The assessment of cash flow forecasting

Linares‐Mustarós, Salvador; Comalat, Joan Carles Ferrer i; Cassú‐Serra, Elvira

doi:10.1108/k-03-2013-0060

Cited by 10 publications

(6 citation statements)

References 16 publications

(15 reference statements)

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“…It is also possible that we ourselves have to own the correct distributions, as the selected spreadsheet has no built-in tool for that move. For example, Linares-Mustarós et al (2013) suggested using fuzzy logic, which is not available directly in most programmes, for estimating corporate liquidity problems. They point out: this approach provides a practical estimate of the frequency of payment difficulties and, with some learning, this approach can be easily adapted to the models of spreadsheet programmes widely used in corporate environment, as well.…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

Risk analysis in corporate financial modelling

Juhász¹

2020

Economy & finance

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Models applied in corporate finance can typically use only one segment of our knowledge about the future: expected values. In the light of this, the final results do not reflect the uncertainty related to the input parameter values. Despite having several available tools to allude to the uncertainty we were forced to hide when we are presenting the results to the decision maker, this process and the interpretation of output data often include misunderstandings and mistakes. When providing an overview of the risk analysis toolkit, this article focuses on exploring such misunderstandings and mistakes and describes other risk factors which influence the outcome of the modelling process. Based on the literature review, it seems that a kind of conversion between different risks exits: theoretically more accurate models may be more sensitive to estimation errors of input parameters and set much higher requirements towards modellers. JEL codes: C6, D8, G17, G32Decisions on corporate finance issues require information about the future, however, such information is usually far from being certain. Consequently, we can rely only on various forecasts and estimates. At the same time, almost none of the indicators and formulas supporting decisions in corporate finance use expected distributions or value ranges, but suppose the accurate knowledge of specific values. In view of the above, our expectations about the future usually have to be concentrated in one number (in most cases, in the expected value).This article examines the available means of displaying the uncertainty about input parameters in the final result of calculations in spite of the above, supporting risk-conscious decision-making. Agreeing with the main message of the study 1 Péter Juhász, Associate Professor with habilitation at Corvinus University of Budapest.

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Section: Monte Carlo Simulationmentioning

confidence: 99%

Risk analysis in corporate financial modelling

Juhász¹

2020

Economy & finance

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“…The fact that the figure contains gray indicates that this subset cannot be considered as a classic subset of the reference set, since there could only be black and white colors, symbolizing that either the object meets a certain property or does not meet it. The acceptance of the fundamentals of the fuzzy subset theory allows us to find better solutions to general problems more simply for the same type of modeling (Linares-Mustarós, Ferrer-Comalat & Cassú-Serra, 2013;Linares-Mustarós, Merigó & Ferrer-Comalat, 2015;Linares Mustarós, Ferrer Comalat, Corominas Coll & ---Merigó,2019;Ferrer-Comalat, Linares-Mustarós & Corominas-Coll, 2016, 2018Corominas, Ferrer-Comalat, Linares-Mustarós & Bertran, 2017), and to create new techniques that are currently used in various industrial applications, such as control systems used to focus video cameras or for vehicle brakes (Terán et al, 1987;Kaufmann & Gupta, 1988).…”

Section: Fuzzy Subset Theorymentioning

confidence: 99%

Evolutionary positioning of outsourcing in the local public administration

López

Linares

Viñas

2019

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Purpose: This work intends to establish a new methodology to quantitatively measure the initial position and evolution of outsourcing in a public administration.Design/methodology: Product of the generalization of a methodological tool based on the fuzzy set theory "index of maximum and minimum level," a method is designed to evaluate the evolution of the positioning of outsourcing in a public administration. Findings:The proposed model that is presented opens up the possibility to study the evolution of public administration in terms of the actions that have been taken to reach an ideal outsourcing position, which would allow managers to create points of control at various stages to ensure the success of the actions taken. Research limitations/implications:The limitations to the proposed method are directly related to the cases established in the fuzzy set theory. As an example, having to choose a specific value for the ideal values in an outsourcing strategy implies a regression to the concept of certainty. The work shows no examples of how to overcome these problems, although it does offer some ideas on how to minimize them. Practical implications:This work offers a tool to help determine the degree to which the objectives have been reached that are established within a public administration with regard to actions associated with outsourcing. Social implications:The ability to establish a distance from an ideal value means that citizens can numerically analyze whether the actions taken by the management team have improved or worsened the position of the public administration in terms of its outsourcing actions.Originality/value: The work offers two original proposals. First, it offers an expansion of an index customarily used to calculate the distance from an ideal position. Second, it offers a context in which to demonstrate the usefulness of the index.

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“…First, given the complexity of reality, a decision must always be made regarding the choice of variables, either by reasoning why they are considered essential to the model [2,3] or through statistical studies, using a data set to look for variables that can be deleted [4]. Second, there is the decision of what kind of relationship the model should represent between the variables [5][6][7]. The choice of relationships is made while waiting for the model to be able to predict behaviors or quantify aspects of the system in question.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Economic Dynamic Model

2021

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In the study presented here, fuzzy logic was used to analyze the behavior of a model of economic dynamics that assumes income to be in equilibrium when it is composed of consumption and investment, that is, when savings and investment are equal. The study considered that consumption and savings depend on the income of the previous period through uncertain factors, and, at the same time, that investment is an uncertain magnitude across various periods, represented as a fuzzy number with a known membership function. Under these conditions, the model determines the factor of income growth and investments required to maintain equilibrium, as well as the uncertain values of income for the different periods, expressed through fuzzy numbers. The study also analyzes the conditions for their convergence and the fuzzy value that income represents in equilibrium.

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The assessment of cash flow forecasting

Cited by 10 publications

References 16 publications

Risk analysis in corporate financial modelling

Risk analysis in corporate financial modelling

Evolutionary positioning of outsourcing in the local public administration

A Fuzzy Economic Dynamic Model

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