A detailed report of my contribution to a Client's Investment Analysis Report

1. NAME: KEHINDE DEBORAH OGUNJEMILUSI
TOPIC: A DETAILED ACCOUNT OF MY CONTRIBUTION TO MR BROWN’S
PORTFOLIO

2. Introduction:
In this report, I would give a detailed analysis of my contribution to the project & critical analysis of the
portfolio performance.
Contributions:
 Downloaded historical price from S&P 500 index price for 10 sectors and identified the risk and return by
calculating the mean and standard deviation for the sectors.
 Calculated the descriptive analysis and using the summary statistics as a means of verifying the mean and
standard deviation to ascertain the correctness of the results.
 Collated the mean and standard deviation for all 38 sectors (502 companies) and eliminated companies
using the first criteria- Negative mean. At this point, we had a total of 399 companies was eliminated.
 Downloaded data on dividend paid date from Eikon based on our time period (21st Oct 2016- 23rd Oct
2017) for all 38 sectors to ascertain companies who are consistent in the payment of dividends. This we
did because we want a much higher return for Mr Brown. This brought the total number of companies to
300companies.
 Downloaded and analysed the actual dividend paid for 22 sectors to ascertain companies that regularly pay
dividend and eliminated companies below Industry average.
 Downloaded and analysed the dividend yield for all 38 sectors and calculated the average industry dividend
yield which was used as a benchmark for elimination. After which we had 170 companies.
 Collated the compound returns and carried out a correlation between the S&P 500 index and the remaining
95 companies after companies were eliminated based on ratio analysis.
 We chose companies that have a high correlation with the Index and low correlation with other correlation
for hedging and diversification purpose. I suggested we eliminate companies with correlation between 0.4
and 0.6. However, my team mate had a better suggestion; and this was what we worked with.
 Eliminated companies with beta between 1.2 and above. This brought our total number of companies to
31.
 Analysed the companies into the Manual Sharps calculator. I took into consideration all variables such as
mean return, beta, residual variance (regression analysis was done for this), excess return to beta, Cut off
rate etc. During analysis, I discovered that one company (Starbucks) had a negative excess return and thus
was eliminated, leaving us with 30 companies
 Companies whose excess return to beta was higher than the cut off rate were retained in the portfolio. This
gave us 13companies in our portfolio.

3.  The weight allocation of the sharps manual calculator was then derived (see sheet- sharps manual in the
excel file attached).
 The returns of our 30 companies were included in the sharps online calculator in order to get the weight
allocation based on risk tolerance. I used several risk tolerance level e.g. 0%, 5%, 10%, 15% and 20%. As
a group, we decided to go with 10% since Mr Brown is a risk adverse individual.
 Using the optimization output with 10% risk tolerance, we had 7 companies in the portfolio, and this was
verified with the output from the sharps manual to ascertain its correctness.
 Using the weight allocation from the sharp’s manual and online calculator output, I allocated weights to
the final selected 13 companies using four trials. The following metrics were considered – mean return,
beta, average dividend yield and the weights allocated by the sharp’s calculator.
 I inputted the selected 13 companies into the portfolio composition (weight). I observed that 3 companies
were in the same industry. This I feel is risky for our client and hence I eliminated (with the agreement of
my team) the company (Stryker) with the lowest return and average dividend yield was eliminated.
Therefore, we had 12 companies left to be invested in.
 Linear regression was done for all twelve (12) companies comparing each company with the index.
 I analysed the Optimal portfolio and allocated funds taking into consideration the beta, weight and
Investment capital. I suggested we invested €700,000 and this was agreed by my team mates. Since Mr
Brown's investment capital is in euros, I converted €700,000 to dollars using the exchange rate as at 24th
October 2017 to get the actual investment to be used on Eikon Portfolio Analytics.
 I obtained the index price as well as the value of the multiplier for a future contract on the S&P 500 Index
in order to calculate the actual number of future contracts to buy. From the calculation, I got 4.83 and this
was rounded up to 5.
 The next step I did was to design an investment strategy using an index future to hedge our portfolio against
market risk since the prices obtained from S&P 500 is in dollar and the amount to be invested is in euros.
I had to convert this to dollar to give a true reflection of the total amount lost in euros.
 Furthermore, the portfolio was inputted into the Eikon portfolio analytics by creating a profile for the group
using the S& P 500 benchmark and obtaining the codes of each company from the S&P monitor quote list.
The actual number of shares to be invested in was arrived at by calculating the actual investment divided
by the share price.
 Analysed the strategy outcome

4.  Report Writing:
Below are some of my inputs in the report;
 Executive summary
 Stock Analysis
 Optimal Portfolio
 Hedging
 Performance summary
CHALLENGES ENCOUNTERED
During the creation of portfolio on Eikon Data Analytics, after inputting the codes from the quote list and
number of shares, I encountered a challenge in which the portfolio did not show chart analysis, allocation etc
(See annexure 1). In the light of this, I chose None as the benchmark.
Portfolio Performance:
Overall, the portfolio was a profitable one. Our portfolio composition was well diversified, all elements of risk
were taking into consideration. The fact that we chose companies that paid dividend and also companies with
high dividend yield also resulted in the gain we had for Mr Brown.

5. ANNEXURE 1
ANNEXURES 2: