Market Conditions Research Articles

Global Economic Policy Uncertainties and International Oil Price Volatility Under Alternative Market Conditions

This study mainly investigates the predictability prowess of global economic policy uncertainty (GEPU) for oil price volatility under different market episodes. The results clearly show that the GEPU-oil price volatility nexus is episodic in nature. While evidence of mean reverting stance is confirmed across the sectionalized market phases, the long-term impact of GEPU in the nexus holds true for the Covid-19 pandemic, Russia-Ukraine war, and oil price shock episodes.

A data-driven operations management model: Implementing MIS for strategic decision making in tech businesses

In today’s rapidly evolving technological landscape, data-driven decision-making is critical for business growth and competitiveness. This paper presents a comprehensive operations management model that integrates Management Information Systems (MIS) to facilitate strategic decision-making in technology firms. The proposed model emphasizes the transformation of raw data into actionable insights, enabling organizations to optimize their operations and respond effectively to market dynamics. By systematically incorporating MIS into operational frameworks, the model provides a structured approach to data collection, analysis, and dissemination. It identifies key performance indicators (KPIs) that are essential for monitoring business performance and aligning operational activities with strategic objectives. Through case studies of leading tech companies, the model illustrates how data-driven methodologies can lead to improved forecasting, resource allocation, and overall operational efficiency. The framework is designed to be adaptable, allowing technology firms to customize the MIS components based on their unique business needs and market conditions. Key features of the model include real-time data analytics, predictive modeling, and user-friendly dashboards that enhance visibility across departments. These components empower decision-makers with timely and relevant information, fostering a culture of informed decision-making at all organizational levels. Moreover, the model addresses common challenges faced by tech businesses, such as data silos and integration issues. By promoting a centralized data repository and standardized reporting mechanisms, it enhances collaboration among teams and ensures consistency in data interpretation. The implications of this model extend beyond operational efficiency, influencing strategic initiatives such as product development, market expansion, and customer engagement. In conclusion, this data-driven operations management model not only supports strategic decision-making in technology firms but also positions them for sustainable growth in a competitive marketplace. By leveraging MIS effectively, organizations can harness the power of data to drive innovation and enhance overall business performance.

A Novel Deep Learning Model for Stock Prediction Integrating Historical Prices, Core Volatility Trends and Financial News

Accurate stock price forecasting is crucial for investors and analysts aiming to make informed decisions and mitigate risks in financial markets. While deep learning models have proven effective in identifying complex patterns in quantitative data, they often struggle to account for non-quantitative factors such as corporate events, market sentiment, and regulatory changes that can cause unpredictable stock price movements. To address this challenge, an ensemble model is introduced that integrates historical price data, financial news, and stock fundamentals to predict next-day stock prices. The model combines one-dimensional Convolutional Neural Networks (1D CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs), applied independently to each data stream and concurrently as a unified ensemble, to capture both short-term and long-term market behaviors. NVIDIA (NVDA) stock was selected for testing due to its high volatility and rapid price fluctuations, presenting a challenging case for predictive modeling. Using data spanning from April 2014 to March 2024, the model achieved a coefficient of determination (R²) of 0.983 and a mean absolute error (MAE) of $12.72. These results suggest that the ensemble approach effectively captures both quantitative and qualitative factors, making it a promising tool for improving stock price prediction in volatile market conditions.

Building a theoretical framework for financial risk management in emerging markets: Applying global best practices to the oil and gas industry in developing economies

