Capital Budgeting Risk Analysis
Group Assignment
PROGRAMME: BBA-MBA Integrated Programme
Batch: 2018-23
Term: 8
Course Name: Strategic Financial Management
Submitted by:
Submitted to:
Group 9
Dr. Aditya Sharma
Kirti Satwani (187129)
Lavina Daryani (187131)
Rishika Singh (187145)
Sakshi Singh (187150)
Shriya Heda (187154)
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Acknowledgement
We would like to extend our sincere gratitude to Prof. Aditya Sharma for including this assignment
in the curriculum of Strategic Financial Management. This assignment has given us great insights
about project analysis.
We would like to extend our gratitude to the Institute of Management, Nirma University for giving
us the opportunity to learn this subject as a part of our course.
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Contents
I.
Cash Flow ............................................................................................................................... 4
II.
Sensitivity Analysis ............................................................................................................ 5
III.
Scenario Analysis ............................................................................................................... 6
IV.
Simulation Analysis ........................................................................................................... 9
V. Conclusion ............................................................................................................................ 11
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I.
•
Cash Flow
On looking at the cash flows for the project, it could be seen that the company will face a
loss for initial three years of the project, from 0 to 2.
•
These losses could be traced to the various costs that they will be incurring in the beginning
of the project like construction cost, land, etc.
•
From year 3rd, the company will receive a profit for each year till the 17th year.
•
The higher rate of IRR than the WACC suggests that the company will have higher cash
flows in the project and will be able to cover to financing cost of the project investment.
•
The NPV for the project comes out to be 29.06 after adjusting the cash flows for inflation
and discounting factor, which is a good sign indicating that the project will be beneficial
to the company overall.
•
Based on the above stated findings, the project seems to be a favorable one and could be
undertaken by the company.
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II.
Sensitivity Analysis
Sensitivity Chart
100.000
80.000
60.000
40.000
20.000
-50%
-40%
-30%
-20%
0.000
-10%
0%
10%
20%
30%
40%
50%
-20.000
-40.000
Sesitivity Chart Rentals
Sesitivity Chart Share of Sales
Sesitivity Chart Op. & Maint. Cost
Sesitivity Chart Real Estate Taxes
Sesitivity Chart WACC
•
Sensitivity analysis is a financial model that determines how target variables are affected
based on changes in other variables known as input variables. This model is also referred
to as what-if or simulation analysis. It is a way to predict the outcome of a decision given
a certain range of variables.
•
In case of Radhamohan Gupta’s Mall project, there were major 4 reasons of concern:
o Rentals
o Share of sales
o Operating & Maintenance cost
o Real estate taxes
o WACC
•
Our target variable is our NPV.
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Deviations
-40%
-30%
-15%
0%
15%
30%
Rentals
3.961
10.236
19.649
29.062
38.475
47.888
40%
54.163
Sensitivity Chart
Share of Sales
Op. & Maint. Cost
-21.140
49.980
-8.590
44.750
10.236
36.906
29.062
29.062
47.888
21.218
66.714
13.374
79.264
Real Estate Taxes
37.429
35.337
32.200
29.062
25.924
22.787
WACC
76.546
62.627
44.468
29.062
15.938
4.712
20.695
-1.874
8.144
•
Rentals: The deviations on rentals reveal a high impact on the value of the project’s NPV.
•
Share of sales: The deviations reveal a high impact on the value of the project’s NPV.
•
Op. & maintenance cost: On increasing the deviations, the project’s NPV is seen to be
increasing. The deviations reveal a moderate level of impact on the value of the project’s
NPV
•
Real estate taxes: On increasing the deviations in real estate taxes, the NPV of the project
is seen to be decreasing.
•
WACC: On increasing the deviations in this variable, the NPV of the project is seen to be
decreasing.
•
On looking at the chart, the line for real estate taxes is the flattest one, revealing that it is
the least sensitive variable.
•
III.
On the other hand, share of sales is the most sensitive of the other variables.
Scenario Analysis
Scenario analysis is the process of estimating the expected value of a portfolio after a given change
in the values of key factors take place.
Both likely scenarios and unlikely worst-case events can be tested in this fashion- often relying on
computer simulations.
Scenario analysis can apply to investment strategy as well as corporate finance.
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Base case- This case is built on the most anticipated outcomes that a business can have.
After constructing the cash flow from the given details in the case, the net cash flow and the NPV
of that Cash Flow comes out to be $29.06 million in Base Case scenario which shows the expected
present value of the project. The IRR comes out to be 12.33% and WACC will remain as 9%. IRR
is higher than the WACC and therefore it can be interpreted that the decision should be to approve
the project.
Best Case- This is the best-case scenario, in which certain assumptions are made in the project's
favor.
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The assumptions made are those stated in the question •
The figures for sales, rents, and costs remain identical.
•
The WACC is currently at 7%.
•
The rate of inflation is 10%.
