Price Fluctuations Research Articles

Optimizing the food supply chain through the integration of financial models and big data in procurement: A strategy for reducing food prices

Optimizing the food supply chain through the integration of financial models and big data analytics presents a transformative strategy for reducing food prices. In an increasingly complex global market, managing procurement effectively is crucial for cost control and price stabilization. Financial models, such as cost-benefit analysis, financial forecasting, and risk assessment, provide valuable insights into procurement decisions, helping organizations anticipate price fluctuations and manage expenses. When combined with big data analytics, these models enable more precise predictions and strategic planning. Big data analytics harnesses vast amounts of information from various sources, including market trends, supplier performance, and consumer behavior. By analyzing this data, organizations can identify patterns, optimize inventory levels, and forecast demand with greater accuracy. This data-driven approach enhances decision-making in procurement, allowing for better negotiation with suppliers, efficient inventory management, and the reduction of wastage. The integration of financial models with big data provides a holistic view of the supply chain, enabling companies to implement dynamic pricing strategies and adjust procurement practices based on real-time insights. This integration helps in minimizing operational costs, improving supply chain efficiency, and ultimately reducing food prices for consumers. For example, predictive analytics can identify potential supply disruptions and adjust procurement strategies proactively, while financial models can evaluate the cost-effectiveness of various sourcing options. Challenges such as data integration, quality, and privacy must be addressed to fully leverage these technologies. Moreover, investment in advanced analytics tools and skilled personnel is essential for successful implementation. In conclusion, the synergy between financial models and big data analytics offers a strategic approach to optimizing the food supply chain. By enhancing procurement practices and leveraging real-time insights, this strategy contributes to reducing food prices and improving overall supply chain efficiency, benefiting both businesses and consumers. Keywords: Food Supply Chain, Financial Models, Big Data Analytics, Procurement Optimization, Cost Control, Price Reduction, Inventory Management, Predictive Analytics, Supply Chain Efficiency.

Interest rate dynamics and commodity prices

In economic studies and popular media, interest rates are routinely cited as a major factor behind commodity price fluctuations. At the same time, the transmission channels are far from transparent, leading to long-running debates on the sign and magnitude of interest rate effects. Purely empirical studies struggle to address these issues because of the complex interactions between interest rates, prices, supply changes, and aggregate demand. To move this debate to a solid footing, we extend the competitive storage model to include stochastically evolving interest rates. We establish general conditions for existence and uniqueness of solutions and provide a systematic theoretical and quantitative analysis of the interactions between interest rates and prices.

New Methods of Project Management Based on Web Environment

One of the main challenges of management in today's era, is to adjust organizations to the speed of changes in the business world. Rapid changes in technologies and working tools, continuous changes in market conditions, unpredictable fluctuations in prices, continuous changes in customers' tastes, etc. are factors that make decision-making and management difficult for organization managers. In such a situation, the delay in the implementation and delay in the exploitation of any of the organization's business goals can lead to the loss or serious reduction of the organization's expected benefits from this business goal. One of the issues in project-oriented organizations is that the planning of each project may include thousands or tens of thousands of activities, each activity contains different information in terms of physical, time and financial progress; Therefore, one of the new methods of project management is the use of intranet, extranet and internet environments in project management. This research examines new methods of project management based on web environments. The results of the research show that in the project management process, using the web environment, project and non-project processes of organizations and companies can be organized. It also controlled the management of teams and teamwork well.

Energy firms in China towards resilience: A dynamic quantile connectedness approach

Α comprehensive understanding of the energy and technology sectors is imperative for the implementation of effective climate and industrial policies. This study investigates the quantile interconnectedness between the top five energy equities and the top five technology equities, listed on the Shanghai Stock Exchange 50 Index (SSE50), based on market capitalization. Employing the Quantile VAR methodology, we uncover significant interconnectedness across various quantiles of the return distribution for both energy and technology equities. This implies that price fluctuations within one sector can have a significant effect on the other. Furthermore, our analysis reveals directional connectedness, where energy equities consistently influence technology equity prices throughout the entire period of investigation. Additionally, a heightened degree of interconnectedness is observed during periods of extreme market movements, i.e. in the upper and lower tails of the return distribution. This suggests a more integrated system during stressed conditions, where all equities under investigation exhibit a magnified influence on each other, potentially leading to amplified volatility spillovers. These findings provide valuable insights for investors, portfolio managers, and policymakers, enabling them to develop more informed investment strategies and construct robust risk management frameworks.

