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Cg31545555

  1. 1. Deokkyo Oh , Young-Hee Cho / International Journal of Engineering Research and Applications (IJERA) SSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.545-555 Validating the Technology Valuation with Monte Carlo Simulation DEOKKYO OH (Corresponding Author), Young-Hee Cho *Korea Corporate Governance Service/ Research fellow 76 Youinaru-ro, Yeongdeungpo-gu, Seoul 150-977, Korea. **Korea Institute for the Advancement of Technology / senior manager 305 Teheran-ro, Kangnam-gu, Seoul 135-780, Korea1. INTRODUCTION Technology valuation is performed when There are largely three valuation approaches:a company needs a decision making on project income, cost, market approach (Mard 2000; Pavrifinancing, especially Research and Development 1999; Park and Park 2004). The market approach(R&D) project financing but some people wonder estimates the market price of a similar technologywhether or not it reflects the reality due to the that has already been traded on the market andsubjectivity in the valuation process. It is the very applies it to their assessment (Reilly and Schweihslethal problem especially in the third-party 1998; Baek et al. 2007). This approach is rathervaluation as the fairness is basically required simple and direct method and the prerequisite is the(Hwang 2000, p.7). To reduce or eliminate the existence of active public market and transactionsubjectivity during valuation, the Monte Carlo data of comparable properties (Park and Parksimulation is often introduced as one of effective 2004). In this approach, determining the similaritymethods (Razgaitis 2003, p.64). level is the most important parameter affecting the Decision problems usually involve value of technology. However, if the market is notrandom variables and risk and although expected well activated, this approach is not effective at all.value and decision analysis techniques can be Cost approach is to calculate the valueuseful approaches for dealing with analytical based on the cost expensed in developing themodels, it is difficult to apply or present a complete technology and the depreciation factor is applied topicture of risk (Camm and Evans 1999, p. 286). depreciate the cost annually. This approachFurthermore, some problems are difficult to solve necessitates accurate cost data and depreciation butanalytically because they consist of random in reality it is difficult to obtain cost data and tovariables represented by probability distributions estimate depreciation factor (Park and Park 2004).(Russell and Taylor 2004, p. 564). To reduce this Cost approach is the easiest way to value theuncertainty, Monte Carlo (MC) simulation is often technology only if there is enough information onutilized. cost, but shows some flaws such that the This research shall focus on performing technology value is lowered as it is estimated bythe MC simulation in the technology valuation to historical total cost. This means technology valuesembody the validity and reduce the subjectivity in cannot be beyond the total costs. This approach isthe valuation. Specifically, the MC simulation shall utilized most in the accounting and taxation fieldsbe performed for the technology valuation for a because it is manifest to understand and thesmall and medium enterprise extracting depreciation years are already legitimately fixed bydistributions from the external data source to add types of products.the validity onto the technology valuation result. As cost approach is based on the economic principle of substitution that postulates2. TECHNOLOGY VALUATION that a prudent buyer would pay no more and a Technology assessment is categorized willing seller could command no more for ainto technology evaluation and valuation. technology than the cost to create an intellectualEvaluation means the rating or ranking assessment asset of equal desirability and utility, there are twosystem that the output is ranking or score. Most fundamental types of cost quantified, reproductionevaluation models are developed based on the cost and replacement cost: reproduction costlinear regression equations. Regression analysis is contemplates the construction of an exact replica offrequently exploited to develop the evaluation the subject intellectual property, while replacementmodel. Technology valuation represents the cost contemplates the cost to recreate thefinancial valuation of technologies that the output functionality or utility of the subject technologyis the monetary value. That is to say, technology (Park and Park 2004; Cha 2009).valuation is to find the economic or monetary value Income approach is to value a technologyof technology at the microeconomic or firm level by the expected future earnings of the technology.(Baek, Hong and Kim 2007). In detail, this approach considers the sum of the 545 | P a g e
