Juan Flores, Federico González, and Beatriz Flores
juanf@zeus.umich.mx , fsantoyo@zeus.umich.mx
Fac. Ing. Eléctrica ,Fac. Contabilidad. y Administración.
Universidad Michoacana de San Nicolás de Hidalgo Morelia, México
Abstract: We present a methodology to perform Financial Analysis with as much information as available. The problem we solve is the assessment of investment projects; we are to determine whether on not an investment project is profitable. We perform that task at the qualitative, semi-quantitative, and quantitative levels, depending on how much information there is at hand. The main advantage of the method presented here, is that it can deal with very little information about quantities, still yielding some results. The least piece of information the system can work with is a set of Order of Magnitude Relations (omrs) among the model variables. If those Omrs are sufficient to disambiguate the results of our model, we can tell whether an investment project is worthwhile or not. The idea is that our system remembers what has been said, and takes any additional information to refine its results. If all we have is a few omrs it may or may not determine the outcome of our investment project analysis. Later on, we provide the system with some more information (perhaps imprecise), and the results will be refined. The more precise the provided information is, the more accurate the results will be. If at the end, all provided variables are precise, the results are the same as performed via traditional analysis. This methodology takes ideas from the field of Qualitative Reasoning, some algorithms from graph theory and information structures, and the traditional interval computation arithmetic to perform operations under uncertainty.
Keywords: order of magnitude reasoning, approximate reasoning, qualitative reasoning, interval computation, financial analysis.
Full Paper (DOC 104 KB)