Financial Model Research Articles

Assessing risks and opportunities associated with ESG factorsin investment projects

Our aim was to investigate the influence of environmental, social, and governance (ESG) factors on risks and opportunities in investment and construction projects. The research methodology involved a review of scientific publications, both domestic and foreign, concerning ESG factors and approaches to their integration into investment projects. This allowed us to develop a step-by-step approach to assessing the impact of these factors on the risks and opportunities in investment construction projects. The proposed approach consists in identifying significant ESG aspects, carrying out a qualitative and quantitative analysis of the associated risks and opportunities, and establishing a system for managing ESG factors within a particular project. In addition, we developed a system of quantitative indicators for assessing ESG factors, allowing them to be taken into account when designing a financial and economic model of the project. Theoretical results related to the development of an algorithm for assessing the impact of ESG factors and the system of indicators for their quantitative measurement were obtained. It is concluded that the assessment and management of ESG factors play an important role in improving the sustainability and efficiency of investment and construction projects. Further research should be focused on the creation of industry standards and indicators for assessing ESG factors in the sphere of investment and construction, as well as on the improvement of tools for environmental funding of projects.

A Comprehensive Study of Techno Economic Viability (TEV) Report of Various Projects

This paper provides a comprehensive analysis of the TEV process, focusing on its application to both greenfield and brownfield projects across various sectors, including manufacturing, infrastructure, energy, and services. The study aims to guide decision-makers by bridging technical assessments with financial analysis to establish project profitability and sustainability. A Techno-Economic Viability (TEV) Study serves as a crucial tool in the decision- making process for both lenders and investors. It offers a detailed assessment of a project's technical feasibility, economic viability, and overall sustainability. It delves into the dual aspects of technical feasibility and economic viability, emphasizing their roles in influencing investment decisions across a wide spectrum of industries, including manufacturing, infrastructure, energy, and services. The findings from this research also illustrate real-world applications, highlighting two case studies with an emphasis on key financial indicators such as the Debt Service Coverage Ratio (DSCR) and Internal Rate of Return (IRR), which are used to measure a project’s ability to meet debt obligations and generate profits. Keywords: Techno Economic Study, Financial Viability, Technical Feasibility, Financial Modelling, Risk Analysis, Investment Decision

Finance Modeling Approach Using Machine Learning

The integration of machine learning (ML) into financial modeling represents a transformative shift in the finance sector, enhancing analytical capabilities and decision-making processes. This article explores the fundamental concepts of financial modeling and machine learning, illustrating how ML methodologies—both supervised and unsupervised—improve traditional financial models by enabling the analysis of extensive datasets to uncover intricate patterns. By leveraging historical data, ML enhances predictive accuracy in various applications, including risk assessment, algorithmic trading, credit scoring, and fraud detection. The article highlights significant advancements in predictive analytics through techniques such as regression analysis and time series forecasting, which allow financial analysts to navigate market uncertainties more effectively. Moreover, the automation of data processing and feature engineering through ML leads to increased operational efficiency, reducing human error and enhancing real-time decision-making capabilities. However, challenges such as data quality issues, model interpretability, and ethical considerations regarding algorithmic bias necessitate careful management to ensure responsible implementation. The article emphasizes the importance of transparency in ML models to foster trust among stakeholders while meeting regulatory requirements. Future research directions are proposed, focusing on the integration of explainable AI with blockchain technology and the implications of big data analytics on ML models. By addressing these emerging challenges and opportunities, finance professionals can leverage advanced analytical tools to drive innovation and enhance strategic initiatives in an increasingly complex financial landscape. Ultimately, this article underscores the pivotal role of machine learning in reshaping financial modeling practices and its potential to redefine the future of finance.