  2. 2. Deokkyo Oh , Young-Hee Cho / International Journal of Engineering Research and Applications (IJERA) SSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.545-555present values of future cash flows of the significantly incorrect answer when these optionstechnology as the value of the technology (Baek et to change course are present (Trigeoris and Masonal. 2007). The income approach is considered to be 1987; re-quoted by Faulkner 1996). There are sobest suited for the valuation of intellectual property much research to overcome flaws of NPV-DCFsuch as patents, trademarks and copyrights (Park method in the DCF domain: Stewart et al. (2001)and Park 2004). Among three approaches, the most proposed the Risk-adjusted NPV (rNPV), using thepopular method is income approach because it has Acmed’s rNPV that is the NPV of the risk-adjustedthe strong theoretical background in the academia payoff minus the sum of the NPV of the risk-and it is easy to calculate the technology value adjusted costs and argued that by rNPV approach,(Faulkner 1996; Kelly 2007). biotechnology companies may be able to seek debt Table 1 below epitomizes and compares financing even at early R&D stages; Herbohn andthree valuation approaches. Harrison (2005) suggest that three performanceTable 1 Comparison of three basic approaches criteria, NPV, Profitability Index (PI) and Internal(Park and Park 2004) Rate of Return (IRR), should be used together inApproach Cost Market Income assessing projects; and Shrieves and Wachowicz Valuing (2001) show that the discounting of appropriately Valuing defined cash flows under the Free Cash Flow based on Valuing based on (FCF) valuation approach is mathematically cost based on the present equivalent to the discounting of appropriately required the price ofDefinition worth of defined economic profits under the Economic to comparable future Value Added (EVA) approach, positing that FCF reproduce subjects in income approach focuses on the periodic total cash, or replace market flow whereas EVA approach requires defining the subject Possible to periodic total investment in the firm and FCF and Possible to EVA are conceptually equivalent to NPV. capture Easy to calculate As well as the DCF domain efforts to present calculate the most overcome the flaws of DCF, Real Option Valuation worthMerits if cost rational (ROV) is regarded as an alternative approach based on data is value if method. Triest and Vis (2007) argue that the DCF profit- available market data method can be extended using the so-called real generating is available options approach to valuation as the real options capability Lack of Chance of approach allows for flexibility in pursuing or Ignorance abandoning lines of research and in the actual market data error due of future application of technology, since this will generallyDemerits on to potential require irreversible investments. Real options comparable subjective of subject represent the application of options methodology to assets estimation business situations (Boer 2000) and differ from financial ones in several important respects: they The most popular income approach is Net Present cannot be valued the same way, they are typically Value (NPV) – Discounted Cash Flow (DCF) less liquid, and the real value of an investment to analysis. NPV is n n one firm may differ a lot from its value to another NCFt NPV  PVNCF  NINV   t 1 (1  k ) t   NINV  firm (McGrath and)  NINV 2000). The ROV t 1 ( NCFt * PVIF k ,t MacMillan approach is to project valuation seeks to correct the deficiencies of traditional methods of valuation, where PVNCF is the present value of net cash NPV and DCF through the recognition that active management and managerial flexibility can bring flows, NINV the net investment, k the cost of significant value to a project (Jacob and Kwak capital, NCF the net cash flow, and PVIF the 2003). It incorporates the financial concepts of options in technology valuation and as options arepresent value interest factor (Moyer, Mcguigan and not considered as an obligation but a right, the Kretlow 2006, pp. 334-335). investors have the opportunity to correct their Though it has the strong theoretical