How to Use Simulation to Evaluate a Interter Company

8/6/2019 How to Use Simulation to Evaluate a Interter Company

1/27

Otto-von-Guericke-University Magdeburg

Faculty of Management and Economics

Chair for Banking and Finance

Prof. Dr. Peter Reichling

A case study: Evaluate eBay.com with

real auction method and simulation

technology

SEMINAR PAPER

COURSE OF STUDY: VALUATION OF INTERNET FIRMS

NAME: ZHANG, BAODONG

Address: Moldern Str 4,

Zi.234, Magdeburg 39106

Student register number: 158819

1


2/27

1 INTRODUCTION ...................................................................................................................................3

2. CONTINUOUS-TIME MODEL AND DISCRETE VERSION OF THE MODEL ........................4

2.1 CONTINUOUS-TIMEMODEL...................................................................................................................... 4

2.2DISCRETE VERSIONOFTHE MODEL.......................................................................................................... 9

3.COMPANY BACK GROUND AND PARAMETERS ESTIMATION ............................................10

3.1 BACKGROUNDOFINTERNETCOMPANYEBAY............................................................................................10

3.2ESTIMATETHEPARAMETERS................................................................................................................... 13

4. RESULT OF SIMULATION ...............................................................................................................16

5. SENSITIVITY ANALYSIS .................................................................................................................20

6. CONCLUSION .....................................................................................................................................23

BIBLIOGRAPHY .....................................................................................................................................26

2


3/27

A case study: Evaluate eBay.com with real auction

method and simulation technology

1 Introduction

Evaluating Internet stock maybe the most appealing investment topic at

present. Due to the especial character of internet industry-- uncertainty

and shortness of history data, many traditional valuation approaches do

not work well or even seem to be reversed in this industry. Dotcom

companies continuously get high market value even with negative cash

flow.

From some traditional analysts point of view, the Internet stocks

have been bid up irrationally, the frenzy in the market is a spectacular

example of market bubble. Whereas other investors believe that with the

application of high technology Internet firm will dramatically transform

the way in which the business is transacted and will rapidly grow to

dominate their traditional competitors.

New methods and adjustment of traditional methods have been

adopted to justify the relative high market value of Internet company.

Real option is one of these methods. Eduardo Schwartz and Mark Moon

applied real options theory and capital budgeting techniques to the

3


4/27


5/27

the development of the company value2. An important determinant of

companys value is sales growth. In the simulation, different sales

growths induce to different cash flows. Under the consideration of costs,

loss carry forward, and tax, for each period net cash flow is obtained.

These cashes available generate compound risk free interest during the

time process. The firm value is present value of total cash available in

the end horizon of simulation.

In the model the sales growth is depicted with stochastic process in

which the growth rate evolves along its expected value and at the same

time it is subjected to volatility.

Another stochastic process is the expected sales growth rate. Since the

initial very high-expected growth cannot sustain for a long time, in the

long run it converges to a more reasonable or moderate level.

Assuming an Internet firm has an instantaneous revenue at time t, tR , the

following stochastic differential equation is used to depict the dynamic

of these revenue.

1dzdtR

dRtt

t

t += (1)

Where the drift, t, is the expected growth rate of revenue. t is the

volatility of in this growth rate. 1z is a random variable and subjects to

standard normal distribution if time increment is 1.

2 See Bhmer, C.(2001) p.10

5


6/27

The dynamic of the expected growth rate in revenue t is:

2)( dzdtkd ttt += (2)

In which the expected revenue grow rate t stochastically converges to

its long term more reasonable and sustainable rate of growth, . k

describes the speed of this process. 0 is the initial volatility of this

expected growth rate in revenue. 2z is a second random variable reflects

the draw from a normal distribution if time increment is 1.

The volatility in the rate of revenue growth, t, and the volatility of the

expected growth rate in revenues, t, also evolve during time. t

converges to a more normal level and t converges to 0.

dtkd tt )(1 = (3)

dtkd tt 2= (4)

The mean reversion coefficient 1k , 2k describe the speed these variables

converge to their means. The unanticipated changes in revenue growth

rate and the unanticipated changes in its drift are assumed correlated:

dtdzdz =21 (5)

To calculate the net cash flow generated in each period, the cost of the

revenue need to be considered. It is made up of two components: cost of

goods sold (COGS) and other expenses. The first item is assumed to be

proportional to the revenue and the second item has a fixed component,

F , and a variable which is also proportional to the total revenue.