Dynamic evolution of causal relationships among cryptocurrencies: an analysis via Bayesian networks

AbstractUnderstanding the relationships between cryptocurrencies is important for making informed investment decisions in this financial market. Our study utilises Bayesian networks to examine the causal interrelationships among six major cryptocurrencies: Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether. Beyond understanding the connectedness, we also investigate whether these relationships evolve over time. This understanding is crucial for developing profitable investment strategies and forecasting methods. Therefore, we introduce an approach to investigate the dynamic nature of these relationships. Our observations reveal that Tether, a stablecoin, behaves distinctly compared to mining-based cryptocurrencies and stands isolated from the others. Furthermore, our findings indicate that Bitcoin and Ethereum significantly influence the price fluctuations of the other coins, except for Tether. This highlights their key roles in the cryptocurrency ecosystem. Additionally, we conduct diagnostic analyses on constructed Bayesian networks, emphasising that cryptocurrencies generally follow the same market direction as extra evidence for interconnectedness. Moreover, our approach reveals the dynamic and evolving nature of these relationships over time, offering insights into the ever-changing dynamics of the cryptocurrency market.

Hybrid local energy markets: Incorporating utility function-based peer risk attributes, renewable energy integration, and grid constraints analysis

This study proposes a novel hybrid Local Energy Market (LEM) trading framework meticulously designed for efficient energy trading among distribution network participants. The proposed LEM hybrid modeling framework aims to maximize social welfare by considering the behavior of market participants and incorporating network constraints into the transactional framework. To address the consumption behavior of market participants, the concept of the utility function with peer risk attributes is employed, differentiating between flexible and inflexible demands. Additionally, it adeptly manages various generation types, including diesel generators, wind and solar generators, and storage, each characterized by a unique cost function. The investigation extends to the analysis of the Japan Electric Power Exchange market (JEPX), taking into account the residential electricity demand in Tokyo, Japan, by comparing with community-based (CB) and peer-to-peer (P2P) markets and scrutinizing market player behavior across multiple scenarios. These scenarios include comparisons of different market structures, variations in prices, considerations of grid constraints, and diverse weather scenarios. The proposed framework undergoes rigorous evaluation utilizing the IEEE 33-bus and 69-bus standard test systems, with results demonstrating its practicality, superiority of the proposed market compared to the CB and P2P markets, and providing valuable insights into the intricate interplay between market players’ behavior and the dynamics of diverse market structures, price fluctuations, grid constraints, and weather scenarios.

Decoding Bitcoin: leveraging macro- and micro-factors in time series analysis for price prediction.

Predicting Bitcoin prices is crucial because they reflect trends in the overall cryptocurrency market. Owing to the market's short history and high price volatility, previous research has focused on the factors influencing Bitcoin price fluctuations. Although previous studies used sentiment analysis or diversified input features, this study's novelty lies in its utilization of data classified into more than five major categories. Moreover, the use of data spanning more than 2,000 days adds novelty to this study. With this extensive dataset, the authors aimed to predict Bitcoin prices across various timeframes using time series analysis. The authors incorporated a broad spectrum of inputs, including technical indicators, sentiment analysis from social media, news sources, and Google Trends. In addition, this study integrated macroeconomic indicators, on-chain Bitcoin transaction details, and traditional financial asset data. The primary objective was to evaluate extensive machine learning and deep learning frameworks for time series prediction, determine optimal window sizes, and enhance Bitcoin price prediction accuracy by leveraging diverse input features. Consequently, employing the bidirectional long short-term memory (Bi-LSTM) yielded significant results even without excluding the COVID-19 outbreak as a black swan outlier. Specifically, using a window size of 3, Bi-LSTM achieved a root mean squared error of 0.01824, mean absolute error of 0.01213, mean absolute percentage error of 2.97%, and an R-squared value of 0.98791. Additionally, to ascertain the importance of input features, gradient importance was examined to identify which variables specifically influenced prediction results. Ablation test was also conducted to validate the effectiveness and validity of input features. The proposed methodology provides a varied examination of the factors influencing price formation, helping investors make informed decisions regarding Bitcoin-related investments, and enabling policymakers to legislate considering these factors.