Optimal financing strategies for low‐carbon supply chains: A Stackelberg game perspective

AbstractThis study constructs a Stackelberg game model between low‐carbon manufacturers and capital‐constrained retailers. Considering the manufacturer's financing model, the manufacturer's investment model, and the internal blended financing model, it studies the optimal wholesale price of manufacturers. This paper investigates the impact of consumers' low‐carbon preference, the financing interest rate of manufacturers, investment ratio, investment return on carbon emission reduction investment, and supply chain pricing. The conditions for low‐carbon supply chain participants to choose the best financing mode and the impact of different financing models on the performance of low‐carbon supply chains were obtained.

Model risk management in stress testing: The road up to here

This paper reviews the historical evolution of the quantitative models, model usage and model risk management (MRM) in large US banks since the 1980s. It comments on the most significant model-related events through this period. Some of these events were associated with contexts of great stress to the financial system. The paper identifies the main features of modelling in finance and economics that distinguish it from its application in the natural sciences. Then it presents the different types of models used in the large banks' stress testing programmes, the specific characteristics and risks of each type of model and the best practices for the implementation of sound model risk management, and it suggests a proper way to interpret the results of stress testing and capital assessment in the presence of model risks. The paper discusses the future evolution of MRM towards the implementation of a `fully fledged risk management framework` along the lines of other risk management disciplines, including risk identification, measurement, monitoring, reporting, limiting and capitalisation. It acknowledges the enhancements and opportunities offered by advances in the use of artificial intelligence and machine learning as well as increases in computational power. The paper concludes by noting that the model risk management framework that has been enforced by the Federal Reserve's stress testing and capital planning programmes has substantially strengthened the capital position, profitability and resilience of the large banks in the US.

A robust financing theory of ICOs under demand uncertainty of products of token platforms

PurposeThe market products produced by Initial Coin Offerings (ICO) platforms are often relatively new and have no previous transaction records and therefore are hard to estimate for its demand. The purpose is to study the impacts of the degree of ambiguity aversion of entrepreneurs to demand uncertainty on the ICO financing ratio, the optimal expected output, the optimal efforts and the token price.Design/methodology/approachIn an optimal ICO design, we introduce demand uncertainty of the product and establish a robust optimization method to solve the ICO optimal design. We compare ICO financing and the general venture capital (VC) financing model. We analyze the impact of demand uncertainty on the optimal ICO financing ratio.FindingsFindings include that the ICO financing ratio is positively related to the degree of ambiguity aversion, the token price is negatively related to the degree of ambiguity aversion and the “ambiguity premium” exists in the ICO market, the optimal effort levels are negatively related with the ICO financing ratio, but positively related with token price, and in the environment of high production cost, VC financing is not as good as ICO financing.Originality/valueWe develop a robust ICO financing model by assuming that the entrepreneur is ambiguity aversive to the demand uncertainty. Analyze the impact of the degree of ambiguity aversion on the ICO financing ratio in theory and find that the entrepreneur can raise funds with the higher ICO token ratio when she has a larger degree of ambiguity aversion to the demand uncertainty. Extend the impact analysis of the degree of ambiguity aversion on the expected token price and find a negative relationship between the expected token price and the degree of ambiguity aversion of the entrepreneur to the demand uncertainty.

The Finance of Cybersecurity: Quantitative Modeling of Investment Decisions and Net Present Value

Despite the growing literature on cybersecurity investment, significant gaps remain unaddressed. First, the existing literature does not differentiate between cybersecurity expenditures, namely investment versus spending, despite the differences in their financial treatments. Second, the literature predominantly employs economic and risk-based models, in which the incorporation of financial parameters is limited. Third, the literature focuses on quantifying market (stock) loss of cyberattacks or conducting a cost-benefit analysis of cybersecurity investments but has yet to bridge these two areas. Thus, our objective is to introduce a new financial model, the Alpha Model, that addresses these gaps. We first provide distinctions between cybersecurity investment (capital expenditure) and spending (operational expenditure). Grounded in Relative Valuation Models and Optimal Capital Structure Theory, we quantify the upper-bound of optimal cybersecurity investment by utilizing book and market values of equity and debt. We also demonstrate that the Net Present Values (NPV) of cybersecurity investment is considerably lower than its book value by incorporating financial parameters of equity and debt financing, inflation-adjusted capital cost, and tax credits of depreciation and interest. To validate our model, we empirically test it by using data from the S&P500 index during 2018–2023. We offer practical implications to key stakeholders, including: (1) industry executives to financially quantify cybersecurity investments and ascertain their actual NPVs, individually and collaboratively with supply chain partners; (2) cybersecurity solution providers to promote business investments in cybersecurity; and (3) insurance providers to price premiums with higher NPVs than the corresponding investments. We also offer several avenues for further research.

