Assessing the Impact of XML/EDI with Real Option Valuation

von Dr. Shermin Voshmgir

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[1.] Svr/Fragment 071 01 - Diskussion
Zuletzt bearbeitet: 2020-05-25 19:58:12 [[Benutzer:|]]
BauernOpfer, Copeland Weston 1992, Fragment, Gesichtet, SMWFragment, Schutzlevel sysop, Svr

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Untersuchte Arbeit:
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Quelle: Copeland Weston 1992
Seite(n): 28, 29, Zeilen: 28: last paragraph; 29: 14 ff.
[The NPV is computed by] discounting the cash flows at the firms opportunity cost of capital. The NPV method aims to find projects with a positive NPV (greater than zero). Copeland and Weston (1992: 28) state that the NPV of a project is exactly the same as the increase in shareholder's wealth. According to them, the NPV technique is the correct decision rule for capital budgeting purposes.

Copeland, T.E. & Weston, J.F. 1988, Financial Theory and Corporate Policy, 3rd ed., Addison-Wesley, Reading, Massachusetts.

The net present value (NPV) criterion will accept projects that have an NPV greater than zero. The NPV is computed by discounting the cash flows at the firm’s opportunity cost of capital.

[page 29]

The NPV of a project is exactly the same as the increase in shareholders' wealth. This fact makes it the correct decision rule for capital budgeting purposes.

Anmerkungen

The source is given, but no quotation marks are used.

Sichter
(SleepyHollow02) Schumann


[2.] Svr/Fragment 071 12 - Diskussion
Zuletzt bearbeitet: 2020-02-27 15:00:08 [[Benutzer:|]]
Fragment, Gesichtet, KomplettPlagiat, SMWFragment, Schutzlevel sysop, Schuyler 1992, Svr

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Quelle: Schuyler 1992
Seite(n): 17, 18, Zeilen: 17: right col., 11 ff., 26 ff.; 18: left col., 3 ff.
5.3.1 Decision Tree Analysis

A decision tree is a graphical extension of the Expected Value (EV) concept. [To calculate the expected value of a certain scenario, the values of the endpoints (possible scenario) are simply multiplied by the probabilities of reaching these endpoints. To consider the time value of money the value of each endpoint are discounted back to the present.] One of its strengths is that the tree diagram clarifies the decision problem with a visual approach. The diagram clearly shows all important decision elements, including contingencies (outcomes of chance events); decisions and alternatives, the logical sequence of decision points and chance events. One can often solve decision trees by hand, and the method is helpful at providing insights, especially related to the value of additional information and additional control. Branch and outcome values must be value measures (Present Value works, but, for example, internal rate of return (IRR) does not), and probabilities must be assessed or calculated for all chance event outcomes. One of the weaknesses of decision tree analysis is that one must represent all possibilities by a finite number of paths through the tree. This limits the practical number of random variables that can be accommodated, and one must use discrete distributions as approximations to continuous probability functions. Thus, range extremes are not represented. Other weaknesses of this approach include the fact that the analyst must limit the representation of uncertain time-spread events, such as for prices and inflation, to a few scenarios. Also, in [tree diagrams an output probability distribution (risk profile curve) is usually not obtained, only the EV.]

[page 17]

1. Decision Tree Analysis—A decision tree is a graphical extension of the EV concept. Working from right to left, EV is back-calculated by:

  • Replacing chance nodes with their EV’s,
    EV = Σ xi p(xi)
    where
    xi is value (ie, PV) for outcome i, and
    p(xi) is the probability function for xi.
  • Replacing decision nodes with the EV of the best (highest EV) alternative.

The tree diagram clarifies the decision problem with a visual approach, one of its strengths. The diagram clearly shows all important decision elements, including contingencies (outcomes of chance events); decisions and alternatives; and the logical sequence of decision points and chance events. One can often solve decision trees by hand, and the method is helpful at providing insights, especially related to the value of additional information and additional control.

One of the weaknesses of decision tree analysis is that one must represent all possibilities by a finite number of paths through the tree. This limits the practical number of random variables (chance events) that can be accommodated, and one must use discrete distributions as approximations to continuous probability functions; thus, range extremes are not represented.

Other weaknesses of this approach include the fact that the analyst must limit the representation of uncertain time-spread events, such as for prices and inflation, to a few scenarios. Also, in tree diagrams an output probability distribution (risk profile curve) is usually not obtained, only the EV.

[page 18]

Branch and outcome values must be value measures [PV works, but, for example, internal rate of return (IRR) does not], and probabilities must be assessed or calculated for all chance event outcomes.

Anmerkungen

The source is not given.

Sichter
(Klgn) Schumann



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