6


7/27

FR

RFR

ensesOtherCOGSCost

t

tt

ttt

++=++=

+=

)(

)(

exp

(6)

Where is the COGS as a percentage of revenue and is the

percentage of other expenses relative to the total revenue.

The earning before interests, taxes, depreciation, and amortization

(EBITDA) at period t, tE is:

ttt CostRE = (7)

If the sum of EBITDA in period t and the loss carry forward in the same

period, tL , is positive, it subjected to tax. Otherwise the tax ratio is zero.

Therefore the after tax net cash flow at time t, tY , is given by:

)1)(( += ttt LEY , if 0tt LE + (8a)

or

)(tttLEY += , otherwise (8b)

If the cash available at time t is tX . The dynamic of this number is

given by:

dtYdX tt = (10)

For the simplicity case, the bankruptcy is defined as the situation where

the company runs out of cash, tX reaches 0. The definition of

bankruptcy neglects the fact that the company can raise cash, sell equity

or merge with other company when it run out of cash.

The objective of the model is to determine the value of firm at current

7


8/27

time, 0V . Again for the simplicity reason, it is assumed that the cash

generated in the operation remains in the company, earns a risk free rate

of interest and will not be distributed to shareholder until in the end

period of simulation. According to these assumptions, the company

value equals to the expected value of the firm in the end of simulation

discounted under risk neutral measure at the risk free rate.

At the end period of simulation, T, the firm value has two

components. One is the accumulated cash available TX , another is the

continuing value of the company at time T. The continuing value is

calculated as a multiple M of the EBITDA at time T, TE . The multiple

employed by Schwartz and Moon is 10, which is an average value for

mature company and is also industry independent. Following this

process each simulation path will determine one possible firm value.

[ ] rTtttQt eXCRMEV

+= *)( (11)

where rTe is the continuously compounded discount factor.

Considering the uncertainties in the model, uncertainty of changes in

sales and uncertainty of growth rate in sales, Schwartz and Moon

obtained the risk-adjusted equations for the variables stated in equation

(1) and (2):

+= 11 )( dzdtR

dRttt

t

t (12)

( )[ ]

+= 22 dzdtkd tttt (13)

8


9/27

The asterisk stands for the risk adjustment of the process. 1, 2 are

market price for the factor risk. In this eBay case, the values of these

parameters are assumed to be 0.123 and 0.025 which0.025, which were

used by Bhmer3 for the valuation of Amazon.

2.2Discrete Version of the Model

To imply Monte Carlo simulation to solve the value of Internet firm, the

discrete version of the risk-adjusted process is depicted in Schwartz and

Moons article:

[ ] }2/*{ 121 tt

ttt

tttt

eRR+

+= (17)

( ) 22

2

211 t

k

e

kee

t

tk

ttk

t

tk

tt +

+=

+ (18)

Where

( )tktkt

ee 11 10

+= (19)

and

tk

te 20

= (20)

Considering 1 and 2 are random variables drown from standard

normal distribution and possible to be negative, in the simulation of

eBay case, they were set out side of the squared root.

If losses generated in the operation are indicated by negative number, for

3 See Bhmer, C.(2001)

9


10/27

example, loss carry forward may either be negative or zero and can

never be positive, the discrete change in total cash available at time t,

tX can be depicted as:

{ } { }ttttttLLELEX +++= 0,min)1(0,max (21)

The last two term in equation 21 indicates the change in the carry

forward loss in current period t, L ,:

{ }tttLLEL += 0,min (22)

Before estimating the parameter and carry out simulation, it is necessary

to get some basic knowledge about eBay.

3.Company back ground and parameters estimation

3.1 Background of internet company eBay

Ebay is known as the king of online auction houses today.

It was founded in September 1995 and was incorporated in may 1996. In

early 1998, it has only about 30 employees and 300000 users who

generated less than $200 million annualized merchandise sales,

primarily in collectible categories. Just four years later, in 2001, gross

10


11/27

merchandise sales have grown by 72% to more than $9.3 billion. Its own

net revenue grew from 41.37 million to 748.821 million. It evolves to a

market place where businesses and individuals buy and sell collectibles,

automobiles, high-end or premium art items, jewelry, consumer

electronic and a host of practical and miscellaneous items. More than

$30 million goods are traded on this marketplace everyday.