The Profound Nature of Derivatives and Integration: A Comprehensive Examination

This article delves into the fundamental concepts of derivatives and integration, two core principles of calculus that govern a wide range of mathematical and real-world applications. Derivatives, which measure the rate of change of a function, and integration, which calculates accumulation and areas under curves, are essential tools in fields such as physics, economics, engineering, and finance. The article offers a detailed exploration of their mathematical definitions, key properties—including linearity, the product and chain rules, and the Fundamental Theorem of Calculus—and the diverse techniques used in integration. The practical applications of these concepts are also examined, with special emphasis on their role in modeling dynamic systems, optimizing processes, and solving real-world problems such as motion analysis, financial modeling, and economic optimization. Through in-depth discussion and examples, this comprehensive article bridges the theoretical underpinnings of derivatives and integration with their vast and critical uses in various scientific and economic disciplines.

Backtest overfitting in the machine learning era: A comparison of out-of-sample testing methods in a synthetic controlled environment

We present a comprehensive framework to assess these methods, considering the unique characteristics of financial data like non-stationarity, autocorrelation, and regime shifts. Through our analysis, we unveil the marked superiority of the Combinatorial Purged (CPCV) method in mitigating overfitting risks, outperforming traditional methods as evidenced by its lower Probability of Backtest Overfitting (PBO) and superior Deflated Sharpe Ratio (DSR) test statistic. Walk-Forward, by contrast, exhibits notable shortcomings in false discovery prevention, characterized by increased temporal variability and weaker stationarity. This contrasts with CPCV’s demonstrable stability and efficiency. We introduce novel variants of CPCV, including Bagged CPCV and Adaptive CPCV, which enhance robustness through ensemble approaches and dynamic adjustments based on market conditions. Our empirical validation using historical SP 500 data confirms these advanced cross-validation methods’ practical applicability and resilience. The analysis also suggests that choosing between Purged K-Fold and K-Fold necessitates caution due to their comparable performance and potential impact on the robustness of training data in out-of-sample testing. Our investigation utilizes a Synthetic Controlled Environment incorporating advanced models like the Heston Stochastic Volatility, Merton Jump Diffusion, and Drift-Burst Hypothesis alongside regime-switching models. This approach provides a nuanced simulation of market conditions, offering new insights into evaluating cross-validation techniques. We also address the computational aspects of these methods, demonstrating that parallelization significantly improves efficiency, making them feasible for large-scale financial datasets. Our study underscores the necessity of specialized validation methods in financial modeling, especially in the face of growing regulatory demands and complex market dynamics.

A planning framework for intra-city electric bus road transportation systems

Gasoline-based vehicle transportation plays a crucial role in environmental pollution, which eventually leads to serious health issues in urban areas. The scope of large-scale deployment of personalized electric vehicles in the near future can lean toward such issues but can overburden local distribution grids (LDGs), and the entities are producing green energy. In this context, an electric bus road transportation system (EBRTS) can be seen as an effective and promising way, although it is capital-intensive. EBRTs can be successfully deployed by developing a well-tailored planning model that encompasses amicable solutions while considering the conflicting interests of electric utilities and investors/transporters.This paper proposes a planning model for intra-city EBRTS, especially for metropolitan cities, to extract tangible benefits in favour of electric utilities and private players. The proposed framework envisages a bus fleet charging model and financial model within the spatial, technical, and financial constraints yet preserves the interests of electric utilities and investors by providing tentative solutions with all information necessary from the investor’s perspective. The methodology is free from optimization as the realistic constraints inherently employ search space reduction. The simulation results on a real public transportation system reveal the proposed methodology’s simplicity, rationality, applicability, and flexibility.

