A Fuzzy Logic Algorithm for Optimizing the Investment Decisions within Companies

Boloș, Marcel Ioan; Bradea, Ioana-Alexandra; Delcea, Camelia

doi:10.3390/sym11020186

Cited by 24 publications

(16 citation statements)

References 49 publications

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“…Zadeh's fuzzy set theory [24] addresses the problem of how to understand and manipulate imperfect knowledge. It has been used in various applications [25][26][27][28]. Recent investigations have attracted more attention on combining covering-based rough set and fuzzy set theories.…”

Section: Introductionmentioning

confidence: 99%

A New Type of Single Valued Neutrosophic Covering Rough Set Model

Wang

Zhang

2019

Symmetry

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Recently, various types of single valued neutrosophic (SVN) rough set models were presented based on the same inclusion relation. However, there is another SVN inclusion relation in SVN sets. In this paper, we propose a new type of SVN covering rough set model based on the new inclusion relation. Furthermore, the graph and matrix representations of the new SVN covering approximation operators are presented. Firstly, the notion of SVN β 2 -covering approximation space is proposed, which is decided by the new inclusion relation. Then, a type of SVN covering rough set model under the SVN β 2 -covering approximation space is presented. Moreover, there is a corresponding SVN relation rough set model based on a SVN relation induced by the SVN β 2 -covering, and two conditions under which the SVN β 2 -covering can induce a symmetric SVN relation are presented. Thirdly, the graph and matrix representations of the new SVN covering rough set model are investigated. Finally, we propose a novel method for decision making (DM) problems in paper defect diagnosis under the new SVN covering rough set model.

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Section: Introductionmentioning

confidence: 99%

A New Type of Single Valued Neutrosophic Covering Rough Set Model

Wang

Zhang

2019

Symmetry

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“…Peng et al [10] developed a new outranking approach for multi-criteria decision-making problems. This method was developed in a simplified neutrosophic environment where the truth-membership degree, indeterminacy-membership degree, and falsity-membership degree were subsets in [0,1].…”

Section: State Of the Artmentioning

confidence: 99%

“…Financial markets specialists have shown a particular interest for financial assets lately, both due to returns they generate for investors and also because they can predict the future evolution of financial performance [1]. Researchers in financial markets, starting with Markowittz [2], developed the modern theory of financial asset portfolios, focusing on the financial assets return (R a ) and on the financial assets risk (σ 2 a ).…”

Section: Introductionmentioning

confidence: 99%

Modeling the Performance Indicators of Financial Assets with Neutrosophic Fuzzy Numbers

2019

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This research sets the basis for modeling the performance indicators of financial assets using triangular neutrosophic fuzzy numbers. This type of number allows for the modeling of financial assets performance indicators by taking into consideration all the possible scenarios of their achievement. The key performance indicators (KPIs) modeled with the help of triangular fuzzy neutrosophic numbers are the return on financial assets, the financial assets risk, and the covariance between financial assets. Thus far, the return on financial assets has been studied using statistical indicators, like the arithmetic and geometric mean, or using the financial risk indicators with the help of the squared deviations from the mean and covariance. These indicators are well known as the basis of portfolio theory. This paper opens the perspective of modeling these three mentioned statistical indicators using triangular neutrosophic fuzzy numbers due to the major advantages they have. The first advantage of the neutrosophic approach is that it includes three possible symmetric scenarios of the KPIs achievement, namely the scenario of certainty, the scenario of non-realization, and the scenario of indecision, in which it cannot be appreciated whether the performance indicators are or are not achieved. The second big advantage is its data series clustering, representing the financial performance indicators by which these scenarios can be delimitated by means of neutrosophic fuzzy numbers in very good, good or weak performance indicators. This clustering is realized by means of the linguistic criteria and measuring the belonging degree to a class of indicators using fuzzy membership functions. The third major advantage is the selection of risk mitigation analysis scenarios and the formation of financial assets’ optimal portfolios.

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“…Other practical applications including fuzzy logic that can be mentioned are: the design of a warning system and fire monitoring system for smart buildings [15], modeling pedestrian dynamic behavior [16], choosing the location for power plants [17], supplier evaluation and selection [18], investment decision optimization [19], assessing the commercial viability of technology start-up businesses [20], e-commerce regional cooperation [21], material selection procedures [22], risk assessment [23], cooperative mobile robots' learning [24], and alternatives' evaluation [25], etc. As for the theoretical aspects related to fuzzy logic application with regard to multi-attribute decision making, more can be read in [26][27][28][29][30][31][32][33][34][35][36][37].…”

Section: Symmetry 2019 11 X For Peer Review 3 Of 18mentioning

confidence: 99%

“…Credit lending policies are geared towards analyzing the company's financial performance both in terms of operating activity and projects' profitability. Among the most important key performance indicators (KPIs) of a company are EBITDA and CFA [19]. Both of these have a very important role in measuring and assessing a company's financial performance for bank financing as both contain information about the company's ability to repay debt to its financial creditors.…”

Section: The Ebitda and Available Cash-flow Rule And The Influence Omentioning

confidence: 99%

The Development of a Fuzzy Logic System in a Stochastic Environment with Normal Distribution Variables for Cash Flow Deficit Detection in Corporate Loan Policy

2019

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This paper develops a Mamdani fuzzy logic system (FLS) that has stochastic fuzzy input variables designed to identify cash-flow deficits in bank lending policies. These deficits do not cover the available cash-flow (CFA) resulting from the company's operating activity. Thus, due to these deficits, solutions must be identified to avoid companies' financial difficulties. The novelty of this paper lies in its using stochastic fuzzy variables, or those categories of variables that are defined by fuzzy sets, characterized by normally distributed density functions specific to random variables, and characterized by fuzzy membership functions. The variation intervals of the stochastic fuzzy variables allow identification of the probabilistic risk situations to which the company is exposed during the crediting period using the Mamdani-type fuzzy logic system. The mechanism of implementing the fuzzy logic system is based on two stages. The first is based on the determination of the cash-flow requirements resulting from loan reimbursement and interest rates. This stage has the role of determining the need for financial resources to cover the liabilities. The second stage is based on the identification of the stochastic fuzzy variables which have a role in influencing the cash flow deficits and the probability values estimation of these variables taking into account probability calculations. Based on these probabilistic values, using the Mamdani fuzzy logic system, estimations are computed for the available cash-flow (the output variable). The estimated values for CFA are then used to detect probability risk situations in which the company will not have enough resources to cover its liabilities to financial creditors. All the FLS calculations refer to future time periods. Testing and simulating the fuzzy controller confirms its functionality.

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A Fuzzy Logic Algorithm for Optimizing the Investment Decisions within Companies

Cited by 24 publications

References 49 publications

A New Type of Single Valued Neutrosophic Covering Rough Set Model

A New Type of Single Valued Neutrosophic Covering Rough Set Model

Modeling the Performance Indicators of Financial Assets with Neutrosophic Fuzzy Numbers

The Development of a Fuzzy Logic System in a Stochastic Environment with Normal Distribution Variables for Cash Flow Deficit Detection in Corporate Loan Policy

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