Financial Model Research Articles

Construction and evaluation of financial distress early warning model based on machine learning

As the economy of our nation has expanded, the harm resulting from the financial risks associated with listed corporations has intensified, significantly impeding the survival and expansion of businesses. We must create a reliable financial crisis early warning system to prevent financial risks from endangering the business. This article aims to analyse the development of financial early warning models that use machine learning techniques. The model employs the Archimedes optimization method (AOA) method to optimize the parameters of SVM and selects 20 financial risk assessment indices as the input to anticipate the financial crises. The results suggest that the proposed model surpasses current models in terms of both prediction accuracy and resilience. They also highlight the ensemble model's greater predictive capacity when compared to individual algorithms, highlighting its efficacy in spotting early warning signs of financial instability in a variety of market scenarios. Financial organisations and governments may improve their risk management procedures and prevent future crises by incorporating machine learning techniques into the early warning system.

Construction and Optimization of Financial Transformation Decision Model Based on Data Mining

Real-time data processing and analysis is crucial in the financial markets since transactions in real time are directly linked to profit. Understanding the integration of finance and business allows the organization to enhance its core competitiveness by improving management and enabling it to lead business expansion more effectively. This manuscript proposes a Sparse Oblique Trees Algorithm (SOTA) optimized with the Waterwheel Plant Algorithm (WWPA) for the construction of financial transformation. The data are collected from multi-national organization dataset. Afterward, the data’s are fed to pre-processing. In pre-processing segment; it removes the noise and enhances the input data’s utilizing Data-Adaptive Gaussian Average Filtering (DAGAF). The outcome from the pre-processing data is transferred to the SOTA. The investment and dividend are successfully classified by using SOTA. The WWPA is used to optimize the weight parameter of SOTA. The proposed SOTA-WWPA is applied in python working platform. The proposed technique was computed by examining performance indicators like precision, F1-score, accuracy, sensitivity and recall. The proposed SOTA-WWPA technique yields improved results in terms of accuracy (16.65%, 18.85%, and 17.89%), sensitivity (16.34, 12.23%, and 18.54%), and precision (14.89%, 16.89%, and 18.23%). The proposed FT-SOTA-WWPA method is contrasted with the existing methods like FT-CNN, FT-MBLSTMNN, and FT-SVM models respectively.

Analyzing the Dynamics: EU and UN Perspectives on the African Union

This article presents a comprehensive analysis of the dynamics between the European Union (EU) and the United Nations (UN) in their interactions with the African Union (AU). Recognizing the AU's pivotal role in addressing Africa's challenges, the study investigates historical trajectories, policies, and key priorities shaping EU and UN perspectives on the AU. Through a multi-methodological approach, including document review and comparative analysis, it explores areas of convergence, divergence, and potential conflicts in their approaches, shedding light on implications for the AU's development and international relations. Tracing the historical evolution of EU-AU and UN-AU relations, pivotal moments such as the Cotonou Agreement and the establishment of the UN Office to the African Union (UNOAU) are highlighted. Analysis reveals shared objectives in peace, security, sustainable development, and governance, with nuanced differences in emphasis and approach. While areas of alignment exist, particularly in peacekeeping and sustainable development goals, divergences emerge in governance, human rights, and economic relations, posing challenges to collaboration.Case studies, including joint peacekeeping missions in Mali and development initiatives in Eastern Africa, offer insights into successes, challenges, and lessons learned, informing recommendations for future collaborations. Opportunities for enhanced cooperation, such as joint capacity-building initiatives and aligned development programs, are identified.The study contributes to a nuanced understanding of EU and UN perspectives on the AU, providing insights into their implications for Africa's development. Recommendations for improvements in coordination mechanisms, communication channels, and decision-making processes are proposed to enhance effectiveness. Future research avenues include evaluating economic partnerships' impacts and exploring innovative funding models to unlock transformative impact on the African continent. Ultimately, this analysis informs policymakers and international practitioners, fostering constructive partnerships for Africa's stability, prosperity, and sustainable development.

EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX

This study presents an innovative financial time series analysis approach by integrating Complete Ensemble Empirical Mode Decomposition (CEEMDAN) with the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model. The primary contribution of the research lies in significantly enhancing the predictive accuracy and understanding of the dynamics governing major stock indices. CEEMDAN adaptively decomposes complex financial time series into intrinsic mode functions (IMFs), a technique that has yet to be extensively utilized in this domain. IMFs are combined with ARIMAX's predictive proficiency, which accounts for the influence of historical trends and external factors. Our study showcases an R² of 0,93, aligning with some of the high-performing models in the literature. However, the unique strength of our model lies in its lag-free predicting of the DJIA, effectively mirroring its volatility and major movements with high fidelity, making it highly practical for financial applications.

Symmetry and symmetric transformations in mathematical imaging

The article delves into the intricate relationship between symmetry and mathematical imaging, spanning various mathematical disciplines. Symmetry, a concept deeply ingrained in mathematics, manifests in art, nature, and physics, providing a powerful tool for understanding complex structures. The paper explores three types of symmetriesreflection, rotational, and translationalexemplified through concrete mathematical expressions. Evariste Galoiss Group Theory emerges as a pivotal tool, providing a formal framework to understand and classify symmetric operations, particularly in the roots of polynomial equations. Galois theory, a cornerstone of modern algebra, connects symmetries, permutations, and solvability of equations. Group theory finds practical applications in cryptography, physics, and coding theory. Sophus Lie extends group theory to continuous spaces with Lie Group Theory, offering a powerful framework for studying continuous symmetries. Lie groups find applications in robotics and control theory, streamlining the representation of transformations. Benoit Mandelbrots fractal geometry, introduced in the late 20th century, provides a mathematical framework for understanding complex, self-similar shapes. The applications of fractal geometry range from computer graphics to financial modeling. Symmetrys practical applications extend to data visualization and cryptography. The article concludes by emphasizing symmetrys foundational role in physics, chemistry, computer graphics, and beyond. A deeper understanding of symmetry not only enriches perspectives across scientific disciplines but also fosters interdisciplinary collaborations, unveiling hidden order and structure in the natural and designed world. The exploration of symmetry promises ongoing discoveries at the intersection of mathematics and diverse fields of study.

Accelerated numerical solutions for discretized Black–Scholes equations

Abstract Accepted by: Aris Syntetos This study thoroughly investigates the efficiency of advanced numerical extrapolation methods aimed at enhancing the convergence of vector sequences in the realm of mathematical finance. Our focus lies in the application of polynomial extrapolation techniques to calculate finite difference solutions for the Black–Scholes (BS) equation—an indispensable model in options pricing. The performance of our algorithms undergoes rigorous evaluation through a comprehensive analysis involving both simulated and real-world data. Notably, our experiments uncover that a stochastic scheme, incorporating two extrapolation strategies and a random relaxation parameter, outperforms other proposed methods, excelling in both convergence and stability metrics. Our findings underscore the potential of this numerical extrapolation method to enhance the efficiency of financial calculations, particularly in the realm of option pricing. This innovation holds promise for refining financial models and addressing specific challenges within the field of mathematical programming, providing effective solutions to the primary computational bottlenecks commonly encountered in financial decision-making.

Portfolio optimization and risk management through Hierarchical Risk Parity and Logic Learning Machine: a case study applied to the Turkish stock market

This study explores an innovative approach to portfolio optimization, bridging traditional Modern Portfolio Theory (MPT) with advanced Machine Learning techniques. We start by recognizing the significance of Markowitz's model in MPT and quickly proceed to focus on the Hierarchical Risk Parity (HRP) method. HRP overcomes some of the limitations of Markowitz's model, particularly in managing complex asset correlations, by offering a more refined risk management strategy that ensures balanced risk distribution across the portfolio. The paper then introduces an innovative Machine Learning approach that employs the Logic Learning Machine (LLM) method to enhance the explainability of the Hierarchical Risk Parity strategy. Such integration is considered the core research part of the study, given that its application makes the output of the model more accessible and transparent. A case study based on the Turkish stock market has been provided as an example. The combination of traditional financial theories with modern Machine Learning tools marks a significant advancement in investment management and portfolio optimization, emphasizing the importance of clarity and ease of understanding in complex financial portfolio models.

