TISA June 2012
ACCA P4
Mark Fielding-Pritchard
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1
Part A

Steps

Take β

Gives β 𝑎 for new industry. Complication here is that there are
combined industries. Assume all a weighted average

Regear β

Put β

Combine with K

That is your discount rate
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𝑒
𝑒
of Elfu, take out Elfu gearing
𝑎
for project finance to get β
in CAPM , get K
𝑑
𝑒
of project
𝑒
to get WACC
2
Elfu

β 𝑒 is a weighted average . Therefore 1.4 is a weighted average of
the 2 divisions

1.4= (1.25 x 75%) + (β 𝑒 components x 25%)

β

β 𝑎 = 1.86 (480/ (480 + (96 x 75%)) = 1.62

Now put in Tisa’s capital structure

1.62= β

K
𝑒
components = 1.86
𝑒
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𝑒
(18000/18000+(3600x75%))= 1.86
= (1.86x 5.8%) + 3.5%= 14.3%
3
WACC

K
𝑒
=14.3%

K
𝑑
= 4.5% post tax

WACC/ Discount rate= (14.3 x 18/18+3.6) + 4.5
(3.6/18+3.6) = 12.7 (use 13 in exam)
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Part B IRR
10%
20%
0
(3800)
(3800)
(3800)
1
1220
1109
1016
2
1153
952
800
3
1386
1041
802
4
3829
2615
1846
1917
664
IRR = 10% + ((1917/1917-664)) = 25.3% (27% from excel)
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Part B MIRR
Assume all inflows occur at the end of the project
T0
T1
T2
T3
T4
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(3800)
1220
1153
1386
3829
13% is the
WACC
calculated in a)
@13%
(3800)
(1 + 0.13)3
(1 + 0.13)2
(1 + 0.13)1
1760
1472
1566
(1 + 0.13)0
3829
8673
Therefore 3800(1 + 𝑘)4 = 8673 k= 23%
6
Part B Conclusion
Omega
Zeta
IRR%
25.3%
MIRR%
23%
26.6
23.3
Zeta has a higher IRR so maybe choose this one, though
difference is marginal
Consider duration analysis as Omega has higher
cashflows in early years
MIRR is irreverent as the project has only 1 IRR
I recommend Omega
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Value at Risk

VaR was developed on trading desks on Wall St. Our fear is that the market crashes
20+% in on day. We know this will happen 1 day every 3 years

Our aim is to maximise the risk on the 749 days when this doesn’t happen and minimise
it on the 1 day it does

This is done by stress testing portfolios and VaR is 1 technique for highlighting potential
problems

Trading desks do this after the close of business every day so we assume no trading and
normal distributions

We set a maximum permitted allowable daily loss and then statistically calculate the
probability of exceeding that loss

The problem is that market crashes occur so infrequently they will fall outside the norm
so VaR will specifically exclude them, &

Data at the <1% end of the probability tail will probably not behave rationally,&

Models may have been designed where we link to one stock, say Apple, the probability
that Apple falls 20% is immaterial and the system does not aggregate. The sum of
individual risks may be greater than the whole
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TISA
Chart Title
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
-4
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-3
-2
-1
0
1
2
3
4
Calculating the probability of this being greater than
1%
9
Tisa

Look at stats tables, we need 0.49 from the body of the table

On the side we get this at 2.33. Therefore 2.33 standard deviations will
give us 99% confidence

2.33 x 800000 = 1864

Therefore we set our VaR at 1864

In our example it tells us in principle that we are 99% confident that
in 1 time period losses will not exceed 1864

Over a 5 year period we get 1864 x 5 = $4168

Our risk is a function of volatility which is measures as variance.
Standard deviation is the 𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 so we must take the 𝑡𝑖𝑚𝑒 as well
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