Periodic information enhancing and controlling stability of complex corporate financial systems

We explore the mechanism and control behavior of how periodic information affects the stability of complex corporate financial systems from two viewpoints: the corporate life cycle and the stability of financial leverage risk. A complex corporate financial system model with correlated noise and periodic driving is proposed. Furthermore, two stability indicators are proposed to measure the stability of the corporate life cycle and the risk of corporate financial leverage. Stochastic simulation results show that enhancing the intensity and frequency of controllable periodic information can improve system stability. The stability of complex corporate life cycles and financial leverage risks exhibits opposite behaviors in terms of noise correlation. In addition, it is observed that there are specific threshold values of system parameters, and the relationship with the intensity of periodic information shows non-monotonic characteristics, manifesting peculiar phenomena and transition behaviors. Finally, by comparing with actual financial data, the proposed model is verified to be superior to the benchmark model.

Unlocking ETF price forecasting: Exploring the interconnections with statistical dependence-based graphs and xAI techniques

In the complex landscape of financial markets, accurately predicting Exchange-Traded Fund (ETF) price movements requires advanced methodologies. This research introduces a practical approach that integrates network analysis with graph embeddings, specifically utilizing Node2Vec, to enhance financial prediction models' performance and interpretability. By representing the intricate relationships within financial markets in a lower-dimensional space, we improve the efficiency of AI-driven predictions. A key component of our method is applying the SHAP Explainable AI (xAI) framework, which helps interpret our tree-based models' decision-making process. Using six different tree-based models, our approach delivers accurate predictions while maintaining transparency in model interpretation. This combination of graph embeddings and explainability tools enables stakeholders to understand better the factors influencing financial market behavior, improving decision-making based on AI models.

Characteristics of financial capital flow circulation and the risk transmission effect in China

Financial capital is an important force driving China's economic modernisation. Examining the characteristics of financial capital flow circulation across sectors and risk transmission laws is crucial in guiding financial market operations and deepening financial system reforms. This study establishes a 2011–2020 China matrix that includes capital fund flow statements for non-financial corporations, financial institutions, the general government, households, and foreign sources and examines changes in capital flow circulation by developing a financial flow multiplier system model to measure the direct and indirect transmission effects of capital flows between sectors from the perspective of liabilities. Results show that China's overall scale of financial capital flows has increased, but fluctuations have emerged in the last decade. The sectors' financing structure has changed significantly, with financial institutions remaining the primary capital inflow and outflow sectors of the financial market; however, the proportion of liability and investment in the household sector has increased, resulting in increased risk transmission. Moreover, the sectoral fund surpluses and shortages are highly volatile and uncertain, with non-financial enterprises serving as the primary drivers of real-economy investment. The capital flow from financial services to the real economy shows a fluctuating growth trend, primarily flowing to non-financial corporations; however, the proportion exhibits a downwards trend. Finally, we find that the flow transmission relationship between different sectors and financial transactions in the financial market is complex, with both direct and indirect risk transmission effects between sectors. Changes of financial institutions' liabilities significantly affect the liabilities and investments of other sectors.

