Abstract
It is still not easy to define the content of the Financial Management of Companies but, even though we have no desire to create restrictions, we shall conceptualize it as the scientific discipline which attempts to optimally assign the scarce financial resources in a company both from the external and internal perspectives, that is to say, financial markets and financial management, respectively. This definition follows the most accepted meaning of Economy as the science which studies human behaviour as a relation between ends and scarce means which have alternative uses.
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References
References
Suárez (1998, 28)
Femández (1991, 346–347)
Kaufrnarm and Gil (1986,70)
\(\mathop q\limits_ \sim\) and \(\mathop r\limits_ \sim\)are positive fuzzy numbers, that is to say, \(\forall a \in \left[ {0,1} \right],\underline {q\left( a \right)} r\underline {\left( a \right)} >0\)
For the lower limit \(\mathop E\nolimits_l = \frac{{\underline {\mathop C\nolimits^* \left( a \right)} - \underline {C\left( a \right)} }}{{\underline {C\left( a \right)} }}\)and will be calculated for upper limits in a simila way.
Terceño, Márquez and Belvis (1997)
Not all methods for comparing fuzzy numbers are transitive, in the sense that the condition \(\mathop A\limits_ \sim >\mathop B\limits_ \sim\)and\(\mathop B\limits_ \sim >\mathop C\limits_ \sim\)may be fulfilled, and yet \(\mathop A\limits_ \sim >\mathop C\limits_ \sim\)may not be true. As we have said, this means it is not always possible to carry out either a complete comparison, or, consequently, a total ranking of all the fuzzy numbers in which we are interested.
Comparison of n fuzzy numbers requires n(n-1)/2 comparative operations, which presents operational problems if n is a high number.
Any comparison problem can be solved when taken as a ranking problem. llte opposite is not always true.
In addition to those cited in the text, the most Widely-known methods in fuzzy literature may be found in Freeling (1980, 341–354), McCahon and Lee (1990, 159–181) and Kitainik (1993, 109–136), although this list does not pretend to be exhaustive.
As a translation of ‘indice de consentimiento’, usecl by Kaufmann and Gil (1987).
In some ways, the index of consent that we have presented is a method of ordering fuzzy numbers.
We are aware that the NPV and IRR models for investment selection as presented represent another series of limitations (choice of the calculation and reference interest rates respectively, reinvesnnent of intermediate cash flows at the interest rate or at the IRR itself, effects of inflation and taxes, inconsistency of the IRR criterion, interaction between projects, interrelation between the investment projects and their financing, optimum time distribution of the investments, etc.) We will focus the chapter exclusively on the inconveniences specified.
In the example, we got the same ranking for a*=0 as for a*=0,2 by all the ranking methods used. Thus the thre,e investment projects under consideration offer results that are easily differentiated. Clearly, it does not have to be that way, and will depend on the actual form of the membership functions of the fuzzy numbers to be ranked.
We take the triangular approximation of the result obtained, for its ease of operation and for the minimal error made in its calculation.
It is generally accepted that the value of a company can be identified with the discounted value of future dividends which participation in the capital will offer. In this way, \(\sum\nolimits_{\forall i} {\mathop w\nolimits_i } \mathop D\nolimits_i\)can be understood as the increase in the company’s value which will result from the current investment progranune.
\(\mathop A\limits_ \sim \leqslant {}_{a*}\mathop B\limits_ \sim\)in the strict sense if \(\overline {A\left( {\mathop a\nolimits^* } \right)} \leqslant \underline {B\left( {\mathop a\nolimits^* } \right)}\)However, this is not usually taken into consideration in PLP as it is an excessively restrictive criterion.
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Terceño, A., de Andrés, J., Barberà, M.G., Lorenzana, T. (2001). Investment management in uncertainty. In: Gil-Aluja, J. (eds) Handbook of Management under Uncertainty. Applied Optimization, vol 55. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0285-8_7
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