Thursday, January 19, 2012

A Monte Carlo Analysis on Case of Nike, Inc.: Cost of Capital


Referring to my previous post of http://emfps.blogspot.com/2011_06_12_archive.html,
I stated: “…But I am willing to tell you that it can be a complex case in which we can doubt about sensitivity analysis done by Kimi Ford (portfolio manager) too. Because her assumptions such as Revenue Growth Rate, COGS / Sales,
S &A / Sales, Current Assets / Sales, and Current Liability / Sales have been adopted from previous income statements and balance sheets from 1995 to 2001. Perhaps, we can take new assumptions.”
As we know, the most crucial thing to bear in mind for a true financial analysis is to reach to the accurate and reasonable assumptions. We usually use from five years of annual reports to gather data from income statements and balance sheets as the sources of our assumptions. This is only an internal analysis and maybe it will be enough for small size companies. But to analyze the big size companies, we should not only have an internal analysis but also external analysis such as PEST and Porter’s Five Forces to find out competitive advantages. In this case, I have only examined the influence of the economic indicators included in Macroeconomic as driving forces but we as well as know to take a good external analysis, we should analyze the impacts of Political issues, Society-Culture, Technology and prepare a SWOT analysis compatible with value chain (value- added) and Porter’s five forces. This is only a sample of external – internal analysis for Case of Nike, Inc. in which I would like to expand a Monte Carlo Analysis on this case. How can we do our analysis?

In this article, I am willing to tell you the method of Monte Carlo Analysis done on the case of Nike, Inc.: Cost of Capital step by step as follows:

Ø  At the first, we should make a spreadsheet just like EXHIBIT 2 (Discounted Cash Flow Analysis) made by Kimi Ford. This spreadsheet will be our basic platform of the simulation model (Monte Carlo).
Ø  We should have so many scenarios on assumptions such as Revenue Growth rate (%), COGS / Sales (%), S & A / Sales (%), Tax rate (%), Current assets / Sales (%), Current liabilities/ Sales (%), Terminal value growth rate (%) and merge all scenarios to find out what the share price is most sensitive to assumptions. In fact, we would like to know which assumptions have most impact on enterprise value or share price.
Ø  To make the scenario analysis on your spreadsheet (Excel 2007), please go to Data – What-If Analysis – Scenario Manager – Add and write the name of scenario – Changing Cell – write the range of your assumption – Protection –Hide – OK.
Ø  I already made so many scenarios and I merged them together where I found that the assumptions of COGS / Sales (%), S & A / Sales (%) have most impact on share price and enterprise value.
Ø  Kimi Ford considered a range of COGS / Sales between 0.58 and 0.6, and a range of S & A / Sales between 0.25 and 0.28. These ranges could be compatible with five years income statement which is an internal analysis.
Ø  But to take an external analysis, we should find the economic indicators which are driving forces on COGS / Sales and S & A / Sales. Then we should consider the probability distribution for each range of COGS / Sales and S & A / Sales in accordance with data collected from economic indicators.
Ø  Firstly, the timing of our external analysis is very important. We should bear in mind that we are on July 5, 2001(the date of the Case). Therefore. We should collect and select economic indicators data before 2001 year to expand our projection of probability distribution for next 10 years until 2011year.
Ø  The economic indicators, which are affecting on COGS / Sales and S & A / Sales, are Unemployment rate, Inflation rate, PPI (Product Price Index), CPI (Consumer Price Index), and Economic growth rate.
Ø   I collected and selected the economic indicator data from below links:


              http://www.bls.gov/ppi/

              http://www.bls.gov/bls/newsrels.htm#OEUS




              http://www.bea.gov/




Ø  The relationship between inflation rate and unemployment rate is vice versa. It means that in the period of high inflation rate, the rate of unemployment will decrease. But a high inflation rate will increase the price of goods sold including the cost of hiring the workers. The employers have to pay the demand of workers for higher wages during the period of low unemployment rate. If we have a high unemployment rate but during the period of high inflation, the Consumer Price Index will be increase in which the price of goods in stores will go up. Whereas the employers are able to hire the cheaper workers in the period of high unemployment rate, but if the workers cannot receive the enough wages or any loan to purchase the goods, the stores will have to decrease the price of their goods where it will lead to a lower inflation. It is the same first relationship mentioned between inflation rate and unemployment rate which is Vice Versa.
Ø  According to the data selected by me from above links, the unemployment rate fell down from 1995 to 2000 year while we had an increase on PPI and inflation rate. Therefore, my assumption for probability distribution on COGS / Sales and S & A / Sales referred to the outcomes are as follows:

Outcomes
Probability
        S&A / sales
                        COGS/sales
 Stagnant
      0.1
0.25
0.58
 Slow growth
      0.3
0.26
0.59
Average growth
      0.35
0.27
0.6
 Rapid growth
      0.25
0.28
0.61


Ø  Now, I can start the Monte Carlo Analysis as follows:
Ø  I considered the formula = Rand() for COGS / Sales and S & A / Sales to generate the random numbers.
Ø  I obtained the cut-offs table for COGS / Sales and S & A / Sales separately as follows:

Cutoffs
S&A / sales
0
0.25
0.1
0.26
0.4
0.27
0.75
0.28
cutoffs
COGS/sales
0
0.58
0.1
0.59
0.4
0.6
0.75
0.61


Ø  Then, I replaced the formula = Vlookup instead of numbers 0.6 and 0.28 for 2002 year in spreadsheet and copy & paste them for all years.
Ø  Finally, I made a Two –Way Data Table where the column was included the numbers of 1 to 1000 and row was included as variable of discount rate and the impact of column and row variables were on share price. When I run this sensitivity analysis, the calculation was repeated for 1000 times from random numbers of COGS / Sales and S & A / Sales.
Ø  The result of mean and standard deviation for share prices in related to the Cost of Capital have been sorted in below table:

Mean
STDEV
WACC
Share price
Share  price
12%
30.15
2.14
11.50%
32.48
2.39
11.17%
34.03
2.62
11%
35.02
2.64
10.50%
37.43
2.96
10%
40.63
2.93
9.50%
44.34
3.48
9%
48.79
3.97
8.50%
54.20
4.68
8%
60.16
5.33



As we can see, above table shows us that if the cost of capital of Nike increase more than 9.8%, its share price will be overvalue and it will not be valuable. But, if we refer to my previous analysis mentioned on link: http://emfps.blogspot.com/2011_07_03_archive.html, we can find that WACC = 7.92% consequently to purchase the share price is valuable.

Now, let me return back today and see Close share price of Nike extracted from Yahoo. Finance from 2001 to 2011 sorted on below table:

Average for each year
Year
Share price
2001
51.08
2002
50.97
2003
56.21
2004
76.93
2005
83.59
2006
86.10
2007
70.50
2008
61.01
2009
55.40
2010
74.79
2011
87.93

What do you think about my economic analysis? Is it true or wrong?
Nowadays, Nike, Inc.’s cost of capital should be approximately 7%.




Note:  “All spreadsheets and calculation notes are available. The people, who are interested in having my spreadsheets of this method as a template for further practice, do not hesitate to ask me by sending an email to: soleimani_gh@hotmail.com or call me on my cellphone: +989109250225.   Please be informed these spreadsheets are not free of charge.”