decision according to future environmentbackground, it has the several flaws to overcome: (Copeland and Antikarov 2001; Baek et al. 2007).DCF assumes that the same discount rate will be ROV is truly an extension to DCF analysis in that itused over the entire time span of the analysis requires more information, not different(Faulkner 1996); the standard DCF models ignore information: next to expected cash flows, thethe value of management’s ability to make essential ingredient is the standard deviation ofdownstream decisions (Kensinger 1987); and these expected cash flows (Triest and Vis 2007),managers have the flexibility to change course however real option models are superior to simplewhen conditions change and show DCF can give a NPV valuation models in that they recognize the 546 | P a g e
  3. 3. Deokkyo Oh , Young-Hee Cho / International Journal of Engineering Research and Applications (IJERA) SSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.545-555importance of managerial flexibility (Jacob and best/ worst case analysis and what-if analysisKwak 2003; Boer 2000; Razgaitis 2003, p. 68). (Ragsdale 2008, pp.561-562).ROV is generally preferable to DCF for high risk Many modern complex stochasticinvestments (Sharp 1991; re-quoted by Faulkner systems can be modeled as discrete event systems,1996), especially suitable for young patents where for example, communication networks, computerthere is much uncertainty about the effectiveness systems, production lines, flexible manufacturingand the rewards of the technology (Triest and Vis systems, traffic systems, reliability systems, project2007). If the technology is already developed and networks, and flow networks, which systems arecommercialized, DCF analysis is preferable to driven by the occurrence of discrete events overROV (Triest and Vis 2007). Eichner, Gemünden time, studying their performance and optimizationand Kautzsch (2007) performed the binomial real of their parameters are important tasks and theoption valuation obtaining three critical factors that conventional approach for analyzing such complexare the most sensitive factors to the value of the system is a simulation, which means the standardcompany, present value, volatility, and investment computer-based discrete event simulation whichexpenditure and argued that the real option involves writing a computer program to mimic thevaluation can create value beyond the value of system and then performing a MC simulationexpected cash flow presented by DCF analysis by (Rubinstein 1989).managerial flexibility and volatility. Specifically, the MC simulation is However, ROV is not always utilized. applied for design and operation of queuingFor ROV, managers should understand their systems, managing inventory systems, estimatingoptions. Good managers have always understood the probability of completing a project by thetheir options under changing circumstances and deadline, design and operations of manufacturingarguably a good portion of their tactical skill was to and distribution systems, financial risk analysis,recognize and evaluate the available options and health care applications such as resource allocation,neglecting the options approach was one of the expenditure under insurance plans, timing andtraps, pitfalls and snares in the valuation of location of ambulance services, operation oftechnology (Boer 2000). For managers’ better emergency room (Hillier and Lieberman 2001, pp.understanding their options, real option reasoning 1097-1126), and estimation of willingness-to-payis highly recommended, which is a logic for (Kanninen and Kristrom 1993).funding projects that maximizes learning and One of the most sophisticated MCaccess to upside opportunities while containing simulation approaches is Markov Chain Montecosts and downside risk (McGrath and MacMillan Carlo (MCMC) approach that is mostly utilized in2000). the Stochastic Volatility (SV) model (Poon 2005, p. 59). MCMC is a general method for the simulation3. MONTE CARLO SIMULATION of stochastic processes having probability densities MC simulation is more narrowly defined known up to a constant proportionality and can beas a technique for selecting numbers randomly used to simulate a wide variety of random variablesfrom a probability distribution for use in a and stochastic processes and is useful in Bayesian,simulation (Russell and Taylor 2004, p. 564) as is likelihood and frequentist statistical inferencebased on repeated sampling from the probability (Geyer 1992). Chib and Greenberg (1996) alsodistributions of model inputs to characterize the introduced and explained some algorithmsdistributions of model outputs and we may regarding MC simulation: Metropolis-Hastingsconstruct a distribution of potential outcomes of (MH) algorithm proposed by Metropolis,key model variables along with their likelihood of Rosenbluth, Teller and Teller (1953) and Hastingsoccurrence by randomly selecting model inputs and (1970), Gibbs sampling algorithm introduced byevaluating the outcomes (Camm and Evans 1999, p. Geman and Geman (1984) and extended by Tanner286) so the purpose of MC process is said to and Wong (1987) and Gelfand and Smith (1990),generate the random variable by sampling from the hybrid MH and rejection sampling (Tierney 1994),probability distribution (Russell and Taylor 2004, p. and stochastic EM algorithm (Celeux and Diebolt564; Chib and Greenberg 1996). Technically, the 1985).MC simulation as a variance-reducing techniques The most advantageous thing of MCbecause as a simulation progresses the variance of simulation is that this simulation tool can providethe mean decreases (Hillier and Lieberman 2001, the distribution of forecasting variable, not single-p.1126). This provides an assessment of the risk valued answer (Razgaitis 2003, p. 70). Managersassociated with a set of decisions that analytical can make decisions only about whether to accept ifmethods generally cannot capture and managers single-valued answer is provided to them. If thecan use a simulation model to help identify good distribution of forecasting variable is offered todecisions (Camm and Evans 1999, p. 286). The managers, they can more easily make decisionsconventional analysis with the MC simulation is about whether to accept and propose alternatives by comparing the likelihood of forecasting variable. 547 | P a g e
  4. 4. Deokkyo Oh , Young-Hee Cho / International Journal of Engineering Research and Applications (IJERA) SSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.545-555 That is, managers can estimate feasible answers mentions that the confidence problem can beby investigating the distribution of value of solved by utilizing the valid market informationtechnology. If the valuation result has not a high and minimizing the probability of generating theprobability to be higher than the result, the outliers in setting the distributions of variables.alternative technology value can be suggested bycalculating the probability to be higher statistically 4. ANALYSISsignificantly or to the level that they can consider it. 4.1. Technology valuationThus, MC simulation can offer managers more Two kinds of data shall be used in thisreasonable decision making and make decision analysis. The first data set is a firm’s valuationmaking process simpler by suggesting and result which is a basis of simulation and the secondcomparing the probability from the distribution of data set is input variables’ distributions forvalue of technology, i.e., the simulation result simulation. The first data of a valuation result shall(Sung 2004). be explained in this section. MC simulation is one of the most Table 2 below shows the valuation resultpowerful valuation tools and is very useful to that will be adapted to this simulation 1. This firm iseliminate the uncertainty in valuation (Razgaitis a small and medium enterprise (SME) that2003, p. 64). Razgaitis (2003) explains some manufactures several types of portable mediadistributions for cost structure in the MC players in Korea and was established five yearssimulation such as uniform distribution, triangular before at the time of valuation. Manufacturers ofdistribution, and normal distribution: the uniform portable media player come under the industry ofdistribution that can be applied in case of high electronic components, radio, television anduncertainty, the normal distribution that is the most communication equipment and apparatuses (C26)2frequently used, and some special distributions according to the Korean standard industrysuch as double crown distribution to be combined categorization. In Table 2, the discount rate iswith the scenario analysis (pp. 91-93, 97-100, 115- 22.78% which is calculated by adding size and120). technology risk premiums to Weighted Average Furthermore, Sung (2004) explains the Cost of Capital (WACC) as it is conventional topros and cons of MC Simulation: for Pros: 1) it is calculate and add two types of risk premiums in thepossible to make decisions efficiently via this valuation for small and medium