Ranking-based MCDM models in financial management applications: analysis and emerging challenges

Optimized backpropagation neural network for risk prediction in corporate financial management

Corporate financial management is responsible for constructing, optimizing, and modifying finance-related structures for an unremitting function. The finance optimization model incorporates risk prediction and fund balancing for distinguishable corporate operations. This risk prediction is handled using sophisticated computing models with artificial intelligence and machine learning for self-training and external learning. Therefore, this article introduces a Backpropagation-aided Neural Network for designing an Optimal Risk Prediction (ORP-BNN) to pre-validate existing and new financial imbalances. The risk prediction model is designed to cope with corporate standards and minimum riskless financial management. This is designed as a linear snowfall model wherein the BNN decides the significance between fund allocation and restraining. The snowfall model significantly relies on allocation or restraining, which is achieved by assigning significant weights depending on the previous financial decision outcome. The weight factor is determined using gradient loss functions associated with the computing model. The training process is pursued using different structural modifications used for successful financial management in the past. In particular, the risk thwarted financial planning using a snowfall-like computing model, and its data inputs are used for training optimization. Therefore, the proposed model's successful risk mitigation stands high under prompt decisions.

The Establishment of Risk Evaluation Index System for Small- and Medium-Sized Agency Bookkeeping Companies

In recent years, China's economy has developed rapidly; many small companies have risen rapidly; and the tax system has become more and more standardized. Because many small businesses cannot afford to hire full-time accountants, they opt to outsource accounting services, giving small- and medium-sized bookkeeping firms a large market space. However, these opportunities also bring huge operational risks to small- and medium-sized bookkeeping companies. The purpose of this research is to help such enterprises carry out risk management and reduce operational risks. This study uses an analytic hierarchy process and a fuzzy comprehensive assessment approach to successfully combine quantitative and qualitative analysis and create a multilevel analysis structure model of the risk management evaluation index system of small- and medium-sized agency accounting firms. The structural model is verified by a case, the specific risk score of each factor is calculated through the scores of 20 experts, and the importance of risk is judged according to the size of the score, indicating that the structural model is feasible.

A Study on Portfolio Selection Based on Fuzzy Linear Programming

Portfolio management is the allocation of funds in the hands of the investor to ensure minimum risk and maximum profitability among the existing securities. In the finance sector related to portfolio management, many approaches, theories, and models have been developed. In this study, a new model is proposed. The basic objective of this model is to minimize the risk of portfolio while maximizing expected return of the portfolio. The proposed model is based on the Mean Absolute Deviation Model (MAD) proposed by Konno-Yamazaki. Considering the uncertainty of expected returns in the Mean Absolute Deviation Model, the problem was remodeled with fuzzy logic approach. In the application section, the proposed model has been applied to two different indexes, namely Istanbul Stock Exchange (ISE-30) and Standard & Poor’s 500 (S&P 500). In application, 64-month return movements of stocks traded in both indexes between 01.01.2016-30.04.2021 were used. In order to examine the effectiveness of the model, firstly, monthly return data for both indexes were modeled with the Mean-Absolute Deviation method and the Mean Variance method. Then, the same data were modeled with the proposed method. In the conclusion section of the study, the results obtained from each method were compared.

Objective Criticism and Negative Conclusions on Using the Fuzzy SWARA Method in Multi-Criteria Decision Making

The quality of output or decision-making depends on high-quality input data, their adequate evaluation, the application of adequate approaches, and accurate calculation. In this paper, an objective criticism of applying the fuzzy SWARA (step-wise weight assessment ratio analysis) method based on the Chang TFN (triangular fuzzy number) scale is performed. Through research, it has been noticed that a large number of studies use this approach and, as an epilogue, there are wrong decisions based on inconsistent values in relation to the initial assessment of decision-makers (DMs). Seven representative studies (logistics, construction industry, financial performance management, and supply chain) with different parameter structures and decision matrix sizes have been singled out. The main hypothesis has been set, which implies that the application of this approach leads to wrong decisions because the weight values of the criteria are incorrect. A comparative analysis with the improved fuzzy SWARA (IMF SWARA) method has been created and a number of negative conclusions has been reached on using the fuzzy SWARA method and the Chang scale: Primarily, that using such an approach is impossible for two or more criteria to have equal value, that allocating TFN (1,1,1) leads to criteria values that are inconsistent with expert evaluation, that the last-ranked criteria in the fuzzy SWARA method have no influential value on the ranking of alternatives, that there is a great gap between the most significant and last-ranked criteria, and that the most significant criterion has a huge impact on the evaluation of alternative solutions and decision making. As a general conclusion, it is given that this approach is not adequate for application in problems of multi-criteria decision making because it produces inadequate management of processes and activities in various spheres.

A Comparative Analysis of Multi-Criteria Decision-Making Methods for Resource Selection in Mobile Crowd Computing

In mobile crowd computing (MCC), smart mobile devices (SMDs) are utilized as computing resources. To achieve satisfactory performance and quality of service, selecting the most suitable resources (SMDs) is crucial. The selection is generally made based on the computing capability of an SMD, which is defined by its various fixed and variable resource parameters. As the selection is made on different criteria of varying significance, the resource selection problem can be duly represented as an MCDM problem. However, for the real-time implementation of MCC and considering its dynamicity, the resource selection algorithm should be time-efficient. In this paper, we aim to find out a suitable MCDM method for resource selection in such a dynamic and time-constraint environment. For this, we present a comparative analysis of various MCDM methods under asymmetric conditions with varying selection criteria and alternative sets. Various datasets of different sizes are used for evaluation. We execute each program on a Windows-based laptop and also on an Android-based smartphone to assess average runtimes. Besides time complexity analysis, we perform sensitivity analysis and ranking order comparison to check the correctness, stability, and reliability of the rankings generated by each method.