Article Review- Corporate Finance

This week deals with international business in emerging economies and risks associated with international corporate finance. To understand international business and risks of emerging economies, you will review these **two articles** and respond to the questions:

- You are required to combine the two articles in the article review.

Ferguson, M. (2011). Lessons on managing risk in emerging markets. *Journal of Accountancy*

McGowan, C. (2008). Evaluating the impact of foreign exchange rate risk on the capital budgeting for multinational firms. *International Business & Economic Research Journal,* 7(8), 47 – 58

*Address the following questions as you read the article:*

- What corporate finance problems are the articles addressing?
- What method of study (qualitative, quantitative, or mixed study) do the authors use to address the problem?
- What are the significant findings or ideas of the study?
- Summarize the main ideas of these articles.
- What are the strengths and limitations of the study?
- Make a proposal for future research on the topic that needs to be investigated.

International Business & Economics Research Journal –August 2008 Volume 7, Number 8

47

Evaluating the Impact Of Foreign Exchange

Rate Risk On The Capital Budgeting

For Multinational Firms Carl B. McGowan, Jr., Norfolk State University, USA

ABSTRACT

Capital budgeting analysis has evolved to the point where large firms universally use

sophisticated capital budgeting techniques. 1 However, small firms are less likely to use

sophisticated capital budgeting techniques. 2 Even large firms do not generally use simulation for

risk analysis in multinational project capital budgeting analysis. 3 This paper provides a

discussion and example of the use of simulation in evaluating the impact of foreign exchange rate

volatility on multinational project capital budgeting analysis.

Keywords: capital budgeting, foreign exchange risk, and simulation

INTRODUCTION

arragher, Kleiman, and Sahu (2001) discuss eight stages in the capital budgeting process. The first

three stages encompass finding appropriate projects for consideration: strategic analysis,

establishment of corporate goals, and searching for investment opportunities. The next three stages

involve the analysis of the project under consideration: forecasting cash flows, evaluating the projected cash flows,

and making the decisions to accept or to reject the project. The final two steps are implementing the decision and

post-auditing operating performance. In this paper, we deal primarily with the middle three stages of the mechanics

of project evaluation and selection.

FKS report that 55% of respondents perform quantitative risk analysis. Of this number, 95% use sensitivity

analysis and 79% use scenario analysis. However, only ten percent use simulation analysis. Graham and Harvey

(2001) report that 14% of respondents use simulation analysis. The use of simulation for risk analysis has not

increased significantly over the past 30 years. Klammer (1972) reports that 13% of respondents use simulation,

Klammer, Boch, and Wilner (1991) report that 12% of respondent use simulation, and Ho and Pike (1991) report

that 11% of respondents use simulation. Thus, the proportion of firms using simulation as a part of the capital

budgeting process has stayed level at just over ten percent while the use of sophisticated capital budgeting

evaluation techniques has increased substantially.

1 Bierman, Harold, Jr. “Capital Budgeting in 1991: A Survey,” Financial Management, Autumn 1993, pp. 21-29. 2 See, for example, Block, Stanley. “Integrating Traditional Capital Budgeting Concepts into an International Decision-Making

Environment,” The Engineering Economist, 45(4), 2000, pp. 309-325 or Graham, John R. and Campbell R. Harvey. “The

Theory and Practice of Corporate Finance: Evidence from the Field,” Journal of Financial Economics, 60, 2001, pp. 187-243. 3 See, for example, Farragher, Edward, Robert Kleiman, and Anandi, Sahu. “The Association Between the Use of Sophisticated

Capital Budgeting Practices and Corporate Performance,” The Engineering Economist, 46(4), 2001, pp. 300-31, Ho, Simon S. M.

and Richard H. Pike. “Risk Analysis in Capital Budgeting Contexts: Simple or Sophisticated?,” Accounting and Business

Research, 21(83), 1991, pp. 227-238, Klammer, T. “Empirical Evidence of the Adoption of Sophisticated Capital Budgeting

Techniques,” The Journal of Business, July 1972, pp. 387-397, and Klammer, T., B. Koch, and N. Wilner. “Post-auditing Capital

Assets and Firm Performance: An Empirical Investigation,” Managerial and Decisions Economics, (12), 1991, pp. 317-327.