Towards informed decision-making: A comprehensive statistical study of economic and financing models for sustainable energy transition in urban environments

This paper presents a comprehensive approach to optimizing renewable energy allocation in urban environments. Our methodology integrates statistical economic modeling and decision-making techniques to address the complex challenges associated with renewable energy management. By employing stochastic programming and robust optimization, we optimize renewable energy allocation to minimize operational costs, reduce emissions, and enhance system reliability. Our analysis demonstrates significant reductions in operational costs (15.2%) and emissions (20.5% for NOx and 18.9% for CO2) compared to baseline scenarios. Moreover, our approach improves system reliability by reducing load shedding (25.1%) and enhancing overall system reliability (12.3%). These findings underscore the effectiveness of our proposed strategy in fostering economic, environmental, and operational sustainability in urban energy environments.

516 Alternative Clinical Trials Staffing Models for Improved Efficiency, Retention, and Profitability

OBJECTIVES/GOALS: High turnover rates of clinical trials staff pose obstacles to the quality and efficiency of conducting clinical trials. We have explored alternative staffing models to address these translational barriers and to improve the financial viability and return on investment of a centralized clinical trials office. METHODS/STUDY POPULATION: Implementation of an alternative clinical trials staffing model that leveraged burnout rates in clinical service areas by hiring APPs, RNs, EMTs, OTs and PTs in tandem with traditional CRAs/CRCs. Financial modelling of employing higher salaried clinical professionals was analyzed with regards to greater staff retention, trials efficiency, and operational cost savings. RESULTS/ANTICIPATED RESULTS: Since 2014, 30 of 51 (59%) staff left the clinical trials office with 49% of these leaving prior to 2 years employment. Using average local CRCII compensation values, the costs associated with these transient staff amounted to $2.51 million (i.e., recruitment, replacement, and training). Models of staffing that replace 2 CRAs, 2 CRCIs and 2 CRCIIs with an RN, APP, CRCI and 3 CRAs increases compensation by 24.1%. This increase, however, is offset by greater workload capacity, retention, and more efficient trials operations. In addition, revenue generating PI clinical activity is sustained by employing credentialed APPs for study visits. DISCUSSION/SIGNIFICANCE: Long-term financial savings and greater clinical trial operational efficiency may be accomplished by seeking clinical professionals looking for alternative opportunities with greater work-life balance while leveraging their advanced clinical skills and licensing.

A Novel Hyperchaotic Financial System with Sinusoidal Hyperbolic Nonlinearity: From Theoretical Analysis to Adaptive Neural Fuzzy Controller Method

Chaotic systems are known to be extremely sensitive to initial conditions, meaning small changes can have a significant impact on the outcomes. By analyzing the average profit margin in relation to chaotic dynamics, companies can conduct sensitivity analysis to assess the potential impact of various factors on their profitability. This analysis can help identify critical variables or scenarios that may significantly affect profit margins. In this article, we have proposed a hyperchaotic financial system with sinusoidal hyperbolic non-linear variables applied to the average profit margin. Furthermore, we have investigated the stability of the hyperchaotic financial dynamics model to provide information to companies to assess the consistency and reliability of their profitability. In addition, fundamental dynamic behavior like Lyapunov exponents, bifurcation analysis, coexisting attractors have been reported. Finally, a nonlinear feedback control approach is developed to train an adaptive neural fuzzy controller. The application of Lyapunov theory confirms that this nonlinear feedback controller can effectively minimize the synchronization error within a finite duration. The results from simulations establish the effectiveness of the proposed neural fuzzy controller architecture in controlling the synchronization of two hyperchaotic financial models. Additionally, the simulation includes a comparison between the performance of the nonlinear controller and the adaptive neural fuzzy controller.