Examination of the Peaceful Uses of Nuclear Energy in Turkish Legislation

Population growth, industrialization and technological advances increase the demand for energy. Due to problems such as global warming, environmental pollution and destruction, the trend towards alternative energy sources continues on a global scale. For this reason, the use of nuclear energy, one of the secondary energy sources, has tended to increase in recent years. Reasons such as the geographically uneven geographical distribution of resources that are raw materials for energy among countries and their exhaustibility, fluctuations in energy prices and the economical production of nuclear energy are among the reasons why countries in the world turn to nuclear energy. A number of accidents at nuclear power plants have had a negative impact on the perception of nuclear power plants, but for different reasons, nuclear energy is gaining popularity again. Nuclear power plants are the first thing that comes to mind when it comes to nuclear power, but it is also used effectively in medical treatments and applications of various branches of science as well as energy production. In global energy production; the fact that it aims to reduce carbon emissions by providing more energy production within the scope of sustainability, the continuous and uninterrupted continuation of electricity production using nuclear, the production of electricity at more affordable costs in nuclear power plants compared to other power plants (thermal, renewable, etc.), the fact that greenhouse gas emissions are almost negligible, and the use of nuclear technology in many fields such as physics, medicine, transportation, agriculture as well as energy production have made nuclear energy back on the agenda of countries in recent years. In nuclear energy, nuclear waste and their storage processes continue to be discussed as a problem in this process. For these reasons, the possibility of peaceful use of nuclear power has been questioned in academic platforms and various criteria have been determined This article examines Turkey's compliance with the criteria for the peaceful use of nuclear energy and clarifies the development process in this regard. Turkey's legislation on nuclear energy, international conventions, nuclear safety, radiation control criteria and compliance with national legislation will be examined.

The Role of Oil in Iraqi Politics: From Independence to the Beginning of the Twenty-first Century

This study reviewed how oil affected Iraq's internal and foreign policies throughout contemporary history, as it sought in its goal to identify the role of oil in Iraqi politics from independence until the beginning of the twenty-first century, and to achieve the goal of the study, the descriptive analytical approach was relied on, and the historical method, in order to identify the scene of Iraqi politics and the impact of oil and its role in the political practices of the Iraqi state since independence until the beginning of the twenty-first century, and the importance of the study is due to the importance of oil The first is the importance of oil in Iraq, and it has two requirements, the first is a brief summary of the history of the importance of oil in Iraq The second demand focused on the status of oil in the period following the independence of the Iraqi state. .and the second section was entitled Oil crises and their repercussions on Iraqi policy, and it has two requirements, the first dealt with the crisis The first oil and its repercussions on Iraqi politics, and the second requirement dealt with the second oil crisis and its repercussions on Iraqi politics, then came the third section entitled the role of oil in the political scene of the State of Iraq, and it has two requirements, the first talked about the political situation of the State of Iraq at the beginning of the 21st century and the role of Iraqi oil in the international scene, The second and final requirement of the study focused on Iraq's political and economic landscape in the aftermath of 2003; The study also explored the role of oil in the reconstruction of the Iraqi state. It uncovered several key findings, among which the most significant are: the management of oil in Iraq is synonymous with the management of all industrialized nations. The post-2003 period brought about transformative changes, as Iraq's oil market endured severe crises. Politically, the lack of democracy and the challenges of establishing a more democratic system in Iraq can only be fully understood by considering the economic dimensions, particularly the role of oil. The study highlighted that Iraq faced significant market risks and oil price fluctuations. Additionally, the invasion of Iraq in 2003 and the subsequent period up to 2011 confirmed that historical narratives about Iraq remain deeply influential. Although there are multiple political reasons for the invasion of Iraq, all reconstruction policies and projects after the invasion also relied on the oil sector, and the study justifies that the Iraqi state cannot withdraw from the economic scene, but will be forced to adopt economic policies, However, oil remains a political and strategic commodity over which no country can cede control to non-state actors.