Financial risk management is crucial for the sustainability and growth of the oil and gas industry in developing economies, where volatility and regulatory uncertainties present significant challenges. This abstract explores the construction of a theoretical framework for financial risk management, adapting global best practices to the specific contexts of emerging markets within the oil and gas sector. In developing economies, the oil and gas industry faces unique risks such as geopolitical instability, currency fluctuations, and regulatory changes. Effective financial risk management mitigates these risks through proactive strategies that balance risk exposure with strategic objectives. Adopting global best practices involves the integration of comprehensive risk assessment methodologies, scenario planning, and hedging techniques tailored to local market conditions. Key components of the theoretical framework include risk identification through comprehensive analysis of market, operational, and financial factors. This approach enables oil and gas companies to anticipate and mitigate risks before they escalate, ensuring financial stability and resilience in volatile environments. Furthermore, the application of global best practices in financial risk management fosters transparency and accountability, enhancing investor confidence and facilitating access to capital. By aligning risk management strategies with international standards and benchmarks, developing economy oil and gas firms can navigate uncertainty more effectively, thereby optimizing operational performance and sustaining long-term growth. However, challenges such as limited access to sophisticated financial instruments and expertise may hinder effective implementation. Overcoming these barriers requires capacity building, collaboration with experienced partners, and continuous adaptation of strategies to evolving market dynamics. In conclusion, building a theoretical framework for financial risk management in the oil and gas industry in developing economies is essential for mitigating volatility and maximizing opportunities. By applying global best practices tailored to local contexts, companies can strengthen their resilience, attract investment, and drive sustainable development in emerging markets.

Forecasting stock index closing points using ARIMA-GARCH with a rolling data window

This research develops and examines the efficacy of a hybrid ARIMA-GARCH model, augmented by a rolling data window approach, to enhance the accuracy of stock index prediction, specifically focusing on the NEPSE index. Accurate predictions of stock indices are of paramount importance to investors, analysts and policy makers to navigate and circumvent the market uncertainties. The AutoRegressive Integrated Moving Average (ARIMA) model captures linear trends and temporal dependencies in time series data, while the General AutoRegressive Conditional Heteroskedasticity (GARCH) model addresses volatility clustering—ubiquitous character of financial time series—thereby providing a comprehensive framework for prediction. Utilizing a dataset comprised of daily closing points of NEPSE index approximately three years and nine months, the study identifies the ARIMA (5,1,0)-GARCH (1,1) model as optimal fit, upon integrating 180-day rolling data window. This model achieved a Mean Percentage Error of -0.0058% and a correlation of 0.995, which is indicative of superior fit to the underlying time series data. These findings underscore the hybrid model’s capacity to adaptively respond to dynamic market conditions and acclimatize prediction parallel to most recent market trends and volatility. This research is useful for optimizing investment strategies for those invested in Nepalese stocks. Also, this research lays a foundational framework for future investigations into application of this advanced forecasting method in other emerging markets, financial instruments and indices.

Data analytics as a driver of digital transformation in financial institutions

Data analytics plays a pivotal role in driving digital transformation within financial institutions, enabling organizations to leverage insights from vast amounts of data to enhance decision-making and optimize operational performance. This paper explores the multifaceted impact of data analytics on the financial services sector, highlighting how it can improve customer experience, streamline processes, and foster innovation. By implementing advanced analytics techniques, financial institutions can better understand customer behaviour, identify market trends, and personalize services to meet evolving customer needs. The study discusses effective strategies for integrating data analytics into decision-making processes and operational workflows, emphasizing the importance of creating a data-driven culture that encourages collaboration across departments. Case studies of leading financial institutions that have successfully integrated data analytics into their operations illustrate the transformative outcomes, including improved customer satisfaction and increased revenue growth. Additionally, the paper addresses challenges such as data silos, regulatory compliance, and the need for skilled personnel to analyse and interpret data effectively. To overcome these hurdles, best practices are outlined, including investment in robust data infrastructure, fostering cross-functional teams, and ensuring continuous training for employees. The findings indicate that a strategic approach to data analytics not only enhances performance but also positions financial institutions to adapt to rapidly changing market conditions. Ultimately, this paper underscores the critical role of data analytics in shaping the future of financial services and achieving sustainable growth in an increasingly competitive landscape.

