Market Fluctuations Research Articles

Research on Optimal Crop Planting Strategy Based on Improved Quantum Genetic Algorithm

In order to effectively cope with the complex agricultural environment and market uncertainty, this study proposed a crop planting optimization strategy based on the improved quantum genetic algorithm (QGA). Single-objective and multi-objective optimization models were proposed, focusing on profit maximization and risk balance under market fluctuations. The single-objective model used QGA to evaluate the optimal planting scheme under surplus and discount sales scenarios, and analyzed the crop area distribution and total income. For multi-objective optimization, the enhanced MO-QGA model combined with orthogonal experimental design generated a scheme that maximized expected income while minimizing the worst outcome. In addition, this study combined Pearson correlation and hierarchical clustering to quantify the substitutability and complementarity of crops, and optimized the planting strategy through crop combination analysis. The experimental results show that the enhanced QGA effectively balances profits and risks, and the effect is significantly better than traditional methods, which is conducive to improving agricultural decision-making efficiency

Understanding price momentum, market fluctuations, and crashes: insights from the extended Samuelson model

Although momentum strategies result in abnormal profitability, thereby challenging the efficient market hypothesis (EMH), concerns persist regarding their reliability due to their significant volatility and susceptibility to substantial losses. In this study, we investigate the limitations of these strategies and propose a solution. Our literature review reveals that the volatile profits are due to statistical analyses that assume the persistence of past patterns, leading to unreliable results in out-of-sample scenarios when underlying mechanisms evolve. Statistical analysis, the predominant method in financial economics, often proves inadequate in explaining market fluctuations and predicting crashes. To overcome these limitations, a paradigm shift towards dynamic approaches is essential. Drawing inspiration from three groundbreaking economists, we introduce the extended Samuelson model (ESM), a dynamic model that connects price changes to market participant actions. This paradigm transition uncovers several significant findings. First, timely signals indicate momentum initiations, cessations, and reversals, validated using S&P 500 data from 1999 to 2023. Second, ESM predicts the 1987 Black Monday crash weeks in advance, offering a new perspective on its underlying cause. Third, we classify sequential stock price data into eight distinct market states, including their thresholds for transitions, laying the groundwork for market trend predictions and risk assessments. Fourth, the ESM is shown to be a compelling alternative to EMH, offering potent explanatory and predictive power based on a single, realistic assumption. Our findings suggest that ESM has the potential to provide policymakers with proactive tools, enabling financial institutions to enhance their risk assessment and management strategies.

AI-Enhanced Stock Market Prediction: Evaluating Machine Learning Models for Financial Forecasting in the USA

In the USA, one of the world's largest and most liquid financial markets, the ability to anticipate market trends has deep economic implications. Precise stock market forecasting is highly instrumental for investors and analysts in making better asset allocation decisions, managing risks, and setting investment strategies. This research aimed to analyze the efficiency of some machine learning models in stock market forecast evaluation. This research project concentrated on stock market data from the USA, exploring historical price patterns, trading volumes, and relevant economic indicators to assess the performance of various machine learning models. The dataset used for this research work about predicting stock market trends is an exhaustive collection of historical stock prices, some fundamental financial indicators, and relevant news about the market, gathered from various dependable sources. Historical stock prices are retrieved from financial market databases like Yahoo Finance and Google Finance. These sources have daily records of open, high, low, and close prices, and trading volumes for thousands of publicly traded companies for extended periods, normally running into several years. The analyst selected several strategic models, namely, Random Forest, Gradient Boosting, and Logistic regression. Logistic Regression outperformed the other two models with relatively higher accuracy, while the others are just a little below. The findings of this study have implications well beyond academic curiosity into investment strategies and financial analysis. By leveraging the strengths of the best models, investors can create better-informed trading strategies. These models can also include predictive insights that give a serious edge to the risk management strategy in an investment portfolio. By using the predictive power of models like Random Forest, investors can foresee market fluctuations and adjust their portfolios to reduce potential losses.

Russia, China and the USA: perception of effects in the corporate bond market

Purpose: is to identify the dynamics of mutual influences and risks in the corporate bond markets of Russia, China, and the United States under conditions of external environmental instability.Methods: the methods of comparative and econometric analysis of daily data from 2016 to 2024 were used, including the average yields of short- and medium-term corporate and the government bonds of the Russian Federation, China and the United States, selling prices of gold, oil, exchange rates, refinancing rates. GARCH models for forecasting the volatility of the index of corporate bonds of the Russian Federation and China under the influence of internal and external factors, multidimensional quantile regression for industries of the Russian Federation are calculated.Results: six stages of changes in the