enterprisessimulation, 2) easy to understand the result; for because small and middle sized firms are riskierCons: 1) it needs the suitable computer software for than large sized companies (Korea Technologythe purpose, 2) sometimes it is lacked in the Transfer Center (KTTC) 2003, p. 89).confidence on the simulation result, and heTable 2 Valuation result Contents (Million Korean won) T+1 T+2 T+3 T+4 T+5 Revenue ( R ) 5,406 7,028 8,855 10,449 11,912 Cost of sales (O) 4,757 6,974 9,423 11,636 13,879 Operating expenses (E) 867 1,107 1,333 1,580 1,732 Depreciation (D) 211 337 416 512 619 EBIT(A=R-O-E-D) -239 -10 544 905 1,330 Income tax (B) 0 0 0 0 266 Depreciation (D) 21 39 53 70 89 Operating Cash Flow (C=A-B+D) -218 29 597 974 1,153 Increment of working capital (G) -50 138 127 85 22 Investment for assets (H) 100 129 170 280 400 Total investment (I=G+H) 50 267 297 365 422 FCF (F=C-I) -268 -238 300 609 731 Discount rate 22.78% PV of FCF -219 -158 162 268 262 Sum of PV(FCF) (J) 316 Terminal Value (K) 1,674 NPV (M=J+K) 1,990 Technology contribution coefficient (N=L*M) 66.77% Industry Factor (L) 97.47% Individual technology strength (M) 68.50% Value of technology (V=M*N) 1,328 548 | P a g e
  5. 5. Deokkyo Oh , Young-Hee Cho / International Journal of Engineering Research and Applications (IJERA) SSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.545-555This research is based on FCF analysis which is the (FSA) report of the Bank of Korea (BOK): growthmost frequently used in valuation. Criteria rate of revenue, cost of sales, operating expenses,commonly applied in the capital budgeting decision depreciation cost, and increment of working capital.model as well as the financial valuation are NPV, Extracted data are the average values of thePI, IRR, and Payback Period (PB) and the primary industry category (C26) in Korea. Specifically,decision making rule used is NPV that is these data are obtained from FSA reports for small n FCFt n (CF  IC t  DEPt ) and middle sized firms, as the FSA report analyzesNPV  PVFCF  TV    TV  t  TV financial statements of an industry categorizing t 1 (1  k ) (1  k ) t t t 1 firms into large and small-and-middle sized firms. The increment of working capital is calculated withwhere PVFCF is the present value of free cash the formula offlows, TV terminal value, CF the cash flow, IC t  WC t  WC t  WC t 1  ( Rt  IN t  Pt )  ( Rt 1  IN t 1  Pt 1 ) IC the increment of working capital, DEP where IC represents the increment ofdepreciation, and k the discount rate (Kang, Lee, working capital, WC working capital, Rand Cho 1997, p.139). receivables, IN inventories, and P payables. Thus, The valuation result, value of technology is distributions of five variables for ten years, 2000 to1,328 million won while NPV is 1,990 million won. 2009, are obtained from FSA reports. The extractedThe terminal value is remarkably shown much data are shown in Table 3 below. Data for threegreater than the sum of present value of FCF as it is variables show that rates have not a significantassumed to sustain its business of (T+5) year trend of increase or decrease but the oscillatoryinfinitely and continuously. To calculate the value trend over years. Trends of variables are easilyof technology, technology contribution coefficient understood by seeing charts shown in Figures 1which consists of industry factor and individual through 5. This oscillation adds the validity to thistechnology strength should be multiplied with NPV. MC simulation as shows variables are notThe industry factor of corresponding industry dependent on time but seem to be stochastic.category was calculated as 97.465% on average(KTTC 2006, p. 105) and individual technologystrength was calculated to be 68.5%.4.2. Simulation data The data of the Bank of Korea areutilized to obtain some variables’ distributionsbecause it is difficult to obtain due to the shorthistory of this firm. As proxies, we chose inputvariables available in Financial Statement Analysis Table 3 Descriptive statistics of Extracted input data Growth rate of Operating Increment of working Year Cost of sales Depreciation revenue expense capital 2000 0.2546 0.8458 0.0972 0.0053 0.0450 2001 -0.028 0.8665 0.1283 0.0053 0.0156 2002 0.1764 0.8457 0.1155 0.0049 0.0465 2003 0.1567 0.8135 0.1308 0.0054 0.0440 2004 0.1980 0.8391 0.131 0.0049 0.0118 2005 0.0297 0.85 0.1094 0.0046 -0.0011 2006 0.0616 0.843 0.1192 0.0039 -0.0003 2007 0.0016 0.8135 0.1423 0.0059 0.0238 2008 0.0661 0.8212 0.1453 0.0057 0.0602 2009 0.1717 0.8111 0.1283 0.0046 0.0201 Max 0.2546 0.867 0.145 0.006 0.060 Min -0.028 0.811 0.097 0.004 -0.001 Mean 0.109 0.835 0.125 0.005 0.027 SD 0.095 0.019 0.015 0.001 0.021 83.5% portions of revenue. Coefficient of Variation In Table 3 above, the values of variables are (CV) expressed by dividing the standard deviationcalculated based on revenue, that is to say, the by the mean shall be calculated for four variables toratios to the revenue. For example, the mean of cost understand the dispersion of each variable. Cost ofof sales, 83.5% represents the cost of sales takes sales has coefficient of variation of 0.023, 549 | P a g e