Commodity prices and production networks in small open economies

We study the role of domestic production networks in the transmission of commodity price fluctuations in small open economies. First, we present a tractable model of a small open economy's production network to explain sectoral propagation patterns. We demonstrate that the domestic production network is crucial in shaping the propagation of commodity prices. Using a panel of 31 sectors across 9 small open economies, we empirically confirm the model's predictions. Next, we construct a dynamic model of a small open economy featuring a production network to study the macroeconomic importance of the network structure in shaping both aggregate and sectoral responses to commodity price shocks. We show that: (i) the network-adjusted labor share of the commodity sector, rather than the sector's size, is key to understanding the real wage's response to commodity price fluctuations; and (ii) non-unitary elasticities of substitution in production are crucial for understanding the cross-sectional implications of these fluctuations.

The effect of geopolitical risk on the clean energy metals’ prices: the evidence of BRICS countries

ABSTRACT This study examines the effect of geopolitical risk on clean energy metal prices for Brazil, Russia, India, China and South Africa (BRICS) countries. Aluminum, cobalt, copper, nickel and zinc have been used as clean energy metals. The research period was determined as January 1990 to August 2021. Hatemi-J and Roca (2014) asymmetric causality test was used as the research method. According to the results, there is no causality between geopolitical risk of South Africa and cobalt prices for different cases. However, in general, there is causality between other clean energy metal prices and geopolitical risk in BRICS countries. Accordingly, the geopolitical risks of the BRICS countries both affect and are affected by these metal prices. This result suggests that changes in geopolitical risk of BRICS countries can provide useful information in explaining changes in clean energy metal prices. Likewise, changes in clean energy metal prices provide useful information in explaining the changes in geopolitical risk of BRICS countries. Therefore, policy makers need to develop new policies and strategies that can protect BRICS countries from clean energy metal price fluctuations in order to effectively use their renewable energy resources in their economic development.

Sustainable development of the water-energy-Co2 nexus in the refining sector: A stochastic multi-objective optimization under emissions trading systems

Refineries consume substantial energy and water while emitting significant CO2. Solvent-based Carbon Capture (CC) offers a viable CO2 mitigation solution but increases water and energy consumption and requires considerable investments. This research aims to overcome these barriers and improve the Iranian refining sector towards Sustainable Development Goals (SDGs) 6, 7, and 13 by mitigating CO2 emissions, producing low-carbon hydrogen, and utilizing unconventional water resources for CC plants. A comprehensive nexus approach was incorporated to account for the intricate interconnections between SDGs and refineries' water, energy, and CO2 networks. We evaluate the implementation of CO2 market regulations corresponding to seven international Emissions Trading Systems (ETSs) of the EU, UK, New Zealand (NZ), Regional Greenhouse Gas Initiative (RGGI), South Korea, California, and China in the Iranian refining sector to incentivize CC integration. The problem was formulated using Stochastic Multi-Objective Mixed-Integer Linear Programming optimization, with K-means Clustering applied to account for ETS CO2 price fluctuations and uncertainties. Results showed intense competition between economic and environmental objectives without ETS regulations, but appropriate ETS implementation could transform this conflict into a cooperative compromise. Under EU and UK scenarios, 60.3% and 67% of CO2 emissions could be avoided with fully covered capture costs, potentially leading to net economic profit through surplus allowance sales. Implementation of the other five ETSs could reduce capture costs by 18–67%, with a 16.4% CO2 avoidance.

Energy price and returns: a threshold heteroscedasticity model approach

ABSTRACT The energy market is characterized by its intricate dynamics, influenced by various factors that result in erratic behavior, making it challenging to model energy price returns and volatility. In energy market, the changes in Brent and West Texas Intermediate (WTI) crude oil price yields and volatility are the focus of great interest and attention. Due to the influence of different periods and some economic factors, the fluctuation of crude oil price shows asymmetric characteristics. For exploring the asymmetric characteristics of energy market, we introduce the application of the threshold double autoregressive (TDAR) model to handle the complexities of energy price returns and volatility. The TDAR model is able to capture nonlinearities and regime shifts in the oil market, making it particularly suitable for understanding the inherent complexities in crude oil price movements. The empirical results implied that TDAR model demonstrates superior performance in capturing the intricate dynamics of crude oil prices compared to traditional asymmetric GARCH-type models, making it a valuable tool for market practitioners and policymakers. Our findings reveal the subtle movements in Brent and WTI prices and offer valuable insights for risk management and investment strategies, which provide an alternative for market practitioners and policymakers.