An Introduction to Lagrange Multipliers: Theory and Applications in Economics

The purpose of this paper is to explore the basic applications of the Lagrange multiplier method in economics and to help beginners build their understanding of this mathematical tool. Tesla Inc. is used as a case study to examine how it maximizes profits by optimizing pricing and marketing expenses under given market conditions and constraints. The context of the study includes the importance of maintaining Tesla’s leadership position as the world’s leading electric car manufacturer in a competitive market. In this paper, the Lagrange multiplier method is used to incorporate specific market share and marketing expense constraints into the profit function and to find the extreme values by constructing the Lagrange function and solving for the derivatives. However, the results of the study show that the solutions obtained are extreme minima rather than the expected maxima. This result highlights the possible limitations of the Lagrange multiplier method in economic optimization problems. Nonetheless, this paper provides valuable lessons for understanding and applying the Lagrange multiplier method and points the way for further research on the application of optimization methods in economic decision making.

South Africa’s crowded cannabis market: identifying new market opportunities for Ilco Farming’s “green gold”

Learning outcomes Following a discussion of the case, students should be able to analyse competitive dynamics: provide an in-depth critical analysis of Ilco Farming’s microenvironment, using the structure–conduct–performance framework; evaluate strategic positioning: conduct a SWOT analysis of Ilco Farming’s medicinal cannabis business; and develop strategic approaches: propose actionable strategies that would provide effective solutions to the problem of constrained market conditions currently faced by Ilco Farming. Case overview/synopsis Coenie and Ilse Venter established Ilco Farming, a cannabis farm located in the Viljoenskroon district in the Free State province in South Africa, in 2021. From the beginning, they poured their hearts and souls into their new venture, which soon paid off. A few short months after Ilco Farming began operating, despite the presence of other large competitors, Ilco Farming supplied a large share of the domestic medicinal market with flower heads. But then an unexpected challenge presented itself. In March 2023, Ilco Farming was operating at only 23% (600 m2) of its production capacity of 2600 m2 and had considerable room for growth, the local market – at least the local legal market – for cannabis began to show signs of saturation. Coenie and Ilsa found themselves at the proverbial crossroads, grappling with the crucial decision of how to secure their farm’s future in the face of a fast-saturating local (legal) cannabis market and a thriving (illegal) black market. Coenie and Ilse refused to entertain the idea of going the black market route, as they were unwilling to risk losing their operating licence. They calculated that the farm would reach breakeven point within the next two years, with profits unlikely during this period. Should they persist with their current strategy of producing high-quality products and delivering a superior service in the hopes of growing their market share? Or should they consider other strategic options? Coenie and Ilse were sitting at their boardroom table having a cup of coffee and looking out of the window at Ilco Farming’s impressive SAHPRA- and GAP-approved warehouse and tunnels. “What should we do?” they both wondered. Complexity academic level The case study can be used in postgraduate courses in microeconomics (PGDIP/MBA) and agricultural economics (PGDIP/MBA). Supplementary materials Teaching notes are available for educators only. Subject code CSS 5: International business.

Exploring Ganoderma lucidum: morphology, cultivation and market potential.

Ganoderma lucidum, known as the "mushroom of immortality," is a white rot fungus renowned for its medicinal properties, attributed to its bioactive compounds. Although species with similar morphological traits to G. lucidum are found across the globe, precise identification is made possible through DNA barcoding and molecular phylogenetic analysis. Global cultivation and wild harvesting of G. lucidum are both done in response to the growing market needs. Artificial cultivation is typically performed on sawdust, but other woody substrates and the wood log method are also employed. This cultivation leverages the fungus's ecological role in converting industrial and agricultural solid wastes into biomass, thereby producing functional food and potential pharmaceutical sources. The review consolidates research on various aspects of, including cultivation methods (sawdust, agricultural waste, wood logs, and submerged fermentation), and the current global market conditions.

