Mathematical Modeling of the Dynamics of Crisis Situations and Optimization of Capital Relocation Market Management in the Context of Globalization in the World Financial and Economic System

Khilenko, V. V.

doi:10.1007/s10559-022-00440-7

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many companies have begun to build and optimize their nancial management systems and improve their management level by fully implementing the Markov model [9] in order to adapt to the changing times. Some information may reflect a company's financial difficulties, that is, the risk outcome is predictable [10]. e ability to accurately predict the time horizon of future financial crises aids in the protection of investors' money and interests, as well as the timely avoidance of financial crises [11].…”

Section: Introductionmentioning

confidence: 99%

Reliability Optimization Analysis of Enterprise Financial Management System Using the Markov Model

Chen

2022

Mobile Information Systems

View full text Add to dashboard Cite

Financial management, as the central link in the enterprise management chain, has emerged as a critical component in achieving the company’s strategic objectives. Businesses are transitioning from a closed to an open and scalable development model to an intensive development model, which is reflected in financial management and necessitates a growth-oriented transformation. Unreliability abounds in today’s globalized environment, from financial management-oriented accounting to efficiency accounting. In addition to general supply and demand unreliability, the high degree of complexity and increased channel unreliability make companies resist the great challenge of financial management systems’ ability to face uncertain events, corporate supply chains become increasingly vulnerable, and unreliability has become a common and indispensable phenomenon in many economic and management decision problems. Due to their high data requirements and dynamic adaptability, Markov models are used as the primary forecasting tool. They do not require a large amount of historical data, but rather a detailed analysis of the recent past. This thesis examines and resolves the issues with the current financial management system through a detailed examination of the enterprise financial management system’s reliability optimization, as well as identifying and supplying the appropriate financial system management tools for the company’s business activities. New business concepts and their advantages to management are introduced. The experimental results show that adding precision constraints to the floating-point calculation problem transforms it into an integer calculation problem, improving reliability by 0.389, demonstrating that the Markov model improves the efficiency of financial resource allocation. At the decision-making level, it provides a management platform and operational tools. As a result, research on this topic will assist businesses in developing a comprehensive business process that meets their needs and adapts to future development needs at a low cost, as well as assisting businesses in communicating with employees, external collaboration, and collaborative business between customers and partners.

show abstract

Section: Introductionmentioning

confidence: 99%

Reliability Optimization Analysis of Enterprise Financial Management System Using the Markov Model

Chen

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

Managing the Capital Transfer Market and Optimizing Management of the World Economic and Banking Systems Under the Conditions of Globalization. Regulating Regulators

Khilenko

2023

Cybern Syst Anal

View full text Add to dashboard Cite

Business investment decision-making based on mathematical model and risk analysis

Qi,

Liu,

Zhang

et al. 2024

IFS

View full text Add to dashboard Cite

Business investments are prone to market risks, so pre-analysis is mandatory. The type of risk, its period, sustainability, and economic impact are the analyzable features for preventing loss and downfall. In recent years, mathematical models have been used for representing business cycles and analyzing the impacting risks. This article introduces a Decisive Risk Analytical Model (DRAM) for identifying spur defects in business investments. The proposed risk analytical model exploits the investments, returns, and influencing factors over the various market periods. The risk model is tuned for identifying the influencing factors across various small and large investment periods. The model is tuned to adapt to different economic periods split into a single financial year. In the process of tuning and training the mathematical analysis model, deep learning is used. The learning paradigm trains the risks and modifying features from expert opinion and previous predictions. Based on these three factors, the risk for the current investment is forecasted. The forecast aids in improving the new investment feasibilities with minimal risks and model modifications. The frequent market status is identified for preventing unnecessary risk-oriented forecasts using the training performed. Therefore, the proposed model is reliable in identifying risks and providing better investment recommendations.

show abstract

Mathematical Modeling of the Dynamics of Crisis Situations and Optimization of Capital Relocation Market Management in the Context of Globalization in the World Financial and Economic System

Cited by 3 publications

References 11 publications

Reliability Optimization Analysis of Enterprise Financial Management System Using the Markov Model

Reliability Optimization Analysis of Enterprise Financial Management System Using the Markov Model

Managing the Capital Transfer Market and Optimizing Management of the World Economic and Banking Systems Under the Conditions of Globalization. Regulating Regulators

Business investment decision-making based on mathematical model and risk analysis

Contact Info

Product

Resources

About