Market Risk Assessment Research Articles

Financial subsidy optimization to promote renewable energy development for sustainable development goal

Enhancing the efficacy of financial subsidies holds paramount significance in fostering sustainable development due to their influence on energy utilization, installation scalability, and the crucial pursuit of decarbonization. Despite a surge in scholarly interest surrounding financial subsidies for renewable energy, the literature studies fall short in comprehensively addressing the factors that impact on the accuracy and reliability of the model for identifying optimal financial subsidies. This paper introduces a robust approach to optimize financial subsidies to achieve the predefined target for renewable energy installation, considering the equilibrium between investor remuneration and government subsidy outlay. Center to this approach is the integration of a forward-looking price forecasting model and the establishment of an economic evaluation approach based on empirical data encompassing weather patterns, technical factors, investor expectations, subsidy disbursements, market risk assessments, and other pertinent parameters. The case study of Taiwan is conducted, and the results indicate that, in the baseline scenario, the financial subsidy is only effective in the risk-neutral market with a small solar system; in the mix scenario, the benefit of energy system increased at least 4.5%, however, the mix scenario is still ineffective to encourage the renewable energy development; and in the optimal scenario, by providing accurate recommendations for optimal financial subsidies, government can achieve the predefined target, even with reduced subsidies due to declining installation costs.

Dynamic Energy Management and Control of Networked Microgrids based on Load to Grid Services and Incentive-Based Demand Response Programs: A Multi-Agent Deep Reinforcement Learning Approach

This study has presented the energy management paradigm in a networked microgrid structure based on L2G services and considering incentive-based load response (IBDR) programs and energy market requirements to reduce operating costs, control and restore voltage and frequency index, providing the benefits of subscribers and distribution system operators. In this study, multi-objective functions such as optimal operation based on IBDR structure and energy market requirements, risk assessment, and L2G service approach are configured in the framework of central and local controllers. Optimal operation and risk assessment are analyzed by a multi-task learning algorithm based on multi-objective function and L2G service policies are evaluated based on multi-agent deep reinforcement learning. Control policies are sent by the communication system to the components affecting the optimal power distribution as well as the voltage and frequency controllers. L2G services have been evaluated in different scenarios such as plug-and-play operating conditions, load fluctuations, and operating in island mode. The results of optimal operation based on L2G services show that the IBDR program implementation reduces the total operation cost by 21%. Also, the total operating cost of the proposed framework is 13.97% less than the RL method and 27.8% less than the ANN method.

Theoretical and Methodological Foundations of Uncertainty Modeling in Real Estate Markets

Motivation: The need to improve the accuracy and reliability of market valuation and risk assessment in real estate markets, especially under conditions of uncertainty.Aim: To integrate theoretical foundations and methodological approaches for modeling aleatoric and epistemic uncertainties in real estate markets using credal networks and confidence boxes (c-boxes).Approach: This paper presents a comprehensive theoretical and methodological framework for uncertainty modeling in real estate markets, focusing on the application of credal networks and confidence boxes. It does not include empirical validation or practical case studies, instead providing a detailed conceptual and methodological discussion.Results: The proposed method demonstrates significant improvements in uncertainty quantification and market analysis accuracy in theoretical terms, offering valuable insights for investors, urban planners, and policymakers. However, empirical validation is suggested for future research to confirm practical applicability.

Influence of Big Data Analytics on Decision-Making Processes in Financial Firms in South Africa

Purpose: The aim of the study was to assess the influence of big data analytics on decision-making processes in financial firms in South Africa. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: By harnessing vast volumes of structured and unstructured data, these firms can derive valuable insights into market trends, customer behavior, and risk assessment with unprecedented accuracy and speed. This capability enables more informed and data-driven decision-making across various facets of financial operations, including investment strategies, risk management, and customer relationship management. Moreover, big data analytics facilitates the identification of previously hidden patterns and correlations within financial data, enhancing predictive modeling and forecasting capabilities. This proactive approach allows firms to anticipate market shifts, optimize portfolio performance, and mitigate potential risks effectively. Additionally, the adoption of advanced analytics techniques, such as machine learning and artificial intelligence, further enhances decision-making by automating processes and refining predictive accuracy. Implications to Theory, Practice and Policy: Diffusion of innovation theory, technology acceptance model and resource-based view may be used to anchor future studies on assessing influence of big data analytics on decision-making processes in financial firms in South Africa. In practice, financial firms should establish robust data governance frameworks to ensure data accuracy, privacy, and security. Policymakers should develop and enforce regulatory frameworks that support the ethical and effective use of BDA in financial firms.

Multi-Elemental Analysis of Edible Insects, Scorpions, and Tarantulas from French (Online) Market and Human Health Risk Assessment Due to Their Consumption: A Pilot Study.

Edible insects are becoming increasingly popular as protein alternatives to traditional animal-based products. As such, information on their elemental composition is important to ensure they are safe for human consumption. This article describes the development and validation of a rapid, reliable method for the simultaneous determination of 19 elements (Al, As, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Pb, Se, Sr, and Zn) in edible insects by inductively coupled plasma mass spectrometry (ICP-MS) following closed vessel microwave digestion. The method was validated using three insect certified reference materials, namely black soldier fly larvae meal (BFLY-1), cricket flour (KRIK-1), and mealworm powder (VORM-1). The method was applied to analyze twelve different (whole) insect species. The maximum amount of each sample was calculated for As, Cd, and Pb with respect to their provisional tolerable daily intake values established by the Food and Agricultural Organization/World Health Organization. Most of the samples, except for scorpions and tarantulas, were safe to consume at large doses (1000-10,000 insects per day). Furthermore, most of the samples contained high levels of Fe, K, Na, and Zn, providing a preliminary overview of the nutritional profile of these novel protein alternatives.

Dynamic financial modeling and feasibility studies for affordable housing policies: A conceptual synthesis

The escalating demand for affordable housing necessitates innovative approaches to financial modeling and feasibility analysis to ensure the success and sustainability of housing policies. This abstract presents a conceptual synthesis of dynamic financial modeling and feasibility studies, exploring their pivotal role in shaping effective affordable housing policies. Dynamic financial modeling involves creating flexible and adaptive financial models that account for varying economic conditions, policy changes, and market fluctuations. These models are essential for predicting financial outcomes, assessing the viability of housing projects, and guiding investment decisions. By incorporating real-time data and scenario analysis, dynamic financial models help stakeholders understand potential risks and opportunities, enabling more informed decision-making. Key components of dynamic financial modeling include cash flow analysis, sensitivity testing, and scenario planning, which together provide a comprehensive view of a project's financial viability over time. Feasibility studies complement financial modeling by evaluating the practical aspects of affordable housing projects, such as site selection, regulatory compliance, and market demand. These studies assess the alignment of housing projects with community needs and regulatory requirements, ensuring that proposed developments are both feasible and beneficial. Feasibility studies often include market analysis, cost estimates, and risk assessments, providing a detailed understanding of project constraints and opportunities. The synthesis of dynamic financial modeling and feasibility studies offers a robust framework for evaluating affordable housing policies. It allows for the integration of financial projections with practical considerations, ensuring that policies are not only economically viable but also aligned with social and regulatory objectives. Case studies from various regions demonstrate the effectiveness of this approach in identifying successful strategies and addressing common challenges. In conclusion, dynamic financial modeling and feasibility studies are crucial tools for developing and implementing effective affordable housing policies. They provide a comprehensive understanding of financial and practical dimensions, facilitating informed decision-making and enhancing the sustainability and impact of affordable housing initiatives. This conceptual synthesis underscores the importance of integrating these methodologies to achieve long-term success in addressing housing affordability challenges. Keywords: Policies, Affordable Housing, Feasibility Studies, Dynamic Financial Modeling, conceptual Synthesis.

Perbandingan Kinerja Keuangan Menggunakan Risiko Pasar pada PT PAN Indonesia Bank Sebelum dan Selama Covid-19 Periode 2017-2022

This research aims at determining the difference in the results of market risk assessments at PT Pan Indonesia Bank Before and During Covid-19 Period 2017-2022. The data used in this research are secondary data obtained from the Financial Statements of PT Pan Indonesia Bank. The data analysis method used is Inherent Risk Assessment of Market Risk in accordance with SEBI No.13/24/DPNP and SEOJK No.14/SEOJK.03/2017. The results in this research show that there are differences in the acquisition of market risk of PT Pan Indonesia Bank Before and During Covid-19. Market risk rating before Covid-19 (2017-2019) is rating 1 (Low) in 2017 then increased to a rating of 2 (Low to Moderate) in 2018 and fell again in 2019 to a rating of 1 (Low). Meanwhile, During Covid-19 (2020-2022) bank's market risk stabilized with a rating of 1 (Low).

A Study of Stock Market Risk Assessment Using Big Data

A Study of Stock Market Risk Assessment Using Big Data

Strategic financial decision-making in sustainable energy investments: Leveraging big data for maximum impact

In the context of escalating environmental concerns and the transition towards a greener economy, sustainable energy investments have emerged as a pivotal area for financial growth and innovation. This paper outlines a strategic framework for financial decision-making in sustainable energy investments, emphasizing the transformative role of big data. By integrating big data analytics into the investment process, stakeholders can enhance market analysis, risk assessment, performance monitoring, and predictive modeling, leading to more informed and effective investment strategies. The paper delves into various big data sources, analytical tools, and technologies that facilitate the collection, processing, and interpretation of vast amounts of information. Additionally, it presents case studies illustrating successful applications of big data in solar and wind energy projects, highlighting best practices and common challenges. The discussion extends to future trends, including advancements in artificial intelligence and machine learning, which are poised to further revolutionize the sector. The paper concludes with strategic recommendations for developing a data-driven investment approach, building robust data infrastructures, and fostering a culture of continuous learning and adaptation. By leveraging big data, investors can maximize the impact of their investments, drive sustainable growth, and contribute to the global energy transition. Keywords: Sustainable Energy Investments, Big Data Analytics, Strategic Financial Decision-Making, Market Analysis, Risk Assessment, Predictive Modeling.

Optimizing Trade Strategies: The Interactive Trade Decision Making Using Weighted Sum Method

The interactive nature of trade decision making enables us to navigate the complexities of international commerce with agility and precision. Through the power of technology and data-driven analysis, we can optimize our strategies, adapt to market conditions, and seize opportunities in real-time. Interactive trade decision making involves a dynamic process that integrates various factors such as market trends, economic indicators, risk assessment, and competitive analysis. It empowers traders to actively engage with the market, gather crucial information, and respond swiftly to changing circumstances. By harnessing advanced tools and algorithms, we can analyse vast amounts of data, uncover patterns, and make informed decisions that maximize profitability and minimize risks. This interactive approach transcends traditional models, allowing us to actively participate in trade negotiations, explore different scenarios, and assess the potential outcomes of our decisions. It encourages collaboration and knowledge sharing, enabling us to leverage collective intelligence and gain valuable insights from experts and peers. With real-time market data and interactive platforms at our fingertips, we can simulate, test, and refine our strategies, ensuring that our trade decisions are based on accurate information and thoughtful analysis.The significance of interactive trade decision making in research lies in its ability to revolutionize the way businesses and individuals approach international trade. We will use the Weighted Sum Method (WSM) in this study, which is a research approach that gives weights to various factors and combines them to make conclusions based on their relative relevance in a weighted way. Alternative parameters taken as Policy, Environment, Economy, Ability. Evaluation parameters parameters taken as Local investment, Technology transfer, Local procurement, Cooperate with R&D, Training, International marketing assistance”. show the final rank of this paper the Economy is in 2nd rank, the Environment is in 4th rank, the Ability is in 3rd rank, the Policy is in 1st rank, The final result is done by using the WSM method. In conclusion, interactive trade decision making is a powerful approach that leverages technology, real-time market information, and data analysis to enable informed and agile decision making in international trade.

Optimizing Trade Strategies: The Interactive Trade Decision Making Using Weighted Sum Method

The interactive nature of trade decision making enables us to navigate the complexities of international commerce with agility and precision. Through the power of technology and data-driven analysis, we can optimize our strategies, adapt to market conditions, and seize opportunities in real-time. Interactive trade decision making involves a dynamic process that integrates various factors such as market trends, economic indicators, risk assessment, and competitive analysis. It empowers traders to actively engage with the market, gather crucial information, and respond swiftly to changing circumstances. By harnessing advanced tools and algorithms, we can analyse vast amounts of data, uncover patterns, and make informed decisions that maximize profitability and minimize risks. This interactive approach transcends traditional models, allowing us to actively participate in trade negotiations, explore different scenarios, and assess the potential outcomes of our decisions. It encourages collaboration and knowledge sharing, enabling us to leverage collective intelligence and gain valuable insights from experts and peers. With real-time market data and interactive platforms at our fingertips, we can simulate, test, and refine our strategies, ensuring that our trade decisions are based on accurate information and thoughtful analysis.The significance of interactive trade decision making in research lies in its ability to revolutionize the way businesses and individuals approach international trade. We will use the Weighted Sum Method (WSM) in this study, which is a research approach that gives weights to various factors and combines them to make conclusions based on their relative relevance in a weighted way. Alternative parameters taken as Policy, Environment, Economy, Ability. Evaluation parameters parameters taken as Local investment, Technology transfer, Local procurement, Cooperate with R&D, Training, International marketing assistance”. show the final rank of this paper the Economy is in 2nd rank, the Environment is in 4th rank, the Ability is in 3rd rank, the Policy is in 1st rank, The final result is done by using the WSM method. In conclusion, interactive trade decision making is a powerful approach that leverages technology, real-time market information, and data analysis to enable informed and agile decision making in international trade.

Leveraging probability and statistical algorithms for enhanced financial risk management

This paper explores the foundations and applications of quantitative analysis in financial risk management. It examines the pivotal role of probability theory, statistical inference, and advanced algorithms in identifying, quantifying, and mitigating financial risks. Key concepts such as the Normal, Poisson, and Binomial distributions are discussed in the context of risk analysis, alongside statistical inference methods like hypothesis testing and confidence intervals. Furthermore, the paper investigates the application of portfolio optimization models, credit risk evaluation techniques, and market risk assessment methodologies in practical risk management scenarios. Additionally, it addresses the challenges posed by model risk, data quality, and regulatory compliance, emphasizing the need for rigorous validation, robust data governance, and ethical considerations in risk management practices. By integrating sophisticated quantitative techniques with real-world applications, financial institutions can enhance their ability to navigate the complexities of modern financial markets and achieve more effective risk management strategies.

THE INVESTMENT PATTERN AND BANKING NEEDS OF THE HOUSING SOCIETIES.

THE INVESTMENT PATTERN AND BANKING NEEDS OF THE HOUSING SOCIETIES THE INVESTMENT PATTERN AND BANKING NEEDS OF THE HOUSING SOCIETIES. UNDER THE GUIDANCE OF Prof. Reji john SOB, GALGOTIAS UNIVERSITY SUBMITTED BY Bineet yadav 22GSOB2010640 MBA 2022-2024 SCHOOL OF BUSINESS GALGOTIAS UNIVERSITY ABSTRACT This study delves into the investment patterns and banking requirements of housing societies, examining their financial management practices for sustainability and growth. Through a comprehensive analysis of market dynamics, company performance, risk assessment, valuation methods, investment strategies, and portfolio management, this research aims to provide insights into effective financial decision-making within the housing society sector. • Market Analysis: The market analysis section explores the macroeconomic factors influencing the housing society sector, including demographic trends, urbanization rates, regulatory environment, and economic indicators. It assesses the demand for residential properties, rental yields, and competition among housing societies. Additionally, it examines the impact of market volatility and economic cycles on investment decisions within the sector. • Company Analysis: Company analysis focuses on the financial performance and operational efficiency of housing societies. It evaluates key metrics such as revenue growth, profit margins, asset utilization, and liquidity ratios to assess the financial health of individual housing societies. Furthermore, it examines governance structures, management capabilities, and strategic initiatives undertaken by housing societies to enhance shareholder value and mitigate risks. • Risk Assessment: Risk assessment involves identifying and evaluating various risks inherent in investing in housing societies. These risks may include market

Energy Market Prediction and Risk Assessment Based on China's Rural Collective Economy

INTRODUCTION: Energy, as a core element supporting the functioning of modern society, is vital to the development of the rural collective economy. With the upgrading of the agrarian industrial structure and the improvement of rural electrification levels, the energy demand gradually increases. Therefore, for China's rural collective economy, an in-depth study of the forecasting and risk assessment of the energy market has essential theoretical and practical value for scientific planning of resource allocation and improving energy utilization efficiency.
 OBJECTIVES: This study aims to reveal the development trend and key influencing factors through an in-depth analysis of China's rural collective economy's energy market and to make scientific forecasts of the future development of the energy market. At the same time, through risk assessment, it proposes risk prevention and resolution countermeasures of the energy market for the rural collective economy to provide decision support for rural energy security and sustainable development.
 METHODS: This study adopts a comprehensive analysis approach, combining historical data, policy literature analysis, and expert interviews. First, a comprehensive analytical framework is established by combing the development history of the rural collective economy energy market over the past few years. Second, quantitative analysis models and numerical simulations are used to analyze the key factors affecting the energy market. Finally, expert interviews are conducted to obtain the views of experts in related fields on the future development and risks of the energy market to improve the research conclusions further.
 RESULTS: The results of the study show that the energy market of China's rural collective economy will show a trend of gradual growth, but it also faces multiple risk challenges, including market price fluctuations, policy adjustments, and an imbalance between supply and demand. In the future, with the deepening of green energy policies, rural collective economies will pay more attention to the application of clean and renewable energy.
 CONCLUSION: To summarize, this study provides a scientific reference for the energy strategy decision-making of rural collective economies by forecasting and assessing the risk of the energy market based on China's rural collaborative economies. In the future, it is necessary to pay more attention to the improvement of the policy system to promote the development of green energy, as well as the establishment of a sound market regulatory mechanism to reduce the uncertainty of the energy market and provide solid support for the sustainable development of the rural collective economy.

Commercial roadmap of nanobiosensor development

A nanobiosensor is a tool that converts a biological stimulus into an electrical output via nanosized transducer elements. Nanobiosensors are promising instruments, especially in biomedical applications in the literature and industry. To develop a nanobiosensor from idea to product, a life-cycle approach that comprises various processes ranging from conception through commercialization is required. Developers and potential investors should examine market requirements, design possibilities, feasibility, financial return, and risk assessments when developing a nanobiosensor development concept. It is critical to establish a well-defined regulatory pathway for bringing innovation to market at a low cost and in a short period. R&D should conduct thorough examinations of nanomaterial toxicity and health effects, involving marketing, advertising, and financial analysis. Stakeholders should discuss technology transfer office protocols for faster, healthier operations.

Optimization of Business Decision Accuracy through the Application of Mathematical Economics

In the dynamic landscape of contemporary business environments, the need for precise decision-making has become paramount for organizational succes. It explores the strategic integration of mathematical economics as a powerful tool for enchancing decision accuracy in business. Mathematical economics, a discipline that combines economic theories with mathematical methodologies, offers a structured framework to model, and analyze a complex business strategy decision. This study delves into a theorical foundations of mathematical economics and investigates it practical applications in real-world business decision-making processes. Through the utilization of mathematical model, optimization techniques, and statistical analyses, businesses can gain insights into market dynamics, resource allocation, and risk assessment. The objective is to establish a systematic approach that aids decision-makers in formulating well-informed strategies, minimizing uncertaintes, and maximizing overall business performance.

Technical Indicators of Major Healthcare Companies: A Comprehensive Analysis

This research aims to provide a comprehensive analysis of major healthcare stocks along with the nifty health care index within the healthcare sector. Employing technical indicators such as the Moving Average Convergence Divergence (MACD), Rate of Change (ROC) and Relative Strength Index (RSI) we conduct a detailed trend analysis to identify historical patterns and potential turning points in stock prices. Furthermore, advanced time series forecasting models and predictive analytics are utilized to forecast future share prices for the selected three companies, offering valuable insights for investors and market participants.
 In addition, we investigate the relationship between the stock prices of Apollo Hospital, Fortis Healthcare & Narayana Hruda and the Nifty Healthcare Index. This analysis sheds light on how individual stock prices are influenced by broader sectoral performance. We also explore the impact of MACD, ROC, and RSI technical indicators on the prices of all healthcare stocks through rigorous statistical analysis. Our findings provide healthcare sector stakeholders with a deeper understanding of stock behaviour, potential future movements, correlations with market indices and risk assessment.
 The results of this study enhance the decision-making process for investors and market participants in the healthcare sector. By examining historical trends, forecasting price dynamics, assessing sectoral correlations, evaluating technical indicator effects and risk measurement, this research contributes to a more comprehensive view of these stocks and their positioning within the healthcare industry.

The Wax and Wane of CPO Prices in India: A DCC Approach to Understanding Market Sentiment

This study provides a detailed analysis of Crude Palm Oil (CPO) price dynamics, focusing on the relationship between spot and future prices during significant global events, notably the COVID-19 pandemic and the Ukraine-Russia conflict. Utilizing Dynamic Conditional Correlation (DCC) models, the research examines conditional returns and covariance, as well as the correlation trends in the Crude Palm Oil (CPO) market. The study reveals critical insights into market behavior during these tumultuous times. For instance, sharp increases in covariance during key periods suggest strong movement synchronicity between spot and future prices, potentially driven by market-altering events. The onset of COVID-19 and the escalation of the Ukraine-Russia conflict are identified as significant influencers, disrupting global supply chains and impacting global food supplies, which in turn affect both spot and future Crude Palm Oil (CPO) prices. Additionally, the research finds a dynamic correlation trend between the future and spot prices of Crude Palm Oil (CPO). Starting with a strong positive correlation in 2018, the study observes a gradual decrease in this correlation, especially after 2020. This trend indicates diverging market forces or perceptions, particularly in response to the COVID-19 pandemic and the ongoing Ukraine-Russia conflict. Such fluctuations in correlation suggest varying market sentiments and expectations, which have implications for market risk assessment, hedging strategies, and speculative activities

Dynamics of market states and risk assessment

Previous research explored various conditions of financial markets based on the similarity of correlation structures and classified as market states. We introduce modifications to previous selection criteria for these market states, mainly due to increased attention to the transition matrix between the states. Clustering and thus market states are fixed by the optimization of two parameters — number of clusters and noise-suppression, but in similar conditions, we give preference to the clustering which avoids large jumps in the transition matrix. We found statistically significant results applying this model to the S&P 500 and Nikkei 225 markets for the pre-COVID-19 pandemic era (2006–2019). Retaining the epoch length of 20 trading days but reducing the shift of the epoch to a single trading day we are led to the concept of a trajectory of the market in the space of correlation matrices. We may visualize these states after dimensional scaling to two or three dimensions. This approach, using dynamics, improves the options of risk assessment, opens the door to dynamical treatments of markets (e.g. hedging), and shows noise-suppression in a new light.

The impact of joint events on oil price volatility: Evidence from a dynamic graphical news analysis model

The analysis of event-oriented oil market risk deserves attention owing to the diversity, complexity and time variant of the oil-related events. From the aspect of oil price volatility, we examine the dynamic impact of joint events of different aspects on the oil market using graph analytical models and the proposed value-at-risk estimation approach. Based on the construction and evaluation of news indices, we confirm that events of six categories have strong short-term causality with oil price returns. Furthermore, we prove the capacity of event-oriented indices on oil market risk assessment tasks. Demand and energy-based events have shown their effect in improving oil market risk estimation, as proved by empirical analysis of assessing value-at-risk of Brent and WTI oil price. This paper confirms the impact of oil-related joint events on oil market risk and provides a new perspective of assessing latent oil market risks associated with exogenous events.