F

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The first step in making a capital budgeting decision is to forecast future cash flows. The second step is to

evaluate the projected cash flows. The third step is to make the decision to accept or to reject the project. Projects

with positive net present value (NPV) are accepted and projects with negative NPV are rejected. Alternatively,

projects with an internal rate of return (IRR) that is greater than the cost of capital are accepted and projects with an

IRR less that the cost of capital are rejected.

The first stage in the capital budgeting project risk analysis process is to estimate the future cash flows of

the project. Each variable that affects the future cash flows is estimated with a probability distribution. Probability

distributions can range from a simple high, low, best guess estimate to complex distributions of various natures. 4

Each probability distribution is chosen to best reflect the decision maker’s prediction of the nature of the underlying

variable process.

Once all of the probability distributions are estimated for the input variables, the simulation is run. A

simulation is implemented by selecting a value for each variable and combining all of the values to compute an

NPV/IRR for the project. Two options are available for the random selection process, Monte Carlo selection and

Latin hypercube selection. Monte Carlo selection selects each value from the full probability distribution. Latin

hypercube uses stratified sampling, which restricts the number of observations from each part of the probability

distribution. This process is repeated as many times as practicable given the speed of the computer and the time

available. In fact, current technology allows for simulation of 100,000 simulations easily. The result is probability

distribution of outcomes – NPV/IRR.

This probability distribution of possible outcomes allows the decision maker to get a broad view of what

might happen to the capital budgeting project under consideration. The decision maker has the option to do

sensitivity analysis to determine which variables affect the outcome the most. That is, which variables affect the

decision to accept or to reject the project the most.

DOODAD COMPANY: A CAPITAL BUDGETING EXAMPLE

Doodad Company currently exports doodads to a low income country. To take advantage of incentives

provided by the host country government, and to avoid future political risk, Doodad has decided to begin

manufacturing in the host country (LIC). This project will be treated as a stand alone, new venture analysis.

The cost of building and equipping the manufacturing plant in LIC is $1,000,000 and will be depreciated

over the five year life of the project. Doodad uses straight line depreciation. Doodad believes that the risk level of

this project requires a 12.5% required rate of return. Sales volume in the first year (2000) will be 100,000 units and

demand will rise by 10% each year. The initial price of a unit will be 12 FC and will rise by 15% each year.

Variable cost per unit will begin at 6 FC and rise by 7.5% per year.

Doodad will repatriate all earnings after taxes as dividends which are subject to a 10% withholding tax. In

addition, Doodad will repatriate the depreciation. To simplify the exposition, US taxes are assumed to be the same

as the tax credit for taxes paid in LIC, so no US tax is due.

We construct a table of cash flows for the project and compute the net present value and internal rate of

return for Doodad. Table 1 provides the solution to the capital budgeting example for Scenario One. For scenario

one, all of the input variables are assumed to be deterministic, that is, know with certainty. The first three rows

show the value of the three input variables: sales volume, sales price, variable cost per unit and the expected future

spot rate. The level of unit sales volume begins at 100,000 units in year 2000 and grows by ten percent each year to

end at 146410 units. The beginning selling price is $12 and grows by 15% each year to end at $20.99 per unit.

Variable cost per unit begins at $6 and grows at 7.5 percent each year to end at $8.00 per unit. The fourth variable is

the expected future spot rate which begins at 2.00 foreign currency per dollar, is 2.15 foreign currency per dollar, at

4 In this paper, we use the simulation analysis package @RISK published by Palisades Corporation which includes over thirty

different probability distributions.

International Business & Economics Research Journal –August 2008 Volume 7, Number 8

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the end of 2000, and grows by 7.5 percent each year to end at 2.87 foreign currency per dollar. The IRR for this

scenario is 18.9 percent and the NPV for this scenario is $182,704.

