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Trading real options analysis course - business cases and software applic - Mun P.D.

Mun P.D. Trading real options analysis course - business cases and software applic - Wiley publishing , 2003. - 318 p.
ISBN 047-43001-3
Download (direct link): tradingohnathan2003.pdf
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their technology. These royalty rates must fall within contractual terms under obligation with the university, and must fall in line with managementís expectations as well as be competitive. These royalty rates must be accounted for within a real options paradigm where the value of the project is greater than its net present value (NPV) as management has the flexibility not to execute on the short-run limited version of the drug should the long-term research results and market acceptance indicate that it is unprofitable. In addition, the firm wants the analysis to account for competitive effects, cannibalization by other comparable products under development, market saturation effects, and other uncertainties while accounting and controlling for the risks such that the project will still be profitable above a certain management-set threshold.
The first step is to collect historical data on revenues of the comparable product. Suppose the only available historical data on revenues are quarterly data starting from 1997 through 2002 of a highly similar product that was developed by the firm. Using these historical data, the analyst performs time-series analysis using Crystal Ballís Predictor (Figure 7.1).
FIGURE 7.1
Sample historical data in Microsoft Excel.
Combining Forecasting, DCF Modeling, Real Options, and Optimization
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The following step shows the selection of the historical data range using Crystal Ballís Predictor (Figure 7.2). Assuming the historical data exhibits seasonality, a quarterly seasonality correction is performed in the next step as shown in Figure 7.3. Predictor automatically chooses the best fitting time-series model from a series of eight different approaches as shown in Figure 7.4.
FIGURE 7.2 Crystal Ballís Predictor step 1 through 3: input data.
CB Predictor
Inpul Data Data Attributes | Method Gallery | Results j Step 4. Indicate the type of data you have and its seasonality
(* seasonality of [& quarters
Data is in [quarters ^
with
no seasonally (al seasonal methods skipped)
Step 5. Optional -- check here it you have dependencies within your data and you woiid like to use linear regression to forecast the dependent variables
I- Use multiple linear regression Se'ect Variables j
Method-1 Standard StCTwiseOtf or:
W Include constant in regression equation
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FIGURE 7.3 Crystal Ballís Predictor step 4 and 5: data attributes.
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FIGURE 7.4 Crystal Ballís Predictor step 6: method gallery.
Then, a forecast is created for 20 periods (Figure 7.5): For each of the 20 quarterly forecast periods, Predictor automatically creates the revenue point forecasts with the relevant distributional assumptions (Figure 7.6). The highlighted cells indicate where forecast revenues with distributional assumptions are attached. These point forecasts are based on the best fitting line in the gallery of time-series approaches. The graph shown in Figure 7.7 illus-
CB Predictor
Inpul Data | Data Attributes | Method Gallery Step 7. Entei the numbę of periods to forecast 120
Step 8. Select a confidence interval: 152 and 95X ^
Step 9. Select the results you want:
P Paste forecasts at cell:
Select. , by ^ rows<* columns
W Report r~ Charts 1ď Resdts table V~ Methods table Preferences... |
Title: I Step I - Forecasting Step 10. CSck Review to see a graph of the results Cick Run to output the resuls.
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FIGURE 7.5 Crystal Ballís Predictor step 7 through 10: results.
D E F G
s s Periodicity: Quarterly
7 g Seasonality: 4
9 Historical Data Forecast
10 Date Revenues Date Revenues
11 Q1 1997 30 Q1 2003 202.27
12 Q2 1997 35 Q2 2003 210.25
13 Q3 1997 42 Q3 2003 217.26
14 Q4 1997 49 Q4 2003 230.30
15 Q11998 50 Q1 2004 231.80
1G Q2 1998 57 Q2 2004 239.86
17 Q3 1998 S3 Q3 2004 246.81
18 Q4 1998 72 Q4 2004 260.60
19 Q11999 80 Q1 2005 261.33
20 02 1999 85 Q2 2005 269.47
21 Q3 1999 92 Q3 2005 276.37
22 Q4 1999 112 Q4 2005 290.90
23 Q1 2000 120 Q1 2006 290.85
24 Q2 2000 135 02 2006 299.08
25 Q3 2000 144 Q3 2006 305.93
26 Q4 2000 156 Q4 2006 321.20
27 Q1 2001 153 Q1 2007 320.38
28 02 2001 166 02 2007 328.69
29 Q3 2001 178 Q3 2007 335.48
30 04 2001 180 Q4 2007 351.50
31 Q1 2002 178
32 02 2002 185
33 Q3 2002 190
34 Q4 2002 200
FIGURE 7.6 Forecast results with distributional assumptions in Excel.
Forecast Chart
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FIGURE 7.7 Crystal Ballís Predictorís forecast charts.
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trates this fitting of historical data, as well as the forecast out to the future, complete with a 5th percentile and 95th percentile confidence interval. Table 7.1 lists the point forecast for each succeeding quarter, with its corresponding 5th percentile and 95th percentile.
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