Does Family Matters? Exploring the Enablers of Sustainable Indian Women Micro Entrepreneurship

The present article attempts to encapsulate the enablers of a women-led microentrepreneurship sustainable model. To fulfill this purpose, a widely used Indian microcredit women entrepreneurship financial assistance model, namely Self-Help Group (SHG)-linked micro women entrepreneurs (MWEs), who accessed a second loan from the bank individually for their business expansion, were interviewed. Responses of such 30 MWEs were content analyzed using NVIVO 12. The codes, themes, and project maps were generated to identify the enablers resulting in a microentrepreneurial sustainable development framework. The results suggest that business know-how competencies (customer excellence, networking) and personal competencies (confidence, optimism, perseverance) were identified as prominent enablers, with family support as the most critical factor. Findings further suggested that MWEs’ sustainable success depends on capabilities of balancing the support provided from family to business and vice versa (family-to-business support), as well as leveraging the skills and experiences learned in the family for managing the business or vice versa (family-to-business enrichment). Positive spillover of the family-to-business interface was key to their business growth, unlike previous studies where the family was considered to be the biggest challenge for women entrepreneurship. A change in societal mindset and a shift toward gender parity was observed.

Innovative Financial Products And Services For Underserved Communities

This paper provides an in-depth exploration of innovative financial products designed to meet the unique needs of underserved populations in the United States. It systematically examines how Community Development Financial Institutions (CDFIs) and Minority Depository Institutions (MDIs) are important in the delivery of these specialized financial services. By focusing on microloans, credit-building loans, and affordable housing financing, the paper elucidates the transformative potential of these financial products in enhancing financial inclusion and increasing economic empowerment within marginalized communities. The analysis shows how microloans facilitate entrepreneurship and poverty alleviation while credit-building loans serve as a tool for establishing and improving credit histories. It addresses innovative financing models for affordable housing by illustrating their impact on community stability and development. The role of CDFIs and MDIs is examined to reveal their unique contributions and challenges in providing accessible financial services to underserved groups. The paper explores implementation strategies and policy recommendations, emphasizing the need for a comprehensive approach that leverages technology, ensures strong partnerships with government and nonprofit entities, and addresses regulatory barriers. It entails the importance of measuring the impact of financial inclusion initiatives to ensure their effectiveness and sustainability. The paper calls for concerted action from policymakers, financial institutions, and community organizations to collaborate in advancing financial inclusion efforts. By harnessing the insights and strategies outlined, stakeholders can enhance the reach and effectiveness of financial products, ultimately driving economic growth and opportunity for all

Analytical computation of conditional moments in the extended Cox–Ingersoll–Ross process with regime switching: Hybrid PDE system solutions with financial applications

In this paper, we introduce a novel analytical approach for the computation of the nth conditional moments of an m-state regime-switching extended Cox–Ingersoll–Ross process driven by a continuous-time finite-state irreducible Markov chain. This approach is applicable for all integers n≥1 and m≥1, thereby ensuring wide-ranging utility. The key of our investigation is a complex hybrid system of inter-connected PDEs, derived through a utilization of the Feynman–Kac formula for regime-switching diffusion processes. Our exploration into the solutions of this hybrid PDE system culminates in the derivation of exact closed-form formulas for the conditional moments for diverse values of n and m. Additionally, we study the asymptotic characteristics of the first conditional moments for the 2-state regime-switching Cox–Ingersoll–Ross process, particularly focusing on the effects of the symmetry inherent in the Markov chain’s intensity matrix and the implications of various parameter configurations. Highlighting the practicality of our methodology, we conduct Monte Carlo simulations to not only corroborate the accuracy and computational efficacy of our proposed approach but also to demonstrate its applicability to real-world applications in financial markets. A principal application highlighted in our study is the valuation of VIX futures and VIX options within a dynamic, mean-reverting, hybrid regime-switching framework. This exemplifies the potential of our analytical method to significantly impact contemporary financial modeling and derivative pricing.

Renal satellite units in Pakistan: Challenges, efforts, and recommendations.

This commentary delves into the complexities surrounding chronic kidney disease management in Pakistan, specifically examining the critical role played by renal satellite units in providing accessible dialysis services. Chronic kidney disease in Pakistan accounts for 3.9% of total deaths, warranting a focused exploration of challenges and potential solutions. RSUs, smaller entities affiliated with main renal units, emerge as key players in addressing issues of geographic accessibility and diminishing travel burdens for chronic kidney disease patients. Challenges such as financial constraints, limited resources, and staff shortages, particularly in rural settings, pose significant hurdles to the effective functioning of RSUs. This commentary emphasizes the importance of clear eligibility criteria, robust vascular access support, regular physician engagement, and the strategic integration of telemedicine. It explores diverse funding models, including government allocations, community contributions, and philanthropic partnerships, as potential solutions to alleviate cost-related concerns. The commentary advocates for a holistic, cost-effective approach to chronic kidney disease care, highlighting the transformative potential of renal satellite units in improving health outcomes across varied settings.

Financing urban land development in underdeveloped small cities in western China: The role of earmarked funds

Previous studies suggest that market forces and the strategic behavior of local governments regarding land development jointly contribute to rapid urban expansion in China. In underdeveloped small cities in Western China, the market is weak, and local governments lack the conditions required to promote land-centered urbanization. Surprisingly, these cities have experienced an expansion of urban space faster than the national average. This study presents a significant funding model of urban land development within the framework of the project system. Within this model, central and provincial governments are key providers of land development funds. Local governments design, apply for, package, and implement land development projects. Private developers cooperate with local governments rather than functioning as real initiators of and investors in land development projects. The findings provide new and supplementary evidence about how rapid urban expansion occurs in China, particularly in underdeveloped areas.

Financial Model Construction and Identification of Abnormal Activities in Mobile Networks for e-commerce Platform Using Machine Learning Algorithm

The advancement of informatization has significantly affected many sectors due to e-commerce, rendering the existing Financial Model (FM) inadequate for e-commerce customers. Artificial Intelligence (AI) substantially enhances the financial accounting capabilities and resource integration of systems using mobile networks. The insufficient FM of e-commerce tools and issues such as the administration of funds have significantly obstructed the advancement of standardized financial procedures, as the FM structure impacts managers' statistical evaluation of financial information. This research examines the financial hazards, management issues, and underlying causes of these issues inside e-commerce systems, subsequently employing AI to assess the FM operations of these websites in Peru. This research uses a Machine Learning (ML) approach to examine the clustering centers of FM information as well as the privacy aspect of the FM. This study ultimately presents ways for optimizing and constructing FMs to enhance data security and capital administration in e-commerce financing, hence fostering the continued growth of e-commerce systems using mobile networks. The experimental findings indicate that the FM’s classification value and security aspect for e-commerce systems progressively enhance under the ML. The average classification value is 1.23, whereas the average risk factor is around 1.41. AI and ML improve financial accounting requirements and the long-term growth of e-commerce systems in Peru.

A critical review of domestic and foreign methodological aspects of financial strategies formation using a matrix method in modern realities

Subject. The article discusses financial strategies in the corporate finance management system based on a matrix approach. Objectives. The purpose is, after the analysis of existing financial models in economics that are based on the matrix approach, to identify positive and negative sides of each toolkit, while offering criteria for their comparison, formulated on regional, sectoral, and behavioral aspects. Methods. The study employs theoretical (analysis, synthesis, modeling, systematization) and empirical (comparison, classification by characteristics, mathematization) methods of scientific research. Results. I analyzed economic scientific literature concerning domestic and foreign methods of corporate finance management, defined the terms "financial model" and "financial matrix", compared domestic and foreign instruments, developed criteria for evaluating the existing techniques. The criteria enabled to reveal advantages and disadvantages of the matrix strategies mentioned above. Conclusions. A specific feature of domestic matrix methods is diversification. Moreover, almost all existing financial models are marked by universality: industry differences are ignored. However, each methodological toolkit has its "pros" and "cons", various indicators and financial strategies. At present, there is a need to form a new methodological technique based on the matrix approach. This toolkit can be presented in the form of a financial model characterized by precision and optimality, and would also consider such parameters as industry and regional specifics.