Market Fluctuations Research Articles

Study on nonlinear dynamics of Bertrand game between automobile manufacturers under low-carbon policies on both sides of supply and demand

ABSTRACT This paper analyzes the traction effect of dual credit policy on supply-side productivity, as well as the regulatory effect of carbon inclusive benefits and consumer’s low-carbon preference on demand-side market volume. By combining nonlinear dynamics theory with Bertrand game, this paper establishes a dynamic Bertrand game model of new energy vehicle pricing. The results indicate that: (1) A too fast price adjustment can lead to chaotic fluctuations in the automobile market, and plug-in hybrid electric vehicles (PHEVs) are more susceptible to price fluctuations compared to battery electric vehicles (BEVs). (2) The dual credit policy on the supply side can improve the stability of pricing decisions for automobile manufacturers, making BEVs more competitive in the market than PHEVs. (3) From the demand side, the prices of BEVs and PHEVs are complementary. (4) In policy design, the balance between credit trading price and carbon inclusive benefits should be comprehensively considered.

Research on the construction of financial market volatility prediction model in digital economy environment based on machine learning algorithm

Achieving accurate prediction of financial market fluctuations is beneficial for investors to make decisions, while machine learning algorithms can utilize a large amount of data for training and learning, which has good effect on predicting financial market fluctuations. The article first analyzes the financial dataset, and then constructs a feature selection model by combining Boruta and SHAP to screen the financial data features. Based on the LSTM model, a new Dropout layer and fully connected layer are designed to construct the AMP-LSTM model to realize the prediction of financial market fluctuations. The Boruta SHAP algorithm has a RMSPE of 0.242, which is good for screening. The prediction performance of the AMP-LSTM model is significantly better than that of the traditional LSTM (p<0.01), and the predicted values are closer to the actual values. The method in this paper performs better than MLP, RNN and other methods in general in terms of error performance when predicting indicators such as WTI, Brent, LGO, etc., and is able to realize the prediction of financial market volatility in the digital economy environment.

Risk Dynamic Evolution Model of Pension Financial System based on Partial Differential Equation from the Perspective of Complex Network

With the increasing trend of global population aging, traditional risk assessment methods often overlook the nonlinear interactions and dynamic feedback mechanisms between various elements in the system, making it difficult to capture the complex process of risk evolution. This study identified and characterized the complex relationships between participants in the pension financial system through complex network analysis, including fund flows, policy impacts, market fluctuations, and other dimensions. Then, partial differential equations (PDEs) were introduced as the core modeling tool, and a pension financial system based on partial differential equations (PFS-PDE) risk dynamic evolution model was established by combining PDEs. The PFS-PDE model achieved a relatively high intersection over union (IOU) value after fewer training iterations (about 30) and remained relatively stable thereafter, indicating that the model has a fast convergence speed. The PFS-PDE model has relatively low mean square error (MSE) values and a relatively small fluctuation range under most training iterations, which can be considered as a relatively better performance. Compared with other models, the PFS-PDE model has a smaller range of MSE value fluctuations, indicating that the model has high stability during the training process.

Retail Sales Prediction

Accurate retail sales forecasting is critical for optimizing inventory management, reducing operational costs, and improving strategic decision-making. Traditional statistical models often fail to capture complex consumer behaviors and market fluctuations, leading to inaccurate predictions. With advancements in machine learning (ML), predictive models now offer enhanced accuracy and adaptability in forecasting sales trends. This study evaluates the effectiveness of machine learning algorithms, specifically k-Nearest Neighbor (kNN) regression, Multinomial regression, and Decision Tree with AdaBoost regression, in predicting retail sales. The research assesses model performance using standard evaluation metrics, highlighting the advantages and limitations of each approach. The findings provide valuable insights for retailers, business strategists, and policymakers, offering a data-driven framework for improving sales forecasting and decision-making processes. Keywords: Retail Sales, Forecasting, Machine Learning, Regression Models, Predictive Analytics, Business Intelligence