Spatial heterogeneity analysis of biased land resource supply policies on housing prices and innovation efficiency

To rationally allocate land resources, the government has formulated biased land resource supply policies. However, this policy will promote the fluctuation of regional housing prices, thereby affecting regional innovation efficiency. To elucidate the intrinsic logical relationship among these factors, this study investigates the influence of biased land resource supply policies on housing prices and innovation across 31 Chinese provinces over a 16-year period. It places particular emphasis on probing into the spatial disparities in this impact. The innovation of this paper lies in the development of a theoretical analysis model, which creatively utilizes the regional land supply status as an instrumental variable to address the endogeneity problem. At the same time, a double robustness test was conducted by substituting the explanatory variables and employing the fixed effects model. The results of the TSLS regression indicate: (1) The land resource supply policy, which prioritizes the central and western regions, has resulted in an increase in housing prices across the majority of regions in China, with the eastern region experiencing a more rapid rise compared to the central and western regions. (2) The accelerated growth of housing prices in the eastern region is likely to exert a more pronounced inhibitory effect on innovation compared to the central and western regions. In summary, China’s land resource supply policy, which favors the central and western regions, has exacerbated the rise in housing prices in the eastern region, and rising housing prices will inhibit the innovation efficiency of the region. Hence, it is imperative for the government to continuously optimize and adjust land resource supply policies, ensuring their alignment with population migration patterns, stabilizing the real estate market, while also considering the innovation efficiency of regional development.

The Impact of Prospect Theory on Asset Forecasting: A Comparative Study Based on Random Forest and Linear Regression

This study focuses on the application of predictive models and prospect theory in the field of machine learning to predict financial asset prices. Here, the linear regression and random forest algorithms were employed to forecast the price values and fluctuations of two different financial assets: the S&P 500 index and Bitcoin prices. Meanwhile, this study compared the performance of different financial asset datasets before and after the introduction of prospect theory models. Through calculation and comparative analysis, this article provides an in-depth discussion on the predictive performance of regression models and classification models. The research results indicate that in most cases, simple linear regression models have high prediction accuracy. Meanwhile, this paper also found that introducing prospect theory can effectively improve the accuracy of prediction models for specific financial assets. This result has a positive impact on the financial industry, helping to optimize risk management and investment decision-making, improving efficiency at the market level and promoting information transparency, while also promoting the development of emerging financial formats in financial innovation

Quantile connectedness among fintech, carbon future, and energy markets: Implications for hedging and investment strategies

This study proposes a novel framework to explore the quantile connectedness among fintech, carbon future, and energy markets with implications for hedging and investment strategies. We aim to construct portfolio strategies for extreme market conditions based on the results of quantile connectedness to address the complex price fluctuations in the energy markets. Using the daily data from January 4, 2017, to March 6, 2023, we find that (i) carbon future and crude oil are the net receivers, while fintech and clean energy are the net contributors. However, the net spillover direction of fintech, carbon futures, and energy markets change frequently under extreme market conditions; (ii) the COVID-19 pandemic and Russia-Ukraine conflict significantly enhance connectedness across all quantile levels; (iii) the sample period is dominated by the lower quantile connectedness, indicating that investors react more strongly to bad news. Furthermore, the result of portfolios shows that, compared to normal market conditions, the dynamic investment weights of minimum connectedness (MCOP) exhibit greater volatility under extreme market conditions. This highlights the differences in portfolio adjustments across different market conditions. In addition, the result shows that the minimum connectedness and minimum correlation portfolio have a greater cumulative return than the minimum variance portfolio. The analysis based on the Sharpe ratio shows that in extreme market conditions, carbon futures and fintech can serve as effective hedging tools for the energy markets. Robustness tests further confirmed our conclusions. Our results have positive implications for decision-makers and investors about risk management and diversified portfolio selection.