In September 1998 eBay went to public. On the first day of IPO, eBays

stock soared to three times the offer price.4 At present (Dec. 23rd 2002)

its share price grew to $70.1 from $7.896 four years ago.5 Figure 1

shows the development of eBays share price. And the quarterly sales,

COGS, expenses and other basic financial data for the past four-quarter

from Q4 2001 to 3Q 2002 are given by table 1.

4 Business: Not quite another eBay, The Economist, Mar 10, 20015http://quotes.nasdaq.com/quote.dll

11
http://quotes.nasdaq.com/quote.dllhttp://quotes.nasdaq.com/quote.dll


12/27

Figure 1: eBay share price, Oct 1998Dec.20026

Table1: Quarterly sales and Costs for eBay, Dec. 2001- Sep. 20027

Quarter Ending 9/30/2002 6/30/2002 3/31/2002 12/31/2001Total revenue 288.779 266.287 245.106 219.401Cost of Revenue 45.374 44.561 41.277 39.989

Gross profit 243.405 221.726 203.829 179.412Operating Expenses:

R&D 24163 24346 24307 21723

Sales, General &Admin. 127.886 116.831 107.276 103.681

Other Operating Items 1239 1108 1530 12390

sum of operat. cost 25529.886 25570.831 25944.276 34216.681

EBITDA 115.519 104.895 96.553 75.731

6http://quotes.nasdaq.com/quote.dll

7 www.nasdaq.com

12
http://quotes.nasdaq.com/quote.dllhttp://quotes.nasdaq.com/quote.dllhttp://quotes.nasdaq.com/quote.dll


13/27

3.2Estimate the parameters

The simulation is performed with the information available on the date

of 30th Dec. 2002. To carry out the simulation, more than 20 parameters

are need.

Among these parameters, the initial revenue, initial loss carry forward

and initial cash balance can he obtained directly from eBays income

statement and balance sheet for the third quarter of 2002. But some

others cannot be observed and have to be estimated. Estimating these

parameters is a challenging issue because it needs thorough knowledge

about the industry and the particular company. Figure 2 shows the sales

growth rapidly during the past 11 quarters from Q1 2000 to Q3 2002.

Figure 3 shows that the growth rate in revenue for the same period

which were relative high in the beginning and then declined.

13

Figure2: eBay quarterly Sales, Q1 2000--Q3 2002

050000

100000

150000

200000

250000

300000

350000

Q1

2000

Q2

2000

Q3

2000

Q4

2000

Q1

2001

Q2

2001

Q3

2001

Q4

2001

Q1

2002

Q2

2002

Q3

2002

Sales


14/27

The initial expected growth rate in revenue, 0 , is calculated by taking

the average growth rate over the past 7 quarters (2001,1st 2002,3rd). The

initial volatility of growth rate in revenue, 0 , is the standard deviation

of the same period. According to the article of Schwartz and Moon

(2000), the initial volatility of expected growth rate in revenue can be

referred from the stock price volatility. In this context, the parameters

estimated by the authors for Amazon is used as a benchmark to

determine that of eBay. The share price volatility of Amazon is

compared with that of eBay during the period of 1999-2000, and eBays

initial volatility of expected sales growth rate is obtained by:

0 (eBay)= 0 (Amaz.) * (eBay) / (Amaz.) =0.03*17.353861/19.191511=0.027127

(23)

where is the standard deviation of the share price. For simplicity, it is

assumed that the unanticipated changes in growth rate and expected

growth rate in revenue do not correlated. It is also assumed that the long-

14

Figure3: eBay Quartely Sales Growth Rate Q2 2000-Q3 2003

00.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

Q2

2000

Q3

2000

Q420

00

Q1

2001

Q2

2001

Q3

2001

Q4

2001

Q1

2002

Q2

2002

Q3

2002

Sales Growth Rate


15/27

term growth rate in revenue is 1.5 percent per quarter (6 percent per

year). The long-term volatility in the growth rate of revenue is 1 percent

per quarter (2 percent per year). Corporate tax is 0.35. The yield to

maturity rate of American treasury strip bond serves as risk free rate. The

rate of the bond, which matures in February 2025, is 0.015 per quarter.

The three speed-of-adjustment mean reversion coefficient, k , 1k , 2k ,

are assumed to be the same as that of Amazon.