•
The project is done on time.
We receive net cash flows after constructing the cash flow from the given parameters, and the
NPV of that CF comes out to be $197.45 million in this scenario, indicating the project's expected
present value. The WACC has also shifted to a more favorable 7%. The IRR is 20.20 percent,
which is larger than the WACC, indicating that the project is extremely profitable.
Worst case- This is the worst-case scenario, in which some assumptions are made in the project's
favor.
The assumptions made are those stated in the question •
Sales have dropped by 40%.
•
The annual inflation rate is 2%.
•
Due to construction delays, the project is one year behind schedule.
•
The WACC is currently at 11%.
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The sales of retail outlets were reduced by 40% in this scenario, with 5% of that being our cash
intake, resulting in a significant reduction in net cash flow. Aside from that, building starts a year
late, the cost of construction overruns by 25%, and the WACC is boosted to 11%.
Mean
Standard Deviation
Coefficient of Variation
Share of sales
Rental
Wacc
Share of sales
Rental
Wacc
Share of sales
Rental
0.25
Best Case
67.66
33.83
7.00%
28.36
14.18
0.5
Base Case
29.36
14.68
9.00%
2.60
1.30
0.25
Worst Case
17.97
14.98
11.00%
1.59
1.33
0.4190692
0.4190692
0.0884933
0.0884933
0.088493304
0.088493304
Total
36.1
19.5
9%
8.79
4.53
0.02
0.24
0.23
The coefficient of variation for share of sales is 0.24 that means that there is 24% of risk per return
for sales and 23% of risk per return on rental revenue.
IV.
Simulation Analysis
Descriptive statistics
NPV
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence Level(95.0%)
57.44231333
2.89389144
61.8206814
#N/A
94.66166174
8960.830203
0.742630589
-0.486663056
727.4551578
-353.5382108
373.916947
61463.27526
1070
5.67835213
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No. OF Trials
1069
Share of
Sales
Mean
Std. Dev
Max
Min
35.81565968
8.848493978
67.6193542
8.541983741
Rentals
Simulated Input Variables
Op. and Maint Real Estate
Costs
Taxes
24.12568965
4.407948001
37.78837352
9.945845905
28.26904922
11.88753795
71.63939623
-9.433329677
11.48758017
4.861958529
28.08821652
4.509186874
Key Results
NPV
WACC
0.09140001
0.020460275
0.16827927
0.015849846
Median
Probab. Of NPV>0
Coeff. Of Variation
57.32757725
94.63150337
373.916947
353.5382108
61.56795682
74.18%
1.650715204
Following the completion of 1069 experiments, we have deduced the following:
When the share of sales is equal to 35.81 million, the mean NPV is 57.33 million. When the
standard deviation in share of sales is 8.85 million, rentals are 4.41 million, operation and
maintenance costs are 11.89 million, real estate taxes are 4.86 million, and WACC is 2.05
percent, the standard variation in share of sales is 8.85 million. The NPV would then be 94.63
million dollars.
The maximum scenario depicts the best of the random probability situations. When the share
of sales is 67.61 million, the rentals are 37.79 million, the operating and maintenance costs are
71.64 million, and real estate taxes are 28.09 million, the NPV is a stunning 373.92 million,
with a WACC of 16.83 percent.
The worst-case scenario among the random probabilities is depicted in the minimal scenario.
When the share of sales is 8.541 million, the rentals are 9.945 million, the operating and
maintenance costs are -9.433 million, and real estate taxes are 28.08 million, the NPV is 353.53
million, with a WACC of 1.58 percent.
The median NPV is 61.56 units, which is 4.23 units higher than the mean NPV, indicating a
small but significant difference. The probability of NPV being larger than 0 has a 74.18 percent
chance of being true. This also means that the NPV has a 25.82 percent chance of being less
than zero, but as the NPV is more likely to be bigger than zero, this is good news for our
project.
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V.
Conclusion
Four types of analysis were performed for the project. These were cash flow analysis, simulation
analysis, scenario analysis and sensitivity analysis.
The cash flow analysis reveals that the NPV is positive, showing it as a good sign to accept the
project. The project will give good cash flows in the future also because the IRR was greater than
the WACC.
The sensitivity analysis reveals that share of sales, WACC and rentals are the most sensitive
variables and have the most impact on the NPV.
The scenario analysis reveals that for the base case scenario, the IRR is greater than the WACC.
In the best-case scenario, the NPV increases than base case scenario. Here also, the IRR is greater
than the WACC. In the worst-case scenario, the NPV turns negative and IRR is less than the
WACC.
In simulation variable we saw how target variables NPV is affected based on changes in input
variables. The model uses simulations to predict how the outcome of a decision would vary if we
tweak a set of input variables i.e., WACC, rentals, share of sales operating and maintenance cost
and taxes in a given range. We saw and analyzed the effects of constantly changing inputs have on
the outputs
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