IMPLEMENTATION OF DATA SCIENCE IN PLANT ENGINEERING

ABSTRACT Effective risk and credit management are the keys to the financial health of plant engineering firms. Companies can counter market uncertainties and fluctuations in demand and complex supply chains through tailored credit risk analysis. Such analysis will help firms detect early financial vulnerabilities so that they can identify the appropriate time to take preventive measures against loss, defaults, or insolvency. More critically, sophisticated fraud detection and risk predictions are playing an important role in financial stability in plant engineering. Advanced and sophisticated business intelligence techniques can identify patterns of transactions that may represent potential fraud. With this feature, firms are in a better position to respond promptly to financial and operational risks while enhancing stability and resilience. Credit monitoring systems further give another level of security as the credit health can be put into proper perspective through integrating real-time analytics with historic financial data. Data Mining(DM) in such systems maximizes the precision of predicting risks by classifying risk levels and creating tailored credit solutions for plant engineering clients. This integration of these systems with enterprise platforms helps the firms make decisions seamlessly across departments, ultimately supporting overall efforts in risk management. Use of data science in plant engineering has improved the possibility of forecasting risks, detection of frauds, and monitoring of credit toward financial security and operational resilience. As the technology gets more integrative to the industry, it supports growth sustainability for the plant engineering companies handling finance complexities. KEYWORDS: Benchmarks, Data Science, Finance, Plant Engineering, Risk Management

The Influence of Competitive Strategies on the Performance of Airlines in Tanzania

Abstract Competitive strategies influence strategic positioning, giving companies a competitive edge. This study analysed the impact of these strategies on airline performance in Tanzania, focusing on Air Tanzania and Precision Air. Based on the Theory of Competitive Advantage, it was found that differentiation and market focus strategies positively impact performance. Differentiation enables airline managers to make informed decisions and enhance their strategic initiatives, leading to measurable improvement. The market focus strategy allows airlines to specialize in specific segments, enabling them to navigate market fluctuations and swiftly respond to changing consumer preferences. However, the study also identified negative effects from cost leadership and product innovation strategies. Cost leadership was linked to financial losses, operational challenges, and high employee turnover, while product innovation sometimes failed to align with market needs, resulting in poor customer reception. To achieve competitiveness and sustained growth, Tanzanian airlines must balance these strategies, leveraging their strengths while mitigating potential risks in a highly competitive and dynamic market.

In Too Deep: The Effect of Sunk Costs on Corporate Investment

ABSTRACTSunk costs are unrecoverable costs that should not affect decision making. I provide evidence that firms systematically fail to ignore sunk costs and that this leads to significant investment distortions. In fixed‐exchange‐ratio stock mergers, aggregate market fluctuations after parties enter into a binding merger agreement induce plausibly exogenous variation in the final acquisition cost. These quasi‐random cost shocks strongly predict firms' commitment to an acquired business following deal completion, with an interquartile cost increase reducing subsequent divestiture rates by 8% to 9%. Consistent with an intrapersonal sunk cost channel, distortions are concentrated in firm‐years in which the acquiring CEO is still in office.

Matrix H-theory approach to stock market fluctuations

Matrix H-theory approach to stock market fluctuations

Impact of Coastal Hazards on Agricultural Lands in Casiguran, Aurora, Philippines