Figure 4 shows the relationship between COGS and Sales. Figure 5

shows the relationship between selling, general, and administrative

expenses (SG&A) and sales.

20000

25000

30000

35000

40000

45000

50000

COGS($000)

50000 100000 150000 200000 250000300000

Sales($000)

100

110

120

130

SG&A($000)

200 225 250 275 300

Sales($000)

Figure 4: eBay COGS verse Sales Figure 5: eBay SG&A verse Sales

Due to the business character of eBay, COGS does not occupy an importantplace in the total sales. In the contrary, SG&A has a much bigger potential in

the total sales. The percentage of COGS in sales, , is estimated as 0.19.

Variable component of operating cost, , is 0.45. The fixed cost

parameter F is assumed to be 39901($000). Finally, one quarter stands

for one time increment and the horizon for estimation is 24.75 years. The

15


16/27

terminal value for eBay in the end of 24.75 year is assumed to be 10

times of EBIDA for the end period.

Table 2 shows these parameters and their estimations for the

implementation.

Table2: Parameters used in valuation of eBay

Parameter (quarterly) Notation Estimation

Initial Revenue ($000) R 0 228779Initial loss carry-forward ($000) L0 0

Initial cash balance ($000) X 0 748,623Initial expected rate of growth in revenues 0 0.09

Initial volatility of growth rate in revenues 0 0.042285

Initial volatility of expected rate of growth inrevenue

0 0.027127

Correlation between percentage change inrevenues and change in expected rate of growth

0

Long-term rate of growth in revenue 0.015Long-term volatility of the rate of growth in revenue. 0.01Companys corporate tax rate 0.35

Risk-free interest rate r 0.015Speed of adjustment for the rate of growth process k 0.07Speed of adjustment for the volatility of revenueProcess

k1 0.07

Speed of adjustment for the volatility of the rateof growth process

k2 0.07

COGS as a percentage of revenues 0.19Fixed component of other expenses ($000) F 27701.5Variable component of other expenses 0.45Market price of risk for the revenue factor 1 0.123

Market price of risk for the expectedRate of growth in revenues factor1

0.025

Horizon for the estimation T years 24.75Time increment for the discrete version of the model quarters 1

4. Result of simulation

For estimating the basic value of eBay, the discrete-time formulas

16


17/27

depicted in section 2 are used to build an Excel file. The parameters in

table 2 are substituted into this file. For every round of the simulation,

the total cash available for eBay in the end of 24.75 year is calculated

and discounted with risk free interest by computer. Totally the

simulations are carried out for 900 times. Although 900 times of

simulation is not a small number which needs an Excel file with 90000

rows, the times of simulation is still relatively low compared with that of

Schwartz and Moon (2000) where 100000 times were performed.

Therefore the results illustrated below may not present or even distort

the findings if the simulations are performed in large numbers.

The mean of the 900 simulations suggested that the value of eBay is

39.7079 billion. The possibility of bankruptcy is 1 out of 900. The total

shares of common stock of eBay in the end of 2001 is 288.166 million

which is calculated according to eBays 2001 financial annual report.8

The market price on 23 Dec. 2002 is $70.1 per share. The simulation

suggested that eBay is undervalued at present.

The following figure 6 shows the distribution of the eBays simulated

sale paths.

8

17


18/27

Figure 6: Quartiles of 900 simulated paths of the sales

development for eBay( in $000)

0

2000000

4000000

6000000

8000000

10000000

Year 1 Year 5 Year 10 Year 15 Year 20 Year

24.74

90.0%

75.0%

50.0%

25.0%

10.0%

The vary top two lines contain 15% of sales paths with higher outcomes

and are very wide spread. The lowest two lines which contain 15% of

the sales pathes with lower outcomeslines that contain 15% of the sales

pathes with lower outcomes are very close to each other. According to

simulated paths, the quarter sales after 24 years range from $538 million

to $9 billion. Contrary to this wide spread, in the end of first year,

quarter sales range from $274 million to $375million. It can be shown

that in the later period extreme high sales are possible but not very

likely. It should be mentioned that due to the relatively low number of

simulations carried out in this example case, the realization of extreme

high sales might be putted to relative high weight compared to that of a

large number of simulation.

18


19/27

Contrary to the sales paths, the distribution of expected growth rate in

sales spread wider in the initial period and narrower in the later period.