Harnessing nature-based solutions for economic recovery: A systematic review

Nature-based solutions (NbS) involve working with nature to address societal challenges in ways that benefit communities and biodiversity locally. However, their role supporting economic recovery from crises, such as those arising from conflicts or pandemics remains underexplored. To address this knowledge gap, we conducted a systematic review of 66 reviews on the economic impact of nature-based interventions. Most demonstrated positive outcomes for income and employment, though those with critical appraisal of underlying studies reported more mixed outcomes. These varied results were influenced by factors such as the balance between short-term and long-term gains, market conditions, regional effects, reliance on subsidies, and discrepancies between expected and actual economic benefits. National-scale economic growth assessments were scarce. Half of the cases featured nature-based food production investments, with much evidence from sub-Saharan Africa, East Asia and the Pacific. The few reviews comparing NbS with alternatives found that NbS delivered equal or better economic outcomes. NbS also provided broader benefits like food and water security, flood protection and community empowerment. We identified key factors influencing the delivery of benefits and trade-offs, finding that NbS must adhere to best practice standards, with community involvement being critical for equitable outcomes. Well-designed NbS can create diverse job opportunities at different skill levels, diversify income, and improve resilience, offering a rapid, flexible response to economic shocks that can be targeted at deprived communities. By integrating traditional, local and scientific knowledge, NbS can enable eco-innovation, and drive the transition to a clean and efficient circular economy, with high economic multipliers spreading benefits throughout economies. The evidence underscores the need to incorporate NbS in investment programs to concurrently address economic, environmental, and societal challenges. However, improved monitoring of economic, social and ecological outcomes and the development of comprehensive accounting systems are needed to better track public and private investments in NbS.

Maintaining Agricultural Production Profitability—A Simulation Approach to Wheat Market Dynamics

Profitability is a complex notion, intertwined with theoretical, socio-economic, and modern economic considerations. While traditionally linked to monopoly advantages, contemporary views broaden this to include shareholder value creation. In agriculture, profitability faces impediments such as rising input costs, market volatility, and economic disparities, emphasizing technological innovation, market conditions, and policy as crucial determinants. This study presents an econometric simulation model to analyze and forecast wheat production profitability in Poland from 2023 to 2027, providing insights into production efficiencies, market dynamics, and policy impacts. Using a system of recursive equations, the model forecasts profitability, integrating econometric techniques with expert insights. It examines variables like resource use, production levels, and price changes, validated through FADN data. The findings reveal that price relations and production efficiencies are the key to profitability. The model highlights market volatility, particularly through “price scissors”, as the main factor influencing profitability. Favorable price conditions significantly boost profitability and incentivize production. The model is a critical tool for analyzing wheat profitability, highlighting the influence of market and policy changes. Future work could extend the model’s use to other regions or crops, incorporating advanced technologies to improve accuracy. This study offers valuable insights for agricultural economics, aiding stakeholders in strategic decision-making.

The paradoxical surplus of healthcare workers in Africa: the need for research and policy engagement

Abstract Background In many countries in Africa, there is a paradoxical surplus of under and unemployed nurses, midwives, physicians and pharmacists which exists amidst a shortage of staff within the formal health system. By 2030, the World Health Organisation Africa Region may find itself with a shortage of 6.1 million healthcare workers (HCWs) alongside 700,000 un- or underemployed health staff. This study aims to bring the nature and consequences of an existing paradoxical surplus of HCWs in low income countries into the HCW migration debate and to explore novel solutions. Methods The study draws on a rapid review of the literature and deeper insights from expert information, using examples from Uganda, Kenya and Niger. Results In Niger, between 2010-2014, 55% of health graduates were unable to find permanent work in the sector leaving 15 000 HCWs unemployed or in precarious jobs. In Sudan, an 8-fold increase of the number of places at medical colleges (between 1996-2012) was not accompanied by a commensurate investment in physician positions leading to increased unemployment and brain drain. In Kenya, in 2021, 27243 HCWs were un or under-employed. In Uganda, 20590 nurses and midwives were reported as unemployed in 2023. Despite this, the emphasis in policy debates about the healthcare workforce crisis in most national and global levels is on staff shortage and the need to train more HCWs. In contrast, an existing surplus HCWs and their association with patterns of migration are largely unknown. Little time is given over to understand the economic, political and social factors that have driven their emergence and the governance challenges and potential interventions to improve employment rates. Conclusions The results highlight the complexity of migration, labour market conditions and health systems, in particular in low income countries in Africa. Both qualitative and quantitative data is necessary for policy makers to find effective solutions to the problem.