Table 2 provides a solution to the same capital budgeting example but with Sales volume starting at

120,000 units. 5 With increased sales volume, the IRR increases to 25.14% and the NPV increases to $375,881. The

financial decision maker can change variable inputs to determine the sensitivity of IRR to changes in each input

variable. Scenario analysis allows the decision maker to determine which input variable has the most significant

impact of IRR. The capital budgeting project can be restructured to mitigate the effect of those input variables

where only a small adverse change in the input variable changes the IRR decision.

A significant scenario level for each input variable is the level at which the IRR is equal to the required rate

of return, 12.5%. The NPV is zero at this point. For sales volume, the zero NPV level is 81,804 units. For sales

price, this level is $10.72 per unit. For unit variable cost, this level is $7.47 pre unit. For the foreign exchange rate

variable, the break-even, starting level is $2.54. The break-even level for the cost of the project is $1,182,704.

Figures 1-4 show the probability distributions assumed for each of the input variables. 6 Sale volume is

assumed to be a triangular distribution with a minimum value of 95,000 units and a maximum value of 105,000

units. Sales price is assumed to be a histogram distribution with values between $11 and $13. The bottom and top

one-third each have a probability of 20% and the middle one-third has a probability of 60%. The growth rate of the

expected future foreign exchange rate is assumed to normally be distributed with a growth rate of 7.5% per with a

standard deviation of 1%. The exchange rate at time zero is assumed to be 2.00 foreign currency per dollar.

Figures 5-8 show the actual probability distributions for the input variables used in the simulation. Figures

9-10 show the actual probability distributions for the IRR and the NPV used in the simulation. Table 3 shows the

statistics generated by the simulation.

Unit volume has a mean of 100,000 units with a maximum of 104,991 units and a minimum of 95,018

units. Unit selling price has a mean of 12, a minimum of 11 and a maximum of 13. Unit cost has a mean value of 7

with a minimum of 5 and a maximum of 7. The foreign exchange rate has a mean of 2.15 foreign currency per

dollar with a minimum of 2.06 foreign currency per dollar and a maximum of 2.24 foreign currency per dollar.

The NPV for the project is $182,811 with a maximum of $514,545 and a minimum of -$122,330. The IRR

has a mean of 18.84% with a minimum of 7.93 percent and a maximum of 29.57. The probability of a positive NPV

is greater than 95%.

At this point, the decision maker can determine the critical variables which have the greatest impact of the

decision to accept or to reject the project. Managerial time, which is a limited resource, can be used where the time

will have the most impact, those variables whose volatilities have the most influence on the outcome. It is an easy

matter for the decision maker to develop various scenarios for the input variables or the probability distributions for

the input variables. This type of sensitivity analysis allows the decision maker to evaluate the impact of each input

variable on the possible outcome.

AUTHOR INFORMATION

Carl B. McGowan, Jr., PhD, CFA is a Professor of Finance at the School of Business at Norfolk State University.

Dr. McGowan received a BA in International Relations and an ROTC commission from Syracuse University, an

MBA (Finance) from Eastern Michigan University, and a PhD in Business Administration (Finance) from Michigan

State University. From 2003 to 2004, he was the RHB Bank Distinguished Chair in Finance at the Universiti

5 Scenario analysis is single iteration simulation. One variable is changed and the outcome is recomputed. The impact of each

variable can be determined for significant points such as the zero NPV point. 6 The probability distributions are chosen to show the variety of distributions available. @RISK provides thirty different

probability distributions.

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Kebangsaan Malaysia. He has taught in Cost Rica, Malaysia, Moscow, Saudi Arabia, and The UAE. His special

area of interest is international risk analysis and foreign direct investment analysis analyzing the interaction between

political and economic risk and FDI. Professor McGowan has published over one hundred papers and presented

over one hundred and forty papers at conferences. Professor McGowan published in numerous journals including

Applied Financial Economics, Decision Science, Financial Practice and Education, The Financial Review, The

Journal of Applied Business Research, The Journal of Diversity Management, The Journal of Global Business, The

Journal of Real Estate Research, Managerial Finance, The Southwestern Economic Review, and Urban Studies.