Figure7 show the distribution of expected growth rate in revenue in 1

year, 5 year, 10 year and 24.75 year. After 1 year, the growth rates range

from 0.0543 to 0.1804. 24 years later, they range from 0.01411 to

0.01594. As time passes the sale rate converges to its long-term rate

0.015.

1year 5Year

0,05

0,10

0,15

ProbabilityAxis

0 0,1

0,05

0,10

0,15

ProbabilityAxis

0 0,1

Fitted Normal(0,07018,0,0413) Fitted Normal(0,02931,0,03246)

10year 24.75 year

0,05

0,10

0,15

0,20

Proba

bilityAxis

-0,02 0 0 ,01 0 ,02 0 ,03 0 ,04 0 ,05

0,050,10

0,15

0,20

Proba

bilityAxis

0,015 0,016

Fitted Normal (0,01782,0,01111) Fitted Normal(0,015,0,00028)

Figure 7:Expected growth rate in sales distribution for eBay after 1 year, 5,year, 10 year and 24.75year.

19


20/27

Figure 8 show the distribution of companys value resulting from 900

simulations and the frequency of each outcome. The distribution is

obviously skewed to the left side and shows that extremely high value of

the firm is possible but has low possibility. The mean of the simulated

companys value is 39.7079 billion and lies in the left side of the figure.

Figure8: Distribution of eBay's firm value resultingfrom 900 simulations(in $000)

020406080

100120140

50000

00

250000

00

450000

00

650000

00

850000

00

1050000

00

1250000

00

1450000

00

1650000

00

1850000

00

2050000

00

2250000

00

2450000

00

2650000

00

2850000

00

3050000

00

3250000

00

3450000

00

Frequency

Hufigkeit

5. Sensitivity analysis

In table 3, the sensitivity of eBays value to the critical parameters has

been showed.

Table3:Sensitivity of eBay's value relative to changed parameters

20


21/27

Parameter Value of perturbedparameter

Total eBay value($000)

Sensitivity Probability ofBankruptcy

Base case 39707915.7 1/900Miu0 0.099/quarter 45360719.05 1.423596 0

Sigma0 0.0465135 39508704.82 -0.05017 0

E(miu) 0.0165 43599332.55 0.98001 0

E(sigma)) 0.011 39377109.95 -0.08331 0

k 0.077 35636836.3 -1.02526 0

k1 0.077 39890884.67 0.046079 0

k2 0.077 38391322.68 -0.33157 0

Alfa 0.2 38562445.2 -0.28847 0

Beta 0.46 38562445.2 -0.28847 0

Aita 0.029839 43861390.85 1.046007 3/900

Fixed cost 43891.1 38373993 -0.335934 2/900

These numbers were obtained when 10 percent higher value of theindicated parameter(except for and ) was substituted into the

equations while the other parameters are the same as the base valuation.

It can be seen that several parameters in the table have a significant

effect on the value of the firm. Two of them are initial expected growth

rate in sales and the long-term growth rate, which has sensitivity 1.42

and 0.98 respectively. The second high sensitive parameter is 0 , the

initial unanticipated change in the expected revenue growth rate, which

has a sensitivity of 1.04. Comparing with the parameters found by

Schwartz and Moon for Amazon, the most critical factor for the value of

21


22/27

eBay is the initial expected growth rate in revenue rather than the

variable component of the cost function, and . The reason behind

that maybe is the business characteristic of eBay. As mentioned above

COGS does not occupy an important potential in the total sales. And the

potential of SG&A relative to sales is higher than Amazon but the profit

margin is much greater than that of Amazon. Therefore one percent

increases in and do not affect the firm value very much.

As reported by Schwartz and Moon, the parameters for the stochastic

process of changes in the revenue growth rate, 0 and k , affect

Amazons value very significantly. This is also the case of eBay. These

parameters determined the distribution of the future expected growth

rate in revenue. It can be shown that 0 positively related to variance of

the distribution. 0 is the initial volatility of revenue growth rate. Since it

converges to 0, the higher the initial value, the larger initial variance in

the process.

k is the higher speed the growth rate in revenue converges to it long

term level. The larger the value of k, the more quickly the initial high

growth rate decrease, the smaller the possibility that the growth rate

maintain a high initial level. Therefore, k has a negative effect on the

companys sales and then the same effect on the firms value.