Aims: This study aims to assess the impact of climate-related coastal hazards on agricultural lands in Casiguran, Aurora. Specifically, it evaluates the direct and indirect effects of sea level rise, storm surges, and typhoons on coastal farming communities and provides insights for adaptive solutions. Study Design: The study employed a quantitative research approach using a survey-based assessment to gather data from farmers on the effects of coastal hazards on agriculture. The Garrett Ranking Method was used to analyze and prioritize the ranked impacts. Place and Duration of the Study: The study was conducted in 12 coastal barangays of Casiguran, Aurora. The data collection period spanned December 2, 2024, to January 12, 2025. Methodology: A total of 145 farmers were surveyed using stratified random sampling to ensure representation across different barangays. Farmers ranked the perceived direct and indirect impacts of coastal hazards on their agricultural practices. The collected responses were analyzed using the Garrett Ranking Method to determine the most critical challenges faced by coastal farmers. Results: Among the direct impacts, extreme climatic events had the highest mean score (66.14), followed by changes in soil pH (61.90), soil fertility decline (59.00), sedimentation (47.30), soil erosion (43.66), saltwater intrusion (39.19), and land use changes (33.05). For indirect impacts, variability in crop production was the most significant (64.94), followed by pests and diseases (61.07), rising farming costs (58.98), social impacts (56.51), water competition (40.85), market fluctuations (37.30), and farming practice changes (30.88). Conclusion: The study highlights the need for adaptive strategies to mitigate the impact of coastal hazards on agriculture. Addressing soil degradation requires sustainable management practices like organic amendments and crop rotation, while extreme climatic events necessitate climate-resilient infrastructure such as flood controls and improved irrigation and drainage channels. To tackle economic challenges, financial aid, crop insurance, and farmer training in adaptive techniques like saline-tolerant crops and agroforestry are essential. Integrating these solutions into local policies will enhance resilience and sustainability. Future research should utilize spatial analysis to map vulnerability hotspots and predict agricultural risks due to sea level rise and extreme weather events, providing a data-driven basis for more targeted adaptation strategies.

Artificial Intelligence in E-commerce and Digital Marketing: A Systematic Review of Opportunities, Challenges, and Ethical Implications

The transformative power of AI has only just begun to redefine how businesses function and relate to their customers within e-commerce and digital marketing. In fact, AI really does help firms adjust to changes in consumer preference and market fluctuations by improving operational efficiencies. Big data analytics, aided by artificial intelligence, really boosts the understanding of the customer journey-hence, optimizing and finally allowing for tailor-made marketing campaigns in real time. This leads to great growth for the business. The COVID-19 pandemic pushed companies into adopting AI-driven solutions in the quest for their resilience; this consequently led to an increase in the need for effective digital marketing strategies. E-commerce activities are integrated with artificial intelligence in order to better understand consumer behavior, support market dynamics forecasting, and enhance risk management strategies. Hence, it becomes an indispensable aspect. It is relevant that ethical frameworks and further research address the problems of data privacy and scalability in order to optimize the intrinsic potential of AI. A focus on innovative applications of AI, alongside interdisciplinary collaboration, can empower an organization to develop genuinely inclusive and effective marketing strategies. Embracing the AI-driven initiatives, this will result in long-term relationship building with the customer for growth in a sustainable manner and maintaining competitiveness at an exponential pace in changing digitization.

Causes and Remedies of Organizational Crisis: A Case Study on Bangladeshi Manufacturing Industries

Blue chip corporations have been increasingly impacted by organizational crisis in recent years, Organizational crises in Bangladeshi manufacturing industries stem from a complex interplay of factors. Poor management practices, inadequate infrastructure, and lack of skilled labor often contribute to operational inefficiencies and quality control issues. Financial mismanagement, including excessive debt and improper resource allocation, can lead to liquidity problems and potential bankruptcy. External factors such as political instability, regulatory changes, and global market fluctuations also play a significant role in precipitating crises. To address these challenges, companies can implement several remedies. Investing in employee training and development can improve workforce skills and productivity. Adopting modern technologies and lean manufacturing processes can enhance efficiency and product quality. Implementing robust financial management systems and diversifying product lines can mitigate economic risks. Additionally, fostering better communication between management and workers, as well as engaging in corporate social responsibility initiatives, can improve stakeholder relationships and build resilience against future crises.