BIBLIOGRAPHY

1. Bierman, Harold, Jr. “Capital Budgeting in 1991: A Survey,” Financial Management, Autumn 1993, pp. 21-29.

2. Block, Stanley. “Integrating Traditional Capital Budgeting Concepts into an International Decision-Making Environment,” The Engineering Economist, 45(4), 2000, pp. 309-325.

3. Farragher, Edward, Robert Kleiman, and Anandi, Sahu. “The Association Between the Use of Sophisticated Capital Budgeting Practices and Corporate Performance,” The Engineering Economist, 46(4),

2001, pp. 300-311.

4. Graham, John R. and Campbell R. Harvey. “The Theory and Practice of Corporate Finance: Evidence from the Field,” Journal of Financial Economics, 60, 2001, pp. 187-243.

5. Ho, Simon S. M. and Richard H. Pike. “Risk Analysis in Capital Budgeting Contexts: Simple or Sophisticated?,” Accounting and Business Research, 21(83), 1991, pp. 227-238.

6. Klammer, T. “Empirical Evidence of the Adoption of Sophisticated Capital Budgeting Techniques,” The Journal of Business, July 1972, pp. 387-397.

7. Klammer, T., B. Koch, and N. Wilner. “Post-auditing Capital Assets and Firm Performance: An Empirical Investigation,” Managerial and Decisions Economics, (12), 1991, pp. 317-327.

8. Pike, Richard H. “An Empirical Study of the Adoption of Sophisticated Capital Budgeting Practices and Decision-Making Effectiveness,” Accounting and Business Research, 18(72), 1988, pp. 341-351.

9. Reichert, Alan K., James S. Moore, and Ezra Byler. “Financial Analysis among Large US Corporations: Recent Trends and the Impact of the Personal Computer,” Journal of Business, Finance, and Accounting,

15(4), Winter 1988, pp. 469-485.

International Business & Economics Research Journal –August 2008 Volume 7, Number 8

51

Figure 1

Probability Distribution – Sales Volume

Figure 2

Probability Distribution – Selling Price

Triang(95000, 100000, 105000) X <= 103419

95.0%

X <= 96581

5.0%

0

0.5

1

1.5

2

2.5

94 96 98 100 102 104 106

Values in Thousands

V a

lu e s

x 1

0 ^ -4

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

Histogrm(11, 13, {p}) X <= 11.1667

5.0%

X <= 12.8333

95.0%

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

10.5 11 11.5 12 12.5 13 13.5

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

International Business & Economics Research Journal –August 2008 Volume 7, Number 8

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Figure 3

Probability Distribution – Unit Cost

Figure 4

Probability Distribution – Foreign Exchange Rate Change

Discrete({x}, {p}) X <= 5.0000

5.0%

X <= 7.0000

95.0%

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

4.5 5 5.5 6 6.5 7 7.5

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

Normal(0.075, 0.01) X <= 0.058552

5.0%

X <= 0.091448

95.0%

0

5

10

15

20

25

30

35

40

45

0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

@RISK Student Version For Academic Use Only

International Business & Economics Research Journal –August 2008 Volume 7, Number 8

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Figure 5

Output Distribution – Unit Volume

Figure 6

Output Distribution – Selling Price

International Business & Economics Research Journal –August 2008 Volume 7, Number 8

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Figure 7

Output Distribution – Unit Cost

Figure 8

Output Distribution – Foreign Exchange Rate

International Business & Economics Research Journal –August 2008 Volume 7, Number 8

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Figure 9

Output Distribution – Net Present Value

Figure 10

Output Distribution – Internal Rate of Return

International Business & Economics Research Journal –August 2008 Volume 7, Number 8