DEVELOPMENT OF THE VALUE BASE MANAGEMENT CONCEPT AND ITS ROLE IN MAXIMIZING THE VALUE OF THE CORPORATE ENTERPRISE

The purpose of the study is to critically review and summarize the main world approaches to building Value Base Management and to substantiate, on this basis, recommendations for its implementation in the domestic practice of financial management.  The article describes the evolution of VBM development and conceptual approaches to corporate enterprise management. The objective necessity of transforming approaches to the management of corporate enterprises from traditional financial models to value-based management is substantiated. It is established that VBM is not only one of the main directions of improving the efficiency of management decisions, but also has a significant impact on the value of a corporate enterprise in the long term. The article proves that the use of the value-based concept of enterprise financial management implies the selection of appropriate methods and tools at all levels of management aimed at sustainable value reproduction. 
 The article considers the process of transformation of the VBM concept and its main elements. A comprehensive analysis of the main approaches and models to business valuation is presented. A comparative characterization of VBM and the traditional approach to corporate enterprise management is systematized and provided. 
 The generalization of the economic content of management theories has allowed to substantiate that VBM differs significantly from the previously accepted management criteria and is a more effective concept of equity management than other systems. It should be borne in mind that the value of an enterprise, on the one hand, is an indicator of its sustainable development, and on the other hand, it is a regulator of value distribution for various participants in economic relations, so, of course, the management mechanism should focus on the key factors of value creation

Recent Developments in Financial Innovation Research and Sustainable Accounting Models Bibliometric Analysis approach

This study conducts a systematic bibliometric analysis to examine recent developments in financial innovation research and sustainable accounting models. The intertwining of financial innovation and sustainability reflects a significant shift in the global economic paradigm, with innovations such as green bonds and impact investing reshaping financial markets. The research aims to identify key themes, trends, and interdisciplinary connections within the literature, offering insights for academia, practitioners, policymakers, and other stakeholders. Analysis reveals a growing emphasis on the role of financial innovation in driving sustainable growth and the importance of sustainable accounting in fostering transparency and accountability. The study also highlights emerging research directions, including interdisciplinary approaches, drivers of innovation and development, microfinance impact on household economics, and the nexus between education and health. Ultimately, this research seeks to contribute to the advancement of knowledge and practices promoting both financial prosperity and environmental and social well-being.

Применение методов машинного обучения для анализа показателей на финансовых рынках

The results of research in the field of application of various machine learning (ML) methods for the analysis and forecasting of financial time series are presented. The purpose of the study is to assess the suitability of ML algorithms for solving the problem of analyzing and predicting the state of financial markets. The source data was public data on Apple shares for the period from May 2002 to June 2020. As part of the study, machine learning algorithms were developed using stochastic analysis and data was prepared with subsequent preprocessing for better learning algorithms, as shown in the example of matrix formation for the boosting algorithm. The area of forecasting financial time series was studied and algorithms of boosting, fully connected neural network, recurrent neural network, neural networks with long short-term memory, convolutional neural network were applied. Based on the trained algorithms, a comparative analysis was carried out on a sample of financial instruments from one sector

AN ENHANCED SHRINKAGE FUNCTION FOR DENOISING ECONOMIC TIME SERIES DATA USING WAVELET ANALYSIS

In the realm of economic (financial) time series analysis, accurate prediction holds paramount importance. However, these data often suffer from the presence of noise, particularly in highly random and non-stationary datasets like stock market data. Dealing with noisy data makes predicting noise-free economic models exceedingly challenging. This research paper introduces an innovative shrinkage (thresholding) function designed to improve the efficiency of wavelet shrinkage denoising in the context of financial time series data. The proposed function is constructed based on an arctangent model with adjustable parameters meticulously chosen to ensure the function maintains continuous differentiability. The application of this novel shrinkage function effectively reduces noise in stock data. Employing R program for data analysis and figure plotting, the performance of this approach is rigorously validated using closing price data from the Shanghai Composite Index, spanning the period from January 4, 2000 to August 28, 2023. The experimental results demonstrate that the proposed thresholding function outperforms classical shrinkage functions (hard, soft, and nonnegative garrote) in both continuous derivative property and denoising efficacy.

DEVELOPMENT OF PUBLIC TRANSPORT IN RURAL AREAS IN POLAND: THE EXAMPLE OF THE SLUPSK DISTRICT

The paper addresses the development of public transport (PT) in rural areas in the context of the implementation of the Mobility-as-a-Service (MaaS) concept. This paper aimed to indicate whether and how rural areas can use the MaaS concept to develop PT and improve transport accessibility. The financing model for the operation of collective PT in the example of Poland was analysed. The case study Pomeranian Voivodeship was selected due to the strong influence of the Tri-City agglomeration, which does not directly include western parts of the region such as Słupsk County. Several research methods were used: desktop research, case study and GIS analysis. The results highlight that the MaaS concept has the potential to be implemented in rural areas; however, this will be a long-term process and will require advanced cooperation between local authorities, private operators and service providers. This process is dependent on measures being taken to support the use of MaaS at a national level. From the inhabitants' perspective, such changes are a positive direction for PT.