Identifying States of Global Oil and Gas Markets Based on Visibility Graph Embedding Algorithms

Oil and natural gas are indispensable energy commodities. Investigating the evolution and transformation of energy market dynamics represented by oil and natural gas holds significant implications for global energy governance, as well as political and economic development. We have employed the visibility graph algorithm to transform the most intuitive and readily available time series data of oil and natural gas prices into complex networks. Then, three network embedding algorithms based on machine learning are utilized to embed the network into multidimensional vector spaces, enabling us to identify market state changes in both energy products through the sequential clustering method. We find that the visibility graph algorithm and network embedding methods effectively preserve internal structural characteristics of data, capture similarities between price change rate nodes across different trading days in the oil and gas markets, while also identifying the memorability of price volatility trends. Beyond simple price trends, critical market fluctuations such as those induced by events like the COVID-19 or Russia–Ukraine conflict can be discerned from similarity matrix partitions of network node representation vectors. Furthermore, the sequential clustering algorithm accurately identifies transition points in the oil and gas markets’ states. Unlike conventional time series analysis methods, this innovative combination of visibility graph and network embedding algorithms allows for a multi-faceted exploration of market state changes at various levels; thereby facilitating deeper insights into underlying logic behind price time series.

Dynamic Modeling of Limit Order Book and Market Maker Strategy Optimization Based on Markov Queue Theory

In recent years, high-frequency trading has become increasingly popular in financial markets, making the dynamic modeling of the limit book and the optimization of market maker strategies become key topics. However, existing studies often lacked detailed descriptions of order books and failed to fully characterize the optimal decisions of market makers in complex market environments, especially in China’s A-share market. Based on Markov queue theory, this paper proposes the dynamic model of the limit order and the optimal strategy of the market maker. The model uses a state transition probability matrix to refine the market diffusion state, order generation, and trading process and incorporates indicators such as optimal quote deviation and restricted order trading probability. Then, the optimal control model is constructed and the reference strategy is derived using the Hamilton–Jacobi–Bellman (HJB) equation. Then, the key parameters are estimated using the high-frequency data of Ping An Bank for a single trading day. In the empirical aspect, the six-month high-frequency trading data of 114 representative stocks in different market states such as the bull market and bear market in China’s A-share market were selected for strategy verification. The results showed that the proposed strategy had robust returns and stable profits in the bull market and that frequent capture of market fluctuations in the bear market can earn relatively high returns while maintaining 50% of the order coverage rate and 66% of the stable order winning rate. Our study used Markov queuing theory to describe the state and price dynamics of the limit order book in detail and used optimization methods to construct and solve the optimal market maker strategy. The empirical aspect broadens the empirical scope of market maker strategies in the Chinese market and studies the stability and effectiveness of market makers in different market states.

Challenges and constraints to the sustainability of poultry farming in Thailand.

The poultry farming industry in Thailand plays a crucial role in the nation's economy and food security. However, its long-term sustainability is challenged by disease outbreaks, environmental concerns, rising feed costs, market fluctuations, and shifting consumer preferences. This review examines the current state of poultry farming in Thailand, focusing on broilers, layers, and ducks. Disease control requires strict biosecurity measures and government interventions. Additionally, the industry must address environmental concerns by reducing greenhouse gas emissions and optimizing resource efficiency to align with Thailand's net-zero targets. The rising demand for organic and ethically produced poultry products presents both market opportunities and production challenges, necessitating significant adjustments in farming practices. Regulatory compliance, particularly in export markets, adds another layer of complexity, requiring Thai poultry producers to meet increasingly stringent international standards. The adoption of new technologies, such as precision farming and AI-driven systems, presents both opportunities and challenges, especially for smaller producers who may struggle to keep pace with advancements. Rising feed costs, driven by global supply chain disruptions and dependence on imported raw materials, pose a significant economic burden, emphasizing the need for alternative protein sources such as insect-based feed and agro-industrial by-products. Furthermore, regulatory compliance with international standards and consumer-driven trends toward antibiotic-free and organic poultry products further shape the industry's transformation. To ensure sustainable development, Thailand's poultry sector must integrate innovative technologies, adopt environmentally friendly practices, and strengthen collaboration between the government, industry stakeholders, and researchers. The industry can maintain its global competitiveness by addressing these challenges while contributing to sustainable food production and environmental conservation.