  6. 6. Deokkyo Oh , Young-Hee Cho / International Journal of Engineering Research and Applications (IJERA) SSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.545-555operating expenses 0.118, depreciation 0.117, theincrement of working capital 0.8, and growth rateof revenue 0.87. The calculation of CV implies thatgrowth rate of revenue is most widely dispersedrelated with the mean value, the second mostwidely dispersed variable is the increment ofworking capital, and cost of sales is the leastwidely dispersed among five variables. Figure 3 Operating expenses Figure 4 Depreciation Figure 5 Increment of working capital 4.3. Rules for selecting distributions and making decisions If a valuation parameter value deviates from the distribution range, the uniform distribution ranged from the value of valuation parameter to the minimum value of distribution or from the maximum value of distribution to the value of valuation parameter will be applied. Otherwise, the normal distribution with the mean and standard deviation illustrated in Table 4 will be applied. This policy is expressed as:Figure 1 Growth rate of revenue i) V ~ N (  F ,  F ) , if V  [ Fm in , Fm ax ] ,Figure 2 Cost of sales (rate to therevenue) ii) V ~ U (Vt , Fm in ) , if V  [ Fm in , Fm ax ] and V  Fmin , and iii) V ~ U ( Fmax , Vt ) if V  [ Fm in , Fm ax ] and , V  Fm ax where V represents one of five variables above, Fm in the minimum value, Fm ax the maximum,  F the mean and  F the standard deviation of corresponding external data to a variable, V. The simulation result is compared with the valuation result and whether it is properly, over or under-estimated shall be determined as follows: i) Proper estimation, if TV  [TV s. m in , TV s . m ax ] , ii) Over-estimation, if TV  TV s . m ax , and 550 | P a g e
  7. 7. Deokkyo Oh , Young-Hee Cho / International Journal of Engineering Research and Applications (IJERA) SSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.545-555iii) Under-estimation, if TV  TV s . m in statistics of simulation result is illustrated in Table 4 below. The mean of NPV simulated is 357where TV represents the technology valuation million won and the median is 328 million won. Asresult (original value of technology), and TV s. m in its standard deviation is 498 million won and is a little bit high so the value of technology isand TV s. max the minimum and maximum values recognized to be widely dispersed. Maximum valueof technology simulated within the properly of technology value is 2,931 million won and theacceptable range, respectively. To examine the minimum value is -1,334 million won.overestimation and find the proper range of value Statistics of goodness of fit explain whichof technology, 20th percentile rule is applied which distribution is fit to the simulation result in Crystalrepresents 20 to 30th percentile in the distribution of Ball by A-D (Anderson–Darling),  , K-S 2value of technology is the proper range for the (Kolmogorov-Smirnov) test results (see Table 5).reasonable negotiation (Razgaitis 2003, p. 110). The distribution of NPV is best fit to the GammaThat is to say, this rule permits the 20~30%subjectivity based on the mean value of simulation distribution with the location of -2,067, the scale ofresult as a proper and essential subjectivity. If the 103 and the shape of 23.46, whose probabilitytechnology value exists over this properly accepted density function is mathematically definedrange, it can be regarded as an excessively e ( x L) /  as f ((x  L); k , )  ( x  L) k 1 ,subjective technology valuation result. Finally, the value of technology is set as the  k ( k )forecasting variable in MC Simulation. Crystal Ball where L represents the location, k is the shapeprogram shall be utilized to perform the MC parameter and θ is the scale parameter (Crystal Ballsimulation in this research. User’s Guide 2009, pp.240-242; Coit and Jin 1999).4.4. Simulation Result Under given assumptions, we obtainedthe result of simulation after 10,000 trials. The Table 4 Summary of simulation result Statistic Forecast values Trials 10,000 Mean 357 Million won Median 328 Million won Mode - Standard Deviation 498 Variance 248,314 Skewness 0.3959 Kurtosis 3.54 Coeff. of Variability 1.39 Minimum -1,334 Million won Maximum 2,931 Million won Mean Std. Error 5 Table 5 Statistics of goodness of fitDistribution A-D Chi-Square K-S ParametersGamma 2.5394 78.2376 0.0124 Location= (2,067), Scale=103, Shape=23.46Logistic 11.1597 230.1892 0.0175 Mean=341, Scale=281Students t 15.8957 218.5712 0.0271 Midpoint=357, Scale=483, d.f.=17.0Normal 16.2357 219.2224 0.0283 Mean=357, Std. Dev.=498Beta 16.6401 229.5232 0.0286 Min= (6,707), Max=7,422, Alpha=100, Beta=100Max Extreme 58.5755 588.6304 0.0461 Likeliest=116, Scale=462Min Extreme 266.5455 2,177.78 0.1018 Likeliest=614, Scale=563BetaPERT 614.4368 3,911.75 0.1496 Min= (1,357), Likeliest=149, Max=2,962Triangular 891.1076 4,657.30 0.218 Min= (1,357), Likeliest=149, Max=2,962Uniform 2,097.17 14,967.27 0.3547 Min= (1,335), Max=2,931Weibull 5,626.38 15,355.67 0.4378 Location= (1,334), Scale=1,275, Shape=2.41865  It is ranked by Anderson-Darling test results. 551 | P a g e
  8. 8. Deokkyo Oh , Young-Hee Cho / International Journal of Engineering Research and Applications (IJERA) SSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.545-555 As an analysis result, we obtained the chart of of the picture. Figure 7 shows the probability todistribution of NPV, forecasting variable in this have the lower value of technology than thesimulation, under the given assumptions and Figure valuation result, 96.52%. This means the6 below depicts the distribution of value of probability of higher value of technology thantechnology which is best fit to the gamma 1,328 million won is just 3.48%. It shows that thedistribution graphically. The probability of lower valuation was performed optimistically andtechnology value than the valuation result can be overestimated.shown by moving the arrows located in the bottom Figure 6 Distribution of value of technology Figure 7 Probability to have the lower technology value than the valuation result exponentially. As the technology value is about 1.3 billion won, beyond the upper limit of proper range, Table 6 depicts the percentile of forecasting 755 million won, we can say that the technologyvariable, the value of technology. Under Razgaitis’ valuation is over estimated.suggestion, the proper range of value of technology,20 to 30th percentile, is 583~755 million won forthis technology. Figure 8 depicts the location ofthis feasible range of value of technology. It islocated in near the mean value because the meanhas the highest probability and as the value goes farfrom the mean the probability is lowered 552 | P a g e
  9. 9. Deokkyo Oh , Young-Hee Cho / International Journal of Engineering Research and Applications (IJERA) SSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.545-555Table 6 Percentile of value of technology 50% 328 Forecast values 40% 449 Percentile (Unit: million won) 30% 583 100% -1,334 20% 755 90% -252 10% 1,002 80% -54 0% 2,931 70% 88 60% 208 Figure 8 20~30 % range of value of technology Finally, the sensitivity analysis is performed forthis simulation. We shall choose the contribution tovariance view which illustrates the effects of allassumptive distributions on the value of technologyare ranked from the largest contribution totechnology value uncertainty (variance) to the leastand the five percentage rule shall be applied indetermining the contribution of variables onforecasting variable that all assumptions thatcontribute less than five percentage of theuncertainty can be ignored (Razgaitis 2003, pp.120-121). As shown in Figure 9, all variablesexhibited have the contribution of over 5% of thevariance to the value of technology. Growth rate ofrevenue has the positive effect on the value oftechnology, while cost of sales has the negativeeffect (refer to Figure 9). The cost of sales in (T+5)year is significantly the most important factor,which shows that the horizon value is much greater Label Contentthan the sum of present values, and the growth rate C3 Growth rat e of revenue in (t+2) yearof revenue in (T+2) year is the second most D3 Growth rat e of revenue in (t+3) yearimportant factor. The third most important factor is D5 Cost of sales in (t+3) yearthe growth rate of revenue in (T+5) year. E5 Cost of sales in (t+4) year F3 Growth rat e of revenue in (t+2) year F5 Cost of sales in (t+5) year Figure 9 Sensitivity analysis result 553 | P a g e
  10. 10. Deokkyo Oh , Young-Hee Cho / International Journal of Engineering Research and Applications (IJERA) SSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.545-5555. CONCLUSIONS domestic product and can make valuation We performed the MC simulation for more feasible.the valuation for a specific small-sized firm.Throughout the simulation, we could find out REFERENCESthe likelihood of value of technology under 1. Baek, D., Sul, W., Hong, K. and Kim,H.given distributions (assumptions), estimate the (2007). A technology valuation model toproper range of value of technology with the support technology transfer negotiations. th R&D Management, 37(2), 123-138.20 percentile rule of Razgaitis (2003, p.110), 2. Boer, F. Peter (1999). The valuation ofand decide if the valuation result is properly technology: Business and financial issuesestimated or not. in R&D. NJ: John Wiley&Sons. MC simulation can effectively reduce 3. ____________ (2000). Valuation ofthe subjectivity in the technology valuation by technology using real options. Researchutilizing the fair data such as the FSA data Technology Management, 43(4), 26-30.from the BOK. Modelers should be aware of 4. Bowman, E. H. & Hurry, D. (1993).the importance of fair data. Fair data can Strategy through the option lens: anvalidate the valuation data reducing the integrated view of resource investmentssubjectivity. As the third-party technology and the incremental-choice process. Academy of Management Review, 18(4),valuation is required the fairness enough to be 760~782.perceived commonly in the society, especially 5. Bullard, C. W. and Sebald, A. V. (1988).in Korea (Hwang 2000, p. 7), to be sure, the Monte Carlo analysis of input-outputMC simulation with the fair data can satisfy models. The review of Economics andthis requirement. Statistics, 70(4), 708-712. As MC simulation can help valuation 6. Camm, J. D., and Evans, J. R. (1999).more persuasive thus, it is thought to be Management science & decisionapplied to other valuation approaches: cost and technology. OH: South western collegemarket approaches. For the market approach, publishing.it can be utilized in estimating the similarity 7. Cha, S. (2009). Technology Valuation. News & Information for chemicalbased on the probability. The representative engineers, 27(4), 459-461.similar technology or company is chosen to 8. Chib, S. and Greenberg, E. (1996).compare, then how much a technology is Markov Chain Monte Carlo Simulationsimilar with the representative should be Methods in Econometrics. Econometricestimated expressed as a probability. Finally, Theory, 12(3), 409-431.the value of technology is calculated by 9. Coit, D. W. and Jin,T. (2000). Gammamultiplying the market value and the similarity distribution parameter estimation for fieldlevel. For the cost approach, it can be applied reliability data with missing failure times.in estimating the depreciation factor, IIE Transactions, 32, 1161-1166.replacement cost, and reproduction cost. 10. Copeland, T., Koller, T and Murrin, J. (2002). Corporate valuation. 2nd edition,Replacement and reproduction cost may vary NJ: John Wiley&Sons.with the manufacturers so there must exist 11. Eichner, T., Gemünden, H. G., anddeviations. With the shapes of deviations, we Kautzsch,T. (2007). What is technologycan implement the MC simulation to obtain worth? A real options case study onthe distribution of outputs. technology carve-out venture valuation. For further research, it still needs the Journal of investing (Fall), 96-103.reconsideration of 20th percentile rule. This 12. Faulkner, T. W. (1996). Applying ‘Optionrule can be different by regions as people, Thinking’ to R&D valuation. Researchsociety and its atmosphere, and environment Technology Management, 39(3), 50-56.which affect the technology value are different. 13. Geyer C. J. (1992). Practical Markov Monte Carlo. Statistical Science, 7(4),In addition, reflection of economic trend can 473-483.be considered as it is not considered as a 14. Hanke, J. E., and Wichern, Dean W.parameter in this research. Calculating and (2003). Business forecasting. 8th edition,estimating the neutral growth rate of revenue NJ: Prentice Hall.in the MC simulation by subtracting the 15. Herbohn, J. and Harrison, S. (2005).growth rate of revenue from that of gross Introduction to discount cash flow analysis and financial functions in Excel. 554 | P a g e
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