Financial Analysis in uncertainty

  • Anna Maria Gil-Lafuente
Part of the Applied Optimization book series (APOP, volume 55)

Abstract

It is an unquestionable fact that the social surroundings in which businesses operate have an obvious influence on their financial structures at all times and also on the prospects for their evolution in the future.

Keywords

Entropy Income Expense Convolution Lution 

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References

References

  1. 1.
    It is sufficiently well known that the quotient of triangular fuzzy numbers (likewise the product) is not normally a triangular fuzzy number.Google Scholar
  2. 2.
    As is particularly well known, uncertainty, this type of disorder, can be valued by means of the dis-tance relative to order.Google Scholar
  3. 3.
    As is sufficiently well known, a fuzzy number is a fuzzy sub-set of a referential of real numbers, with the characteristics of normality and convexity.Google Scholar
  4. 4.
    See Kaufmann, A. and Gil Aluja, J.: Técnicas operatives de gestión para el tratamiento de la incer-tidumbre. Barcelona, Hispano Europea, 1987, page 52.Google Scholar
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    Mao, James G.T.: Financial Analysis, Buenos Aires, El Ateneo, 1074, page 325.Google Scholar
  6. 6.
    The generalisation to more sources with varied cost is immediate.Google Scholar
  7. 7.
    Among other, consult the work of Suárez Suárez, S.: Decisions óptimas de inversión y financiación en la empresa. Madrid. Pirámide, 1985.Google Scholar
  8. 8.
    See, for example, Gil Lafuente, A.M.: “Estrategias secuenciales para la captación de medios finan-cieros ”. Proceedings of the SIGEF Congress. Vol I. Buenos Aires, Argentine, 1996. Communication paper 2.11.Google Scholar
  9. 9.
    The concept of a neuronal graph was coined in the work of professors Kaufmann, A. and Gil-Aluja, J.: “Grafos Neuronales para la economίa y gestión de empresas ”. Published by Pirámide. Madrid 1995. We follow these authors in the basic aspects.Google Scholar
  10. 10.
    Gil Lafuente, A.M.: “Estrategias secuenciales para la captación de medios financieros ”. Proceedings of the III SIGEF Congress, Vol I. Buenos Aires, Argentina. November 1996. Communication paper 2.11.Google Scholar
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    In a limit case such as this one could also resort to complementary criteria for reviewing both inter-vals. We feel it is not suitable to extend ourselvs on this subject at this juncture as it has been widely treated on other occasions.Google Scholar
  12. 12.
    In this respect see: Kaufmann, A. and Gil Aluja, J.: “Técnicas operativas de gestión para el Irate-miento de la incertidumbre ”. Pub. Hispano-Europea. Barcelona 1987. Pages 393 to 405.Google Scholar
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    Gil Aluja, J.: La colocación óptima de los recursos jinancieros. El modelo ISFUNE (Investmen Selection for Fuzzy Neuronal Networks)“. Proceedings of the 1st SIGEF Congress. Reus, Spain 16–18 November, 1994. Vol 1 pages 9–44.Google Scholar
  14. 14.
    We should remember that in statistical normalisation the sum of all the elements of each row must be equal to the unit.Google Scholar
  15. 15.
    In convex weighting a determined weight is assigned to each characteristic in order to give it a differ-rent relative importance in such a way that the sum of the weights is equal to the unit.Google Scholar
  16. 16.
    What we have called the coefficient of qualification up to this point could have a denomination which would not exactly adapt itself to its arithmetical significance, from now on we will to call it coeffi-cient AG.Google Scholar
  17. 17.
    If matrix \( \left[ {\mathop P\limits_ \sim } \right] \) were to have been arrived at by the use of any method based on distances in stead of using the coefficient AG we would have to find \( \left[ {\mathop {\overline P }\limits_ \sim } \right] \) in order to operate with levels of resemblance.Google Scholar
  18. 18.
    For greater detail see Kaufmann, A and Gil Aluja, J.: Introducción a la teoria de los subconjuntos borrosos a la gestión de las empresas. Pub. Milladoiro, Santiago de Compostela, 1986 1st ed. Chap VII, pages 151 and 152.Google Scholar
  19. 19.
    This algorithm has been applied in an interesting work by Gil Aluja, J.: Modelos no numéricos de assignación en la gestión de personal. Paper presented at the II SIGEF Congress. Santiago de Compostela. November 1995 Vol 1 Congress Proceedings pages 458–466.Google Scholar
  20. 20.
    Konig, D.: Théorie der endlichen und unendlichen graphen (1916), later reprinted by Chelsea Publ. Co. New York, 1950. This work was made known by Kuhn, H.W. in an article The Hungarian met-hod for the assignment problem. Naval Research Quarterly. Vol 2 No 1–2 March-June, 1955, pages 83–98.Google Scholar
  21. 21.
    In relation to this subject, see Kaufmann, A, and Gil Aluja, J.: Técnicas de gestión de empresa. Previsiones, decisiones y estrategias. Madrid, Pirámide, 1992, page 17, where different ways of expressing subjectivity are shown.Google Scholar
  22. 22.
    The incorporation of the fundamental value in the uncertain form in no way alters or complicates the process that is followed from here on.Google Scholar
  23. 23.
    Kaufmann, A. and Gil Aluja, J.: Modelos para la investigación de efectos olvidados. Santiago de Compostela, Milladoiro, 1989.Google Scholar

Bibliography

  1. Gil Aluja, J.: Modeios no numéricos de asignación en la gestión de personal. Ponencia presentada en el II Congrespo de SIGEF. Santiago de Compostela. November 1995. Vol. I. Actas del Congreso.Google Scholar
  2. Gil Lafuente, Anna Maria: Fundamentos de análisis financiero. E. Ariel. Barcelona, 1992.Google Scholar
  3. Kaufinann, A. and Gil Aluja, J.: Introducción a la teoria de los subconjuntos borro-sos a la gestión de las empresas. Ed. Milladoiro. Santiago de Compostela, 1986.Google Scholar
  4. Kuhn, H. W.: The hungarian method for the assignment problem. Naval Research Quaterly. Vol. 2. N° 1–2. March-June, 1955.Google Scholar
  5. Gil Aluja, J.: “La colocación óptima de los recursosfinancieros. El modelo ISFUNE (Investment Selection for Fuzzy Neuronal Networks)”. Proceedings del ler Congreso SIGEF. Reus, España. 16–18 de Noviembre de 1997. Vol. 1.Google Scholar
  6. Gil Lafuente, A.M.: “Estrategias secuenciales para la captación de medios finan-cieros”. Actas del Congreso de SIGEF. Vol. I. Buenos Aires, Argentina. Noviembre, 1996.Google Scholar
  7. Kaufmann, A. and Gil Aluja, J.: “Grafos Neuronales para la econonda y gestión de empresas”. Ed. Pirámide. Madrid, 1995.Google Scholar
  8. Kaufmann, A. and Gil Aluja, J.: “Técnicas operatives de gestión para el tratamien-to de la incertidumbre”. Ed. Hispano-Europea. Barcelona, 1987.Google Scholar
  9. McCulloch, W. S. and PITTS, N.: “A logical calculus of the ideas immanent in ner-vious system”. Bulletin of Mathematical Biophysics n° 5, 1943.Google Scholar

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© Kluwer Academic Publishers 2001

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  • Anna Maria Gil-Lafuente

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