Research on the Influencing Factors of Beijing Housing Price

This paper aims to develop a house price prediction system using a multiple linear regression model to improve the accuracy of predicting housing prices by analyzing the linear relationships among various influencing factors. Fluctuations in the real estate market have significant impacts on urban development and residents' quality of life, making precise house price forecasting essential for both real estate research and informed decision-making. Traditional prediction methods are often rooted in economic theories, but advancements in data science have introduced more data-driven approaches that can leverage vast datasets to enhance predictive power. Multiple linear regression, a classic statistical technique, is particularly suitable for examining the linear connections between multiple independent variables and a dependent variable, in this case, housing prices. This study incorporates factors such as location, infrastructure, economic indicators, and property characteristics to construct a predictive model, subsequently validating its effectiveness through real-world data analysis and the effect of Variance Inflation Factor (VIF) on the model is discussed.

Optimization of the Number and Type Composition of Houses in Housing Development Using the Simplex Method (Case Study: Pesona Arjuna Residence, Malang Regency)

The rising population in Indonesia has led to an increasing demand for housing, presenting opportunities and challenges for property developers. Although luxury homes yield higher profits, most consumers favor affordable housing. This study aims to optimize the quantity and type composition of homes in Pesona Arjuna Residence, Malang Regency, using the Simplex Method to maximize profits. Developers need to balance market demand and government regulations, which require including affordable and middle-income homes alongside luxury units. The Simplex Method is applied in this study to determine the ideal composition of housing units by factoring in key constraints such as available land, production costs, and market demand. The findings reveal that houses in buildings 12 type A, 18 type B, and 23 type C yield the highest profitability. The Net Present Value (NPV) of Rp 610,940,579.76 at a 10% discount rate confirms the project’s financial success, while the Internal Rate of Return (IRR) of 13.49% indicates a solid annual return on investment. A Benefit-Cost Ratio (BCR) of 1.617 further supports the project’s financial viability, though careful consideration of market fluctuations and risks is advised. This research contributes to sustainable urban planning by offering a flexible approach to housing development, enabling developers to better adapt to market trends and optimize profits while addressing community housing needs.

The Influence of Direct Market Access on Profit Margins, Supply Chain Efficiency, and Economic Resilience for Small-Scale Dairy Farmers of Asian Country

This study investigates the impact of Direct Market Access (DMA) on the economic results of Small-Scale Dairy Farmers (SSDF) in Gujarat, India. Specifically, it explores how DMA impacts Profit Margins (PM), Supply Chain Efficiency (SSE), and Economic Resilience (ER), compared to Traditional Market Access (TMA), which intermediaries dominate. A total of 248 SSDF participated in the study, with data collected through structured surveys and financial records. Descriptive and inferential statistical analyses, including t-tests, ANOVA, and Multiple Regression (MR), were employed to assess the relationships between Market Access Type (MAT) and Key Economic Indicators. The results show that farmers using DMA reported significantly higher PM (Mean=₹. 29,123) than those using TMA (Mean=₹. 26,347). The DMA is better SSE by reducing transportation costs, time to market, and product wastage, with a significant difference in efficiency scores (t=4.02, p=0.001). The Farm Size (FS), Education Level (EL), and Years of Experience (YoE) considerably affect agricultural results. Large farms mean there are more scale efficiencies because a farm is a significant operation with many resources to utilize. Education increases farmers' understanding, enhances new technologies, and encourages them to adopt performance management. YoE results in more effective decisions based on practical knowledge, risk management, and an adaptive approach. All these factors are consistent; for example, educated farmers with suitable experience and large farms have the best chance of adopting new methods and maximizing returns and productivity. These improvements create tenacity, sustainability, and effectiveness in farming businesses. DMA enhanced ER, enabling farmers to withstand market fluctuations better and maintain stable incomes. Key factors such as FS, EL, and YoE further influenced these outcomes, with larger and more educated farmers benefiting more from DMA. The study concludes that DMA is a viable strategy for improving the economic sustainability of SSDF. However, addressing gender disparities and providing education and capacity-building initiatives are essential for ensuring that all farmers can fully benefit from DMA. These findings offer essential identifications for policymakers, farmer cooperatives, and development organizations focused on enhancing the incomes of SSDF.

