Adverse Effects Of Uncertainty Research Articles

MODELING STOCHASTIC ADVERSE EFFECTS OF CBN 2023 REDESIGNED NAIRA NOTES POLICY ON RURAL FARMERS IN NIGERIA

The recent Central Bank of Nigeria (CBN) 2023 redesigned naira notes is of good benefits to strengthen the economy of the country by checking counterfeiting and hoarding of large volume of banknotes by the public. Despite all the efforts made by the CBN for citizens to enjoy the benefits of this implementation, most rural farmers were faced with adverse effects of uncertainties in the production and marketing of their agricultural produce due to lack of redesigned new naira notes in circulation. The adverse effects of these uncertainties are modeled as Advanced Stochastic Time-Delay Differential Equation (ASTDDE). The modeled equation is solved using Extended Second Derivative Block Backward Differentiation Formulae Method (ESDBBDFM) without the use of interpolation techniques in the evaluations of the delay term and noise term. In comparing the numerical results of this method with other existing methods in literature, the newly developed mathematical expressions for the evaluations of the delay term and the noise term in solving ASDDEs with the discrete schemes of ESDBBDFM gives better results for step number than step numbers and 3 by producing Least Minimum Absolute Random Error (LMARE) in a Lower Computational Processing Unit Time (LCPUT) faster than other existing methods that applied interpolation techniques in evaluations of the delay term and the noise term.

A Robust Minimum-Cost Consensus Model With Uncertain Aggregation Weights Based on Data-Driven Method

Due to the existence of cognitive bias, it is difficult to accurately obtain the decision-makers’ weights in the aggregating process, which leads to great fluctuations in the optimal decision-making. Therefore, the purpose of this article is to establish a data-driven robust cost consensus model induced by uncertain weights to deal with the adverse effects of uncertainty in the aggregating process. First, the probability density function of the uncertain weight in the aggregation operator is obtained by the kernel density estimation method, and the confidence level is used to control the perturbation range of the uncertainty. Second, a new uncertainty set based on the data-driven robust optimization method is established, which is called the flexible uncertainty set, and two classes of data-driven robust consensus models under uncertain environments are proposed. In the application of power emergency management, by using the data-driven method to traverse the historical data of weights, the new model can effectively deal with the uncertainty caused by the deviation and improve the quality of the aggregation operator, which proves the applicability of the proposed model.

Analysis of Marketing Forecasting Model Based on Genetic Neural Networks: Taking Clothing Marketing as an Example

In this paper, according to the requirements of clothing sales forecast, the forecast model of clothing sales is constructed. Through the design of cyclic structure, the adverse effects of uncertainty, hysteresis, and time-varying factors of the predicted object are overcome, and the prediction procedure is theoretically standardized. Aiming at the shortcomings of traditional NN sales forecasting algorithm, such as low learning efficiency, slow convergence speed, and easy to fall into local minimum, this paper puts forward some improvement measures. Adaptive learning efficiency is used to improve the effectiveness and convergence of the algorithm, additional momentum method is used to improve the adaptability of the algorithm, and improved GA is used to optimize the weights of NN. Improve the global optimization characteristics of GA to achieve the purpose of fast optimization and accurate prediction. Finally, an example is used to verify the algorithm. On this basis, the correlation adaptability and prediction accuracy of clothing prediction methods are compared and analyzed, combined with the theoretical analysis of various methods, to explore the practical applicability of various methods under different prediction conditions. It provides an important basis for the decision-making of garment enterprises.

Formation of Performance Indicators of System-forming Regional Enterprises under Uncertainty

A modern enterprise must be ready for any changes that may arise under the influence of both internal and external influences, including uncertain ones. It must be able to get out of this unfavorable situation as quickly as possible. The proposed study is to confirm the hypothesis of the relationship between the level of uncertainty and the performance of an enterprise based on the use of the preventive preparedness regime. The work solves the following tasks: assessment of the level of uncertainty of the external environment, selected as an indicator of entropy; a methodology for choosing the financial and economic indicators of an enterprise for the formation of a preventive readiness mode is proposed; the existence of a relationship between the level of uncertainty and indicators of the preventive readiness regime has been empirically confirmed. The authors propose a variant of the enterprise’s transition to a state of so-called “maximum readiness”, in which it will feel “comfortable”. The concept of enterprise management in a situation of external uncertainty is presented. In accordance with this approach, the results of the analysis of financial, economic and social indicators of economically significant automobile enterprises located in various regions of Russia, included in the list of backbone and influencing the development of the region, have been investigated. The selected indicators were considered from the point of view of the possibility of applying to them the state of the maximum readiness mode in conditions of uncertainty, which provides an adaptive state of the enterprise and, accordingly, will allow maintaining and even increasing its position in the market in the presence of uncertainties. The proposed concept can be recommended to all firms and enterprises with an innovative focus since they are more susceptible to the adverse effects of uncertainties.

Uncertainty and the Cost of Bank vs. Bond Finance

How does heightened uncertainty affect the costs of raising finance through the bond market and through bank loans? Empirically, I find that a rise in uncertainty is accompanied by an increase in corporate bond yields and a decrease in bank lending rates. This new stylized fact can be explained in a model with costly state verification and a special informational role for banks. In contrast to bond investors, banks acquire additional costly information about borrowers in times of uncertainty in order to reduce uncertainty. Having this information, the lending relationship becomes more valuable to the bank, resulting in a lower lending rate so that the relationship is not put at risk. The cost of bond finance increases because bond investors demand to be compensated for the increased risk of firm default. These findings suggest that the adverse effects of uncertainty are mitigated for firms that rely on bank finance as long as banks are highly capitalized.

Foreign markets expansion for air medical transport business

Air medical transport has gained increasing popularity in modern society. However, the global market of this industry has not been fully explored either in theory or in practice. This research assesses the medical aviation market and identifies the most suitable regions for a private air transport company to expand its business on a global scale. We combine the analytic hierarchy process (AHP) and the grey number (GN) theorem to analyse the potential foreign market. Compared with conventional methods, the proposed model mitigates the adverse effects of uncertainty while providing a practical approach that considers management’s subjective judgements. The integrated model is comprehensive and flexible for assessing the demand for air medical service in different parts of the world. In addition to the air jet service industry, the GN-AHP model can be generalised to evaluate many other markets and industries.

Framework for the Robust Design Optimization of an Airframe and its Engines

The optimization studies for the next generation of aircraft and engine families are typically associated with the insertion of technological advances to improve fuel efficiency, noise levels, and overall aircraft economics. In the conceptual study phase, the optimization process needs to address the uncertainties related to the anticipation of market requirements and potential technological advances at the time of entry into service, along with the fidelity of the modeling applied in the analysis. The current paper presents a framework using a robust design optimization methodology to mitigate these uncertainties when an airframe and its engines are designed jointly. The optimization of a seventy passenger aircraft illustrates the approach with a formulation seeking to optimize the design, while attenuating its sensitivity to the potentially adverse effects of uncertainties.

Predictability-Robust Dynamic Portfolio Choice

Dynamic portfolio choice crucially depends on the predictability of returns. In contrast to its importance, confirmation of the existence of predictability is lacking. We consider a robust investor who arms herself against the adverse effects of uncertainty about predictability but also exploits the benefits of predictability insofar it is significant. We show that robust dynamic portfolio choice does not exhibit the extreme risky investment of traditional and Bayesian portfolios, is less volatile and features intertemporal hedging demands. We also show the worst plausible form of predictability for the robust portfolio result. This worst case depends on the horizon, initial state, the portfolio, and the relations among the predictor variables. For long term investment the worst case features a low long term average of the predictor variable and a high exposure to this low average.

Strategic Commitment Versus Flexibility in a Duopoly With Entry and Exit

We present a continuous-time real options game in which two firms must decide at each instant of time whether to be in or out of a market that expands up to a random maturity date and contracts thereafter. Firms differ only in the opportunity costs of usage of the assets they employ (e.g., owing to different redeployment or resale values), so their investments are not equally recoverable. The paper disentangles the numerous aspects that affect the order of entry and exit, and it challenges the common wisdom that a firm with a sunk cost higher than that of its sole competitor is credibly committed to fight for the market (if necessary) and is worse shielded against the adverse effects of uncertainty. Our analysis also shows that the destructive effect of the threat of preemption on option values may be softened if the rival's commitment to remain active after investing is not credible.