A Research of Fine-Tuning CNN-LSTM Model for Gold Price Prediction

Gold price and foreign exchange price prediction are usually regarded as serialized data with strong time correlation. It is precisely because of this feature that the performance of traditional convolutional neural networks is often not as good as that of recurrent neural networks. This paper fine-tunes the CNN-LSTM model in two ways. The first is to simplify the input data, convert the time series into binary data, and change the model task from predicting prices to predicting price fluctuations. The second is to add a dropout layer and use the dropout layer to improve the model's overfitting problem. The results demonstrate that the improved model outperforms the first model, with prediction accuracy rising from 54% to 57%. Furthermore, the refined model is not limited to processing the gold price series; it can also be applied to other time series, and when paired with other models, it could yield even better outcomes.

Determinants of Food Security in Tobacco and Sugarcane Production Zones, Migori County, Kenya

At 34%, food insecurity in Migori County is considered alarming while its measurement has posed challenges to academicians and researchers whilst many studies exist on food security determinants, none has been done in Migori entailing tobacco producers, sugarcane growers and non-growers in Kuria and Migori sub-counties. The study examined food security determinants guided by a consumer theory and measured by means of HFIAS modelled in ologit model. Data collection was done using an open data kit pre-loaded with a questionnaire and analyzed using stata software wherein results depicted 38.89% as severely insecure, 33.33% moderately insecure, 9.26% mildly insecure and 18.52% food secure while household size, off-farm income, capital sourcing was significant at p<0.001 in varying degrees and categories after ologit regression analysis. There is need for the national government to provide cash crop insurance and fixed contracts to prevent risks associated with price fluctuations as well as invest in innovative biotechnology research through formulation of policies to aid in ensuring capacity building through its stakeholders such as the national research institutes and other donor agencies.

The Contagion of Debt Default Risk in Energy Enterprises Considering Carbon Price Fluctuations

Under the constraints of low-carbon transformation goals, energy enterprises have significantly increased their debt default risk levels due to carbon price fluctuations. This article first analyzes the contagion mechanism of debt default risk among energy enterprises, and based on this, constructs a debt default risk contagion model among energy enterprises considering carbon price fluctuations, and then simulates and analyzes the evolution characteristics of debt default risk contagion among energy enterprises. The research results indicate that: (1) As the proportion of carbon emission cost increment and investor sentiment index increase, the stability of the debt network of energy enterprises strengthens. As the ratio of commercial credit among energy enterprises and influence of energy enterprises increase, the impact of debt risk gradually intensifies. (2) The investor sentiment index has a strengthening effect on the influence of energy enterprises, the proportion of commercial credit among energy enterprises, and the proportion of carbon emission cost increment. The commercial credit ratio between energy enterprises and its influence has a mutually reinforcing effect. (3) The investor sentiment index has suppressed debt default risk for various energy enterprises. The joint risk suppression effect of the proportion of carbon emission cost increment and the influence of energy enterprises in petroleum and petrochemical enterprises is more prominent. The joint risk constraint ability between the proportion of carbon emission cost increment and investor sentiment index in coal enterprises is stronger.

The Impact of Oil Price on Carbon Dioxide Emissions in the Transport Sector: The Threshold Effect of Environmental Policy Stringency

Carbon dioxide emissions from the transport sector make a significant contribution to global greenhouse gases, and understanding the factors that influence these emissions is beneficial for devising effective emission reduction policies. Oil prices are an important influencing factor since the fuel used in the transport sector is primarily based on oil, and fluctuations in oil prices directly impact the sector’s CO2 emissions. Additionally, environmental policies, as a key means of controlling CO2 emissions, can affect the relationship between oil prices and CO2 emissions in the transport sector. Therefore, this study aims to examine the impact of oil prices on CO2 emissions in the transport sector and explore the nonlinear role of environmental policy stringency in this relationship. Based on data from 27 OECD member countries and 6 non-member countries from 1990 to 2019, we used the environmental policy stringency index as a threshold variable to construct a panel threshold regression model. The analysis results indicate a double-threshold effect: when the environmental policy stringency index is low, the impact of oil prices on CO2 emissions in the transport sector is not significant. However, when the index reaches the first threshold, the impact of oil prices significantly increases; upon reaching the second threshold, the effect is further intensified. This paper also analyzes the three subindicators—market-based policies, non-market-based policies, and technology support policies—to clarify the distinct impact mechanisms of different types of environmental policies. Finally, based on the research findings, we propose policy recommendations to achieve carbon dioxide emission reduction targets in the transport sector.