Analyzing Barriers to Innovation Management Implementation in Sustainable Tourism Using DEMATEL Method

The integration of sustainable practices in tourism is increasingly critical, given the sector’s substantial environmental and social impacts. Although the need for innovation management to promote sustainable tourism is widely recognized, various barriers hinder its effective implementation. This study employs the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology to systematically examine these barriers, identifying their interconnections and assessing their broader implications. The findings indicate that a short-term focus and financial constraints are the primary effect barriers to implementing innovation management in sustainable tourism. Their prominence as endpoints in the flow of impact within the model of interconnected barriers developed in this study underscores the need to further address the deeper, cause-related barriers that propagate these effects, such as inadequate leadership support, resistance to change, poor stakeholder communication and collaboration, insufficient knowledge and skills, and restrictive regulatory policies. Additionally, industry-specific challenges like a fragmented industry structure and fluctuating market conditions exacerbate these difficulties. Therefore, a comprehensive, holistic approach is essential for transitioning the tourism sector towards sustainable practices through innovation management and overcoming inherent financial and strategic barriers. This study advocates adopting the recently published Management System Standard ISO 56001:2024 as a structured innovation management framework capable of systematically and holistically mitigating these barriers. By analyzing the cause-and-effect relationships among the identified barriers, this study ultimately provides valuable insights that aid stakeholders—including policymakers, business leaders, and community representatives—in devising more effective, inclusive, and forward-thinking approaches to innovation in sustainable tourism. This is crucial for fostering a tourism sector that is not only economically beneficial but also environmentally responsible and socially equitable.

Evaluation of Managerial Economics Applications Strategy using the SPSS Method

Managerial Economics is an economy branch is, this economic theory and size using methods solve real-world managerial problems. It helps businesses and organizations make informed decisions by using economic analysis. Here are some common applications of managerial economics: Demand Analysis and Forecasting: Businesses use managerial economics to understand consumer behavior and forecast future demand for their products or services. This involves studying factors that affect demand, such as price, income levels, and consumer preferences. Production and Cost Analysis: Managerial economics helps businesses optimize production processes and minimize costs. It involves analyzing production functions, cost structures, and economies of scale to determine the most efficient way to produce goods or services. Pricing Strategies: A product or the right price for the service It is up to a company to decide Crucial to profitability. Managerial economics helps businesses set prices by considering factors like production costs, competition, and consumer willingness to pay. Market Structure Analysis: Understanding the type of market, a company operates in (e.g., perfect competition, monopoly, oligopoly) is essential for making strategic decisions. Managerial economics helps analyses market structures and determine appropriate strategies for each. Risk and Uncertainty Analysis: Businesses often face uncertainty in various aspects, such as demand fluctuations, production costs, and market conditions. Managerial economics provides tools to assess and manage risks, including techniques like decision tree analysis and expected value calculations. Capital Budgeting and Investment Analysis: Managerial economics assists in evaluating investment opportunities and capital projects. It involves methods Payback period analysis of investments. Regulatory and Government Policy Analysis: Managerial economics helps businesses understand and navigate government regulations and policies that affect their operations. This can include areas like taxation, environmental regulations, and trade policies. Strategic Planning: Using economic analysis, businesses can develop long-term strategies for growth and sustainability. This may involve assessing market trends, competitive dynamics, and potential expansion opportunities. Game Theory and Competitive Strategy: Managerial economics incorporates game theory to analyze strategic interactions between competitors. It helps businesses formulate optimal strategies in competitive environments. Resource Allocation: Efficient allocation of resources, including labor, capital, and technology, is crucial for a company's success. Managerial economics provides tools to distribute funds in a manner that optimisesoverall productivity and profitability. Demand Analysis and Forecasting, Production and Cost Analysis, Pricing Strategies, Market Structure Analysis, Risk and Uncertainty Analysis, Capital Budgeting and Investment Analysis and Regulatory and Government Policy Analysis. The Cranach’s Alpha Reliability result. The overall Cranach’s Alpha value for the model is .744 which indicates 74% reliability. From the literature review, the above 50% Cranach’s Alpha value model can be considered for analysis.