Information sharing across institutions: Practices and barriers during public health emergencies in Ethiopia

BackgroundRapid, integrated information exchange between stakeholders is critical for effective emergency preparedness and response. However, many low- and middle-income countries face barriers to seamless data sharing. While information accessibility is recognized as important for evidence-based decision-making and resource allocation in Ethiopia, factors influencing current health information sharing practices among stakeholders involved in public health emergency management programs are unclear. This study aims to examine multi-sectoral stakeholders' perspectives and experiences with health data sharing during emergencies in Ethiopia, to identify opportunities and challenges influencing practices to strengthen the national public health emergency response system. MethodsA mixed-methods study was conducted between June and August 2023, involving a survey of 169 stakeholders actively involved in PHEM programs in Ethiopia as well as 23 in-depth interviews with key informants in senior leadership or advisory roles. The data was analyzed using descriptive statistics in SPSS and thematic analysis of qualitative transcripts. ResultsDuring emergencies, it was observed that data sharing between different entities occurred. Quantitative findings showed the predominant types of health data shared between stakeholders during emergencies included hospital data (109, 64.5 %), clinical case information, and laboratory results. Challenges limiting effective coordination included issues like limited functionality of digital health systems (75, 44 %), incompatible data formats (13, 34 %), and financial constraints (83, 49 %) and and socio-cultural barriers constrain current practices in Ethiopia. Qualitative interviews identified five themes around risk communication and inclusive alert systems. Experts emphasized tailored, multichannel outreach but noted infrastructure gaps and digital divides currently limit poorer communities' engagement. ConclusionWhile collaborative health information exchange during emergencies is recognized as important, systemic, financial, and socio-cultural barriers constrain current practices in Ethiopia. Targeted strategies including capacity building, investment in integrated data infrastructure, economic optimization through innovative financing models, trust-based relationship development, and locally relevant communication channels informed by stakeholder perspectives can optimize information accessibility, coordination, quality, and equity of healthcare services during public health emergencies.

Ijarah Financing Model for Farmers' Capital with a Post-Harvest Return System

The aim of this research is to determine the ijarah financing model for farmers’ capital with a post-harvest return system at BMT Maslahah Cabang Krucil. This research uses qualitative methods with data collection methods in the form of observation, interviews and documentation. One of the financing products offered by BMT Maslahah Cabang Krucil is ijarah financing with a post-harvest repayment system whose aim is to make it easier for farmers to obtain capital. Members who borrow capital with a post-harvest repayment system can return the capital they have borrowed in several harvests and the collateral required is also not that difficult, but BMT Maslahah The Krucil Branch continues to apply caution in providing loans. So this is one of the factors of public interest in ijarah financing with a post-harvest repayment system and at BMT Maslahah, the Krucil Branch does not apply an interest system but applies a profit sharing system. Where members can pay profit sharing. At the beginning of the loan period or paying in installments every month but cannot pay in full at the end of the due date.

