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TISA June 2012ACCA P4

Mark Fielding-Pritchard

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Part A

Steps

Take of Elfu, take out Elfu gearing

Gives for new industry. Complication here is that there are combined industries. Assume all a weighted average

for project finance to get of project

Put in CAPM , get

Combine with to get WACC

That is your discount rate

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Elfu . Therefore 1.4 is a weighted average of the 2 divisions

1.4= (1.25 x 75%) + (components x 25%)

components = 1.86

= 1.86 (480/ (480 + (96 x 75%)) = 1.62

Now put in Tisa’s capital structure

1.62= (18000/18000+(3600x75%))= 1.86

= (1.86x 5.8%) + 3.5%= 14.3%

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WACC

=14.3% = 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 IRR10% 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

@13%

T0 (3800) (3800)

T1 1220 1760

T2 1153 1472

T3 1386 1566

T4 3829 3829

8673

Assume all inflows occur at the end of the project

Therefore 3800 k= 23%

13% is the WACC calculated in a)

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Part B Conclusion

IRR% MIRR%

Omega 25.3% 23%

Zeta 26.6 23.3

Zeta has a higher IRR so maybe choose this one, though difference is marginalConsider duration analysis as Omega has higher cashflows in early yearsMIRR is irreverent as the project has only 1 IRRI 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

-4 -3 -2 -1 0 1 2 3 40

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Chart Title

Calculating the probability of this being greater than 1%

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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 = $4168

Our risk is a function of volatility which is measures as variance. Standard deviation is the so we must take the as well