22


23/27

The relationship between 0 and firm value reported in table 3 indicates

that the variance of the distribution of future sales growth rate

determines the value of the firm in a positive way. The reason behinds

this is, if eBays revenue achieves anextreme high growth rate, it enjoys

an extreme high market value; if it suffers an extreme low growth rate, it

may go to bankruptcy, the worst case is to get a zero market value.

Schwartz and Moon explained this as the option character of the firm

value. By analogy with financial options, the value increases when

uncertainty increases.

Consider the initial uncertainty in sales, 0 , contrary to 0 , it has a

negative effect on the total firm value. Bhmer interpreted this as that

higher volatility in sales means higher risk which leads to lower value.

Investor values uncertainty about expectation as a chance( positively)

and values volatility in sales as risk (negatively)9. The classic

evaluation theory is observed in this situation. The number in table also

reports that the sensitivity of eBays value to the initial volatility of

revenue is smaller than the sensitivity to initial volatility in expected

growth rate.

6. Conclusion

This paper adopts Schwartz and Moons simulated based model to

9 see Bmer, C (2001) p.18.

23


24/27

examine the market value of Internet company eBay. The model is based

on some assumptions such as the behavior of the company revenue

growth rate, future financing opportunities, dividends policy, horizon fo

estimation and so on. The advantage of this model is that it incorporates

the uncertainty in the development of the company and has the ability to

depict the real option character of the firm. The disanvantage is it is

based on some assumptions mentioned above and the final result is

highly dependent on the estimations of the input parameter.

This case study also examined eBays financial report and considered its

business character. Based on these analysis, the parameters needed by

the simulation were observed or estimated. The simulations were

performed for 900 times and the mean of these simulation results were

calculated. Although the final result is very sensitive to these

assumptions and inputted parameters, the result suggested that at present

the market value of eBay is under valuated. For simplicity case, this

case study did not examine the capital structure of eBay and not

determine the share value.

1.

2.

24


25/27

25


26/27

Bibliography1. Amazon annual report(2001)(Form 10-K)

http://amazon.com, visited December 25th , 2002

2. Amazon stock price and chart

http://quotes.nasdaq.com/quote.dllvisited December 23rd , 2002

3. Bhmer, C (2001) Valuation of dot.com,International Finance, St.

Gallen: University of St. Gallen

4. Business: Not quite another eBay, The Economist, Mar 10, 2001

5. eBay annual report 1999(Form 10-K), internet:

http://www.shareholder.com/ebay/annual.cfm ,

6. visited December 25th , 2002

7. eBay annual report 2001(form.10-K), internet:

http://www.shareholder.com/ebay/annual.cfm,visited December 25th , 2002

8. eBay stock price and chart

9. http://quotes.nasdaq.com/quote.dll

visited December 23rd , 2002

8. Hull,J,(2000): Options, Futures, and Other Derivatives, 4th

Edition,Upper Saddle River, NJ: Prentice-Hall international.

26
http://amazon.com/http://quotes.nasdaq.com/quote.dllhttp://www.shareholder.com/ebay/annual.cfmhttp://www.shareholder.com/ebay/annual.cfmhttp://quotes.nasdaq.com/quote.dllhttp://quotes.nasdaq.com/quote.dllhttp://amazon.com/http://quotes.nasdaq.com/quote.dllhttp://www.shareholder.com/ebay/annual.cfmhttp://www.shareholder.com/ebay/annual.cfmhttp://quotes.nasdaq.com/quote.dll


27/27

9.Leuhrman,T.,1998, Investment Opportunities as real options:

Getting Started on the Numbers, Harvard Business Review, July-

August, pp.51-67.

10. Rajgopal, S.; Kotha, S.; Venkatachalam, M.: The Relevance of

Web Traffic for Stock Prices of Internet Firms,

http://us.badm.washington.edu/kotha/personal/pdf

%20files/Internet%20paper.pdf

11 Schwartz, S. E.; Moon, M(2000) Rational pricing of internetcompanies,Financial Analysts Journal, Vol.56, No.3, pp.62-75

12.Trueman,B.; Wong, M.H.H; and Zhang,X.-J. (2000): The eyeballsHave it: Searching for the value in Internet Stocks,Journal of Accounting

Research, Supplement 2000, Vol.38, USA,pp.137-162.

Documents

How to Use Simulation to Evaluate a Interter Company