Financial modeling for strategic pricing in product management: tools for revenue optimization

Strategic pricing plays a pivotal role in product management, serving as a cornerstone for revenue optimization and long-term business success. This paper explores the integration of financial modeling techniques in developing and refining pricing strategies to maximize revenue while balancing customer value and market competitiveness. Financial models enable organizations to simulate various pricing scenarios, assess their impacts on demand elasticity, and predict revenue outcomes, empowering decision-makers with data-driven insights. Tools such as cost-plus pricing models, dynamic pricing algorithms, and value-based frameworks are discussed, highlighting their applicability in different market contexts. Emphasis is placed on leveraging advanced analytics, including machine learning and big data, to enhance pricing precision and adapt to real-time market fluctuations. The study also addresses common challenges, such as understanding consumer behavior, aligning pricing strategies with business goals, and mitigating risks associated with price wars or economic shifts. By illustrating best practices and case studies, this work demonstrates how financial modeling not only supports optimal pricing decisions but also fosters sustainable competitive advantages in an increasingly complex marketplace. The findings provide actionable insights for product managers, financial analysts, and business strategists seeking to harness pricing as a strategic tool for growth and profitability. Keywords: Financial Modeling, Strategic Pricing, Product Management, Revenue Optimization, Pricing Tools.

Determinants of banking efficiency in the MENA region: A two-stage DEA-Tobit approach

In today’s volatile financial environment, banks encounter various risks, including political instability, regulatory changes, and global market fluctuations, which can undermine efficiency and threaten systemic stability. This study focuses on banking efficiency in the MENA region, highlighting its crucial role in economic growth and financial stability. This paper addresses the gap in banking efficiency research in the MENA region by evaluating the technical and pure technical efficiency of 59 conventional banks from 11 MENA countries between 2019 and 2023 and identifying the internal and external factors affecting their efficiency. Using a Data Envelopment Analysis, the study evaluates efficiency based on three inputs and two outputs. A panel Tobit regression model is then applied to analyze the impact of eight internal factors and four external factors on efficiency. The findings indicate that just 16% of the MENA banks were technically efficient, with Qatari banks outperforming and banks in Morocco and Jordan underperforming. The Tobit regression model results indicate that both return on assets and capital adequacy positively influence technical efficiency (TE) and pure technical efficiency (PTE). In contrast, Liquidity and operational costs negatively affect PTE and TE. Non-performing loans negatively impact TE but not PTE, and macroeconomic factors positively influence both TE and PTE. In conclusion, banks in the MENA region must prioritize improving their efficiency to stay competitive. The findings offer valuable insights into operational best practices and provide practical guidance for policymakers, regulators, and banking institutions to enhance the performance of the region’s financial systems.

Stock Returns and Income Inequality in Emerging Economies: Asymmetric Evidence from BRICS and MIKTA

This article explores the nuanced relationship between stock returns and income inequality within the emerging economies of Brazil, Russia, India, China, and South Africa (BRICS) and Mexico, Indonesia, South Korea, Türkiye, and Australia (MIKTA) countries. The study investigates the interactions that potentially influence socioeconomic disparities through market dynamics. Employing a comprehensive panel data analysis, the study utilizes autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) methods to assess the temporal and cross-sectional impacts of stock market fluctuations on income inequality indices, such as the Gini coefficient. The analysis reveals varied responses to stock returns in relation to income inequality across different countries. By integrating both linear and nonlinear approaches in the analysis of stock returns and income inequality, this article contributes to a more nuanced understanding of economic disparities in emerging economies. It underscores the importance of tailored economic policies to manage the impacts of market dynamics on social equity. JEL Classification D63, G10, O16