analysis are presented in this section. The section is divided into two:

• Computation of 90% confidence interval and
• Testing a claim that return on investment is greater than 30 (per $1000).

So I will start with the first test;

Computation of 90% confidence interval

In order for me to be able compute the interval I first had to obtain the descriptive statistics of the variable. The table below presents the summary statistics necessary for the computation of the confidence interval (90%);

Confidence Interval

Where margin of error is given by

2.447766

Confidence Interval (Upper limit) =

Confidence Interval (Lower limit) =  

As can be seen in the computations above, the 90% confidence interval is between 36.1522 and 41.0478.

Testing of the hypothesis claim that the return per $1000 is above $30 

Next after computing the 90% confidence interval, I then tested the claim as to whether the investments return per $1000 is significantly above n$30. The hypothesis that I sought to test is;

H₀: µ = 30

H₁: µ > 30

This was tested at 5% level of significance (α = 0.05).

In the next two tables, I present the results of the one-sample t-test that I used to test the claim hypothesis

As can be seen the sample mean is $38.6 (per $1000) with a standard deviation of 14.9254 and standard error of the mean as 1.4925

For the one-sample test, we see that we are rejecting the null hypothesis (p-value < 0.05). By rejecting the null hypothesis we support the claim conclude that indeed the mean return on investment per $1000 is greater than $30 at 5% level of significance.

Advice to the new employees at the XYZ Company 

Given chance to advice the new staff joining XYZ I would tell them about investments. I would take time to educate them on investments. Research as shown that majority either make wrong decision in regard to investments because they lack knowledge in investment matters I would therefore take time to educate them on the need to invest. I would actually inform them of the returns that one can make out of the investments. Giving an example of the analysis I did where I found out that the average return on investment is significantly greater than $30,000. This would be my selling point to the new employees. 

Conclusions and recommendations for further research. 

This study was about investment analysis and how the investors of XYZ can advised. I did a series of tests such as checking whether there is relationship between having children and the amount of annual income received. I also analysed the return on investments (per $1000). Results showed that there is no significant difference in the annual income of the people with children and those without children.

Works Cited

Arano, K., Parker, C., & Terry, R. (2010). Gender-based risk aversion and retirement asset allocation. Economic Inquiry, 48(1), 147-155.

Barber, B. M., & Odean, T. (2001). Boys Will be Boys: Gender, Overconfidence, and Common Stock Investment. Quarterly Journal of Economics, 116(1), 261-292.

Barberis, N., & Thaler, R. (2003). A survey of behavioral finance. Handbook of the Economics of Finance, Elsevier Science, 1051-121.

Campbell, J. Y. (n.d.). Household finance. Journal of Finance, 61, 1553–1604.

Cocco, J. F., Francisco , J. G., & Pascal, J. (2005). Consumption and portfolio choice over the life cycle,. Review of Financial Studies, 491–533.

Dhar, R., & Ning , Z. (2006). Up, close and personal: An individual level analysis of the disposition effect. Management Science, 726–740.

Feng, L., & Seasholes, M. S. (2005). Do investor sophistication and trading experience eliminate behavioral biases in financial markets? Review of Finance, 305–351.

Goetzmann, W. N., & Alok , K. (2008). quity portfolio diversification. Review of Finance, 433–463.

Gomes, F., & Alexander , M. (2005). Optimal life-cycle asset allocation: Understanding the empirical evidence. Journal of Finance, 869–904.

Graham, J. F., Stendardi , J., & Myers, J. K. (2002). Gender differences in investment strategies: An information processing perspective. International Journal of Bank Marketing, 20(1), 17-26.

Gudmunson, C., & Danes, S. (2011). Family financial socialization: Theory and Critical Review. Journal of Family Economic Issues, 1(32), 644-667.