Racial group differences and export market access challenges in agricultural value chains and supply networks of SMEs in South Africa

ABSTRACT Although previous studies provide an insightful and categorised nature of internal and external export barriers experienced by agricultural SMEs in general; there is little, if any, strategic insights on the racial nuances in value chain barriers for most agricultural SMEs that target global export markets. This study posits that export barriers are not uniform and differ based on race and the context in which the agricultural SME is based. Hence, we specifically explore the nature of export barriers per race within the agro-value chains in South Africa and juxtapose these with the dynamic market and historical conditions of the South African market environment. In this multi-case study, we focused mainly on the differences between black and white-owned agricultural SMEs riding on their different supply networks’ capabilities to navigate the export barriers they face. The findings serve to develop seminal knowledge to enhance export capabilities and opportunities for minority groups globally and more importantly to highlight the underlying drivers for racial imbalances in agro-supply chains. Although the findings are geographically skewed to South Africa, the market dynamics are relevant across countries with similar demographic characteristics and provide a platform for future research.

Reflections of Economic Shocks and Price of Crude Oil on Gold Price Volatility within Asian Nations

The focus of this study is to examine the effects of economic shocks along with the crude oil price on gold prices in Asian countries. This study utilizes a regression analysis to measure the relationship between the macroeconomic indicators, crude oil price, and gold price in six selected nations from 1990 until 2022. The validity of the data was diagnostically checked by performing a VIF procedure, autocorrelation test, and normality test on residual, and data were analyzed via EViews software. The analysis showed mixed findings, however, a major variable that positively affects the fluctuation of the gold price is “the crude oil price” and it reflects global economic stability in the six countries. A sudden rise in crude oil prices might trigger conflicts in oil-exporting countries and limit access to material sources. This situation may force investors to move their funds into a safe-haven commodity like gold. The fluctuations in crude oil prices are not only a response to current market conditions but also alarming a lot more fundamental problems in an economy, which include approaching economic recessions, currency devaluation and a major financial crisis. For practical implications, this study suggests that policymakers pay extra attention to this commodity besides other economic indicators like inflation, interest and currency rates, and a GDP rate to ensure a sustainable economic condition. To the best of the authors’ knowledge, this study is the first that examines the economic shocks along with crude oil prices pre-and-post COVID-19 pandemic focusing on a few Asian countries.

Implied volatility curve: an empirical study on Chinese SSE 50 ETF options

ABSTRACT This paper studies the determinants shaping the implied volatility curve of Chinese SSE 50 ETF options and the predictability of its patterns. Initially, the research examines the economic variables influencing the curve’s curvature. Our analysis reveals a pronounced disparity in how investor sentiment and risk appetite impact the curvature of call versus put options’ volatility curves. Subsequently, the study distinguishes four patterns of the volatility curve, exploring the causes of their formation. We observe that shifts in market conditions precipitate alterations in the curve’s patterns, with pessimistic investor sentiment and diminished risk appetite correlating with a more pronounced skewness in the curve. In the final phase, the research employs machine learning techniques to prognosticate the patterns of the volatility curve. The chosen model demonstrates robust proficiency in forecasting Smile and Smirk patterns, yet it notably struggles to predict the Frown pattern, which typically emerges alongside major market emergencies.

Leadership Training in the Era of Digital Transformation: Case Study of Amazon

Abstract: Enterprises are entering the digital transformation era to adapt to rapidly changing market conditions and technological advancements. As organisations undergo digital transformation and reshape their business models and practices, this study explores how digital technology facilitates the development of new business models, changes corporate interaction channels, and the positive impact of digital transformation on leadership training. As a global leader in e-commerce and cloud computing, Amazon provides lessons and insights for other companies' leadership training in digital transformation through an in-depth study of its leadership training models and practices. This study explores in depth the impact of digital transformation on leadership training, analyses the effectiveness and challenges of leadership training in the context of digital transformation, and how to innovate leadership training models in the context of digital transformation. Analysing the case of Amazon enriches and improves the existing leadership training theories and provides new research perspectives and ideas for the academic community.

Developing theoretical models for effective debt service management in high-volatility industries: a focus on risk mitigation and financial stability in the oil and gas sector

Effective debt service management is crucial for maintaining financial stability and mitigating risks in high-volatility industries, particularly in the oil and gas sector. This abstract explores the development of theoretical models aimed at enhancing debt service management practices in such industries. The oil and gas sector is inherently exposed to price volatility, geopolitical uncertainties, and regulatory challenges, necessitating robust strategies for managing debt obligations. This review emphasizes the importance of integrating risk mitigation techniques into debt service management frameworks to safeguard against financial distress and optimize capital structure efficiency. Key theoretical models examined include proactive debt restructuring, dynamic financial modeling, and scenario analysis tailored to the cyclical nature of the industry. These models emphasize adaptive strategies that enable companies to adjust debt repayment schedules based on market conditions and cash flow projections. Additionally, financial stability metrics such as debt-to-equity ratios, liquidity ratios, and debt coverage ratios are utilized to gauge the sector's resilience to external shocks. Incorporating risk management principles into debt service management frameworks enhances decision-making processes, facilitates early detection of financial vulnerabilities, and ensures alignment with long-term strategic goals. Case studies and empirical data illustrate the practical application of theoretical models in optimizing capital allocation and maintaining investor confidence amid industry fluctuations. The theoretical insights presented contribute to a comprehensive understanding of effective debt service management in high-volatility industries, highlighting the role of financial prudence and strategic foresight in sustaining operational resilience. Recommendations are provided for industry practitioners and policymakers to enhance regulatory frameworks, promote transparency, and foster sustainable growth in the oil and gas sector.. Keywords: Debt Service Management, Risk Mitigation, Financial Stability, Oil and Gas Sector, High-Volatility Industries, Theoretical Models, Debt Restructuring, Financial Modeling, Scenario Analysis, Capital Structure

Sustainability and Strategic Positioning in Hydroponic Production

Objective: This study analyzes the strategic positioning of a family farming company, exploring intersections in support of the certification process for the organic production of leafy vegetables. Theoretical Framework: We adopt a qualitative approach to achieve the objective through a case study on a farm in Minas Gerais/Brazil. We present details on hydroponics, the sustainability tripod, and strategic planning. Method: We presented an analysis of strategic planning. We applied case studies and bibliographic research. We employed a qualitative approach based on information obtained through interviews and in-loco observations. Results and Discussion: The results present the analysis of the SWOT matrix that compares the internal conditions of the organization with market conditions. The results highlighted the potential of the company under investigation. The offensive capacity demonstrates excellent conditions for taking advantage of opportunities. The defensive capacity reported the vulnerability to repel threats. Research Implications: This case study highlights the potential of analyzing strategic positioning. By applying the SWOT matrix and calculating strategic positioning, a significant positive value that attests to the offensive capacity in relation to the external environment was observed. Originality/Value: This study discusses the applicability of strategic positioning analysis for hydroponic companie sthrough sustainability actions. The interest in this product is widely known and highlights the relevance of studies to foster the evolution of strategic management in this segment.