The publisher is John Wiley and Sons.
So, whereas in a DCF valuation the most likely or average or scenario specific cash flows are discounted, here the "flexible and staged nature" of the investment is modelledand hence "all" potential payoffs are considered. See further under Real options valuation.
The difference between the two valuations is the "value of flexibility" inherent in the project. DTA values flexibility by incorporating possible events or states and consequent management decisions.
For example, a company would build a factory given that demand for its product exceeded a certain level during the pilot-phase, and outsource production otherwise.
In turn, given further demand, it would similarly expand the factory, and maintain it otherwise. In a DCF model, by contrast, there is no "branching" — each scenario must be modelled separately.
In the decision treeeach management decision in response to an "event" generates a "branch" or "path" which the company could follow; the probabilities of each event are determined or specified by management.
Once the tree is constructed: See Decision theory Choice under uncertainty. ROV is usually used when the value of a project is contingent on the value of some other asset or underlying variable.
For example, the viability of a mining project is contingent on the price of gold ; if the price is too low, management will abandon the mining rightsif sufficiently high, management will develop the ore body. Again, a DCF valuation would capture only one of these outcomes. Real options in corporate finance were first discussed by Stewart Myers in ; viewing corporate strategy as a series of options was originally per Timothy Luehrmanin the late s.
See also Option pricing approaches under Business valuation. Sensitivity analysisScenario planningand Monte Carlo methods in finance Given the uncertainty inherent in project forecasting and valuation,   analysts will wish to assess the sensitivity of project NPV to the various inputs i.
In a typical sensitivity analysis the analyst will vary one key factor while holding all other inputs constant, ceteris paribus.
The sensitivity of NPV to a change in that factor is then observed, and is calculated as a "slope": For example, the analyst will determine NPV at various growth rates in annual revenue as specified usually at set increments, e.
Often, several variables may be of interest, and their various combinations produce a "value- surface ",  or even a "value- space ", where NPV is then a function of several variables.
See also Stress testing. Using a related technique, analysts also run scenario based forecasts of NPV. Here, a scenario comprises a particular outcome for economy-wide, "global" factors demand for the productexchange ratescommodity pricesetc As an example, the analyst may specify various revenue growth scenarios e.
Note that for scenario based analysis, the various combinations of inputs must be internally consistent see discussion at Financial modelingwhereas for the sensitivity approach these need not be so. An application of this methodology is to determine an " unbiased " NPV, where management determines a subjective probability for each scenario — the NPV for the project is then the probability-weighted average of the various scenarios; see First Chicago Method.
See also rNPVwhere cash flows, as opposed to scenarios, are probability-weighted. A further advancement which "overcomes the limitations of sensitivity and scenario analyses by examining the effects of all possible combinations of variables and their realizations"  is to construct stochastic  or probabilistic financial models — as opposed to the traditional static and deterministic models as above.
This method was introduced to finance by David B. Hertz inalthough it has only recently become common: Here, the cash flow components that are heavily impacted by uncertainty are simulated, mathematically reflecting their "random characteristics".
In contrast to the scenario approach above, the simulation produces several thousand random but possible outcomes, or trials, "covering all conceivable real world contingencies in proportion to their likelihood;"  see Monte Carlo Simulation versus "What If" Scenarios.
The output is then a histogram of project NPV, and the average NPV of the potential investment — as well as its volatility and other sensitivities — is then observed. This histogram provides information not visible from the static DCF: Continuing the above example: These distributions would then be "sampled" repeatedly — incorporating this correlation — so as to generate several thousand random but possible scenarios, with corresponding valuations, which are then used to generate the NPV histogram.
The resultant statistics average NPV and standard deviation of NPV will be a more accurate mirror of the project's "randomness" than the variance observed under the scenario based approach.Corporate Finance 4th Edition by Jonathan Berk; Peter DeMarzo and Publisher Pearson.
Save up to 80% by choosing the eTextbook option for ISBN: , The print version of this textbook is ISBN: , X. Using the unifying valuation framework based on the Law of One Price, top researchers Jonathan Berk and Peter DeMarzo have set the new canon for corporate finance textbooks.
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Fundamentals of Corporate Finance, 4th Edition. By Robert Parrino, Thomas Bates, Stuart L. Gillan, and David S. Kidwell. Fundamentals of Corporate Finance, 4 th Edition develops the key concepts of corporate finance with an intuitive approach while emphasizing computational skills, enabling students to develop the critical judgments necessary to apply financial tools in real-world decision.