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Table 1

NPV Computation

Scenario One

Year 2008 2009 2010 2011 2012

Volume 100000 110000 121000 133100 146410

Price 12.00 13.80 15.87 18.25 20.99

Variable cost per unit 6.00 6.45 6.93 7.45 8.01

Revenue 1200000 1518000 1920270 2429142 3072864

Variable costs 600000 709500 838984 992098 1173156

Depreciation 400000 400000 400000 400000 400000

EBT 200000 408500 681286 1037043 1499708

Taxes (30%) 60000 122550 204386 311113 449912

EAT 140000 285950 476900 725930 1049795

Dividend payment 140000 285950 476900 725930 1049795

Taxes 14000 28595 47690 72593 104980

Net 126000 257355 429210 653337 944816

FOREX rate 2.15 2.31 2.48 2.67 2.87

Depreciation ($) 186047 173067 160992 149760 139312

Dividend ($) 58605 111349 172749 244610 329060

Total ($) -1000000 244651 284415 333741 394370 468372

PV ($) 1182704

Cost ($) 1000000

NPV 182704

IRR 18.90%

International Business & Economics Research Journal –August 2008 Volume 7, Number 8

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Table 2

NPV Computation

Scenario One

Year 2008 2009 2010 2011 2012

Volume 120000 132000 145200 159720 175692

Price 12.00 13.80 15.87 18.25 20.99

Variable cost per unit 6.00 6.45 6.93 7.45 8.01

Revenue 1440000 1821600 2304324 2914970 3687437

Variable costs 720000 851400 1006781 1190518 1407787

Depreciation 400000 400000 400000 400000 400000

EBT 320000 570200 897544 1324452 1879649

Taxes (30%) 96000 171060 269263 397336 563895

EAT 224000 399140 628280 927116 1315755

Dividend payment 224000 399140 628280 927116 1315755

Taxes 22400 39914 62828 92712 131575

Net 201600 359226 565452 834405 1184179

FOREX rate 2.15 2.31 2.48 2.67 2.87

Depreciation ($) 186047 173067 160992 149760 139312

Dividend ($) 93767 155425 227583 312401 412425

Total ($) -1000000 279814 328492 388576 462161 551737

PV ($) 1375881

Cost ($) 1000000

NPV 375881

IRR 25.14%

International Business & Economics Research Journal –August 2008 Volume 7, Number 8

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Table 3 Output Statistics

Outputs Volume Price Cost FX rate NPV / 2000 IRR / 2000

Minimum 95018 11.00 5.00 2.06 -122330 0.0793

Maximum 104991 13.00 7.00 2.24 514545 0.2957

Mean 100000 12.00 6.00 2.15 182811 0.1884

Standard Deviation 2041 0.46 0.63 0.02 105266 0.0358

Variance 4166707 0.21 0.40 0.00 1.11E+10 0.00

Skewness 0.00 0.00 0.00 0.00 0.02 -0.07

Kurtosis 2.40 2.62 2.50 3.00 2.79 2.81

Number of Errors 0 0 0 0 0 0

Mode 96030 11.29 6 2.10 -39537 0.1847

5% 96581 11.17 5 2.12 13327 0.1298

10% 97236 11.33 5 2.12 46295 0.1416

15% 97739 11.50 5 2.13 68073 0.1493

20% 98162 11.67 6 2.13 89273 0.1568

25% 98535 11.72 6 2.14 110876 0.1643

30% 98873 11.78 6 2.14 130148 0.1710

35% 99183 11.83 6 2.14 146014 0.1765

40% 99472 11.89 6 2.14 158994 0.1809

45% 99743 11.94 6 2.15 171012 0.1850

50% 100000 12.00 6 2.15 182432 0.1889

55% 100257 12.06 6 2.15 194259 0.1929

60% 100528 12.11 6 2.16 206160 0.1969

65% 100817 12.17 6 2.16 219424 0.2013

70% 101127 12.22 6 2.16 235059 0.2065

75% 101464 12.28 6 2.16 253990 0.2128

80% 101838 12.33 7 2.17 275030 0.2198

85% 102261 12.50 7 2.17 296645 0.2269

90% 102764 12.67 7 2.18 320516 0.2346

95% 103419 12.83 7 2.18 355048 0.2459