Ranking challenges, risks and threats using Fuzzy Inference System

,; Božanić, Darko; Tešić, Duško; ,; Puška, Adis; ,; Štilić, Anđelka; ,; Muhsen, Yousif Raad; ,

doi:10.31181/dmame622023926

Cited by 17 publications

(3 citation statements)

References 43 publications

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“…Contrarily, while having extensive financial experience, their performance in forecasting risks during crises is low, forcing many to quit their positions. In times of ambiguity, it is crucial to have a high-level administrative framework [66,86]. The findings of this study contained a necessary implication in terms of the financial sustainability of banks.…”

Section: Discussionmentioning

confidence: 74%

Benchmarking Methodology of Banks Based on Financial Sustainability Using CRITIC and RAFSI Techniques

Ali,

Hussain,

Alnoor

et al. 2023

Decis. Mak. Appl. Manag. Eng.

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This study aims to determine the influence of financial risks on the financial sustainability of Iraq banks, in contrast considering the moderator role of financial expertise and the risk of directors’ riskiest directorships. Moreover, the investment attractiveness has also been accounted for specifying the mediating impact between the moderator variables and financial sustainability. Robust random effect model was employed to investigate the determinants of financial sustainability for the listed banks on the Iraqi stock trade and the Iraqi financial marketplace. In addition, multicriteria decision-making techniques were utilized to rank the banks from the best to worst based on financial sustainability. This study has examined 19 Iraqi banks over 14 years from 2007 to 2020 (266 observations), during the development of the Iraqi corporate governance cod. The findings indicate that financial risk has a negative influence on the financial sustainability of Iraq. Furthermore, the link between financial risks and financial sustainability is fully mediated by investment attractiveness. In this context, financial expertise and the relinquishing of directors’ riskiest directorships have an interactive role in the link among investment attractiveness and financial sustainability indicators. Multi-criteria decision making (MCDM) results revealed that the best bank in terms of financial sustainability is BTRI and the worst bank is BNOR. The findings enable managers to develop strategies to mitigate financial risk in phases and prepare for future unpredicted risks. Hence, the results provided an insight to practitioners regarding the best banks in terms of financial sustainability in order to invest and deal with them.

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Section: Discussionmentioning

confidence: 74%

Benchmarking Methodology of Banks Based on Financial Sustainability Using CRITIC and RAFSI Techniques

Ali,

Hussain,

Alnoor

et al. 2023

Decis. Mak. Appl. Manag. Eng.

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“…The ideal portfolio is one in which risk and return have been precisely balanced. Investments with the least amount of risk for a specific return, or investments having the largest potential returns, are balanced in the optimum portfolio to achieve these goals (Bozanic et al, 2023). Therefore, risk taking needs to be analyzed, assessed and monitored.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Method for Optimizing Portfolios Using a Combined Monte Carlo and Markowitz Model: Approach on Python

Mallieswari,

Palanisamy,

Senthilnathan

et al. 2024

Economics

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The main of the study is to comprehend how the mean variance efficient frontier method may be used in conjunction with Markowitz portfolio theory to produce an optimal portfolio. The study uses daily observations 8 pharma companies closing price namely Auropharma, Granules, Glaxo, Lauruslabs, Pfizer, Sanofi and Torntpharma. Further, Nifty pharma index is considered as benchmark index to check the performance of the chosen companies. The study chosen the reference period from 2020 to 2023 and required data has been extracted from the National Stock Exchange (NSE). This research is based on implementing a stochastic method for efficient portfolio optimisation employing a blended Monte Carlo and Markowitz model. In order to forecast the price of these indices in the future and to determine the likelihood of profit or loss while investing in a portfolio of stocks representing the aforementioned indices, the study also uses Monte Carlo simulation. The study involves two algorithms, namely the deterministic optimisation algorithm, which uses Markowitz Portfolio Theory, and the probabilistic optimisation algorithm, which uses Monte Carlo simulation. The study employed correlation matrix to find the exist relationship between the chosen companies and benchmark index. Also, expected return and volatility has been identified with the help of standard deviation using Python. The study found that the NIFTY Pharma index offers a higher return of 14.35. In addition to this, NIFTY Pharma portfolio’s volatility is considerably higher. The study concludes that the NIFTY pharma portfolio is more suitable for those investors who have an appetite for risk.

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“…As a flexible, interpretable machine learning model, fuzzy neural networks (FNNs) have been widely used in various fields, such as image processing [1], fuzzy control [2,3], ranking challenges, risks and threats [4], actual classification and prediction [5][6][7][8], and so on. One of the most commonly used FNN structures is the Takagi-Sugeno-Kang (TSK) [9] fuzzy system, also called TSK neuro-fuzzy system because it can be represented as a neural network [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

First-Order Sparse TSK Nonstationary Fuzzy Neural Network Based on the Mean Shift Algorithm and the Group Lasso Regularization

Zhang,

Wang,

Gong

et al. 2023

Mathematics

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Nonstationary fuzzy inference systems (NFIS) are able to tackle uncertainties and avoid the difficulty of type-reduction operation. Combining NFIS and neural network, a first-order sparse TSK nonstationary fuzzy neural network (SNFNN-1) is proposed in this paper to improve the interpretability/translatability of neural networks and the self-learning ability of fuzzy rules/sets. The whole architecture of SNFNN-1 can be considered as an integrated model of multiple sub-networks with a variation in center, variation in width or variation in noise. Thus, it is able to model both “intraexpert” and “interexpert” variability. There are two techniques adopted in this network: the Mean Shift-based fuzzy partition and the Group Lasso-based rule selection, which can adaptively generate a suitable number of clusters and select important fuzzy rules, respectively. Quantitative experiments on six UCI datasets demonstrate the effectiveness and robustness of the proposed model.

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Ranking challenges, risks and threats using Fuzzy Inference System

Cited by 17 publications

References 43 publications

Benchmarking Methodology of Banks Based on Financial Sustainability Using CRITIC and RAFSI Techniques

Benchmarking Methodology of Banks Based on Financial Sustainability Using CRITIC and RAFSI Techniques

A Stochastic Method for Optimizing Portfolios Using a Combined Monte Carlo and Markowitz Model: Approach on Python

First-Order Sparse TSK Nonstationary Fuzzy Neural Network Based on the Mean Shift Algorithm and the Group Lasso Regularization

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