The importance of timing in energy shipping

Aim: Investment decisions in shipping are notoriously difficult. The key reason is that freight rates are highly volatile and often unpredictable. The energy shipping sector is most challenging. The paper formulates an investment decision rationale in energy shipping and provides insights as to the ability of experts to predict market trends when it comes to the oil tanker supply and demand and the resulting freight rates. Moreover, we focus on the timing of such investment decisions and ways to identify when the timing thereof is right. This paper aims to develop an investment decision framework in energy shipping, specifically investigating the importance of timing when making investment decisions in this sector, and to test the suggested rationale by applying it to a specific oil tanker purchase case. Design / Research methods: The research process started with a Delphi study to gather expert opinions on the changes taking place in the shipping market. We continued our research process with a concise economic analysis of the 2018 shipping market, and we then applied the findings to a feasibility study for purchasing a specific type of small-scale (Aframax) oil tanker by a company based in Cyprus. Conclusions / findings: Our main findings were that the opinions of the selected commercial experts supported the results of the secondary data analysis, and all the respondents found the year 2018 an attractive time for investing in ships. The results of an investment appraisal financial modelling exercise for the above-mentioned specific purchase case were satisfactory for accepting the project. Due to low purchase prices and high residual value, the initial required investment was lower while the return and other results would be better for a 15-year-old ship than for a 10-year-old unit. However, the risks were to increase together with age. Considering that the prices of Aframaxes were hitting their historically low levels in 2018, and that within the next five years the demand for modern tonnage was anticipated to be firm and thus raise their values up to 35% above the 2018 levels, the purchase of a more modern unit was recommended as it could give an opportunity of a beneficial resale at the end of the project or earlier. In view of the latest market developments, we can now conclude that overall, despite the unprecedented turbulence during the pandemic years, the 2018 investment recommendation proved correct and insightful. Originality / value of the article: The current paper’s intended contribution is an investment analysis based on the primary data from a relatively small shipping company. The disclosed primary data for the intended 2018 purchase of an Aframax oil tanker is unique. Thus, the current paper provides useful practical guidance for potential investors and other professionals who follow the energy shipping market as well as contribute to academic research in shipping finance by providing a framework that is applicable well beyond the described case study. Considering the upward move in the crude oil freight rates and anticipating investment interest in energy shipping, following the Covid-19 pandemic and the war between Russia and Ukraine, we believe that this research paper has a good timing.  Keywords: energy shipping, investment appraisal, oil freight market, shipping cycles, decision framework, decision timing JEL: F44, G31, L22, L91, R42

Factors Affecting the Financial Performance of Banks in the Era of Digital Transformation: A Perspective from Vietnam’s Banking Industry

This study aims to explore the factors influencing the financial performance of banks in Vietnam amidst the on-going digital transformation. In today's dynamic economic landscape, characterised by digital transformation and heightened regulatory standards, banks are compelled to innovate in response to continuous macroeconomic fluctuations. While the importance of increasing financial performance was largely recognised, the development and empirical validation of a financial performance model in the banking digital transformation context is not yet completely addressed, especially in developing countries. Utilising foundational theories, this research employed SMARTPLS 4 software alongside a blend of quantitative and qualitative analyses to assess the influences on the financial performance of banks amid digital transformation. Data were collected from 400 bank employees from all genders across Vietnam by the quota sampling method. Most of the survey participants lived in big cities where many banks were concentrated. The research results showed that there were three main factors directly affecting bank financial performance including Deposit mobilisation, Bank profitability, and Loyalty respectively according to the level of impact. This result highlights the importance of effective deposit mobilisation in achieving the bank's financial goals. The results also indicated that the growth of banks' profits is closely tied to their capacity to maintain loyal customers. Additionally, the study revealed a strong correlation between nine other factors, such as Savings Interest rates, Bank technology, and Inflation, with three intermediary factors. Until now, there are very few studies that have comprehensively evaluated both financial and non-financial factors' effect on bank financial performance as this research. This study also offered compelling insights into the strategies of successful banks in Vietnam today, underscoring the importance of investing in bank technology and presenting valuable recommendations. Based on the findings, banks can specifically focus on managing the identified variables and deploying appropriate business strategies to enhance their financial performance for sustainable development in the digital age.

Инструменты финансового сопровождения научно-технологического трансфера

The issues of using financial support tools for technological entrepreneurship, both on the part of the university implementing acceleration programs to support scientific and technical projects, and on the part of the project team developing a startup from the idea stage are considered. The purpose of the study is to identify the specifics of financing scientific and technological transfer and identify areas of application of financial instruments in the field of technological entrepreneurship based on the analysis of sources of scientific literature and foreign practice. The objectives of the study are a review of scientific sources on the problems of financing scientific and technological transfer; assessment of the state of the regulatory framework for ensuring scientific and technological transfer; setting the problem of finding financial support for technological entrepreneurship from the university, using financial instruments to attract private capital, as well as determining the solution to it. The research methods were the bibliographic method for performing a literature review, statistical analysis based on the use of data from Rosstat of the Russian Federation, analysis, and synthesis of the subject area – financial support for scientific and technical transfer and visualization of the data obtained by graphical methods. The option for modifying the financial model of the university, structuring the levels of readiness of the investment project for technological entrepreneurship, selection of financial instruments for promoting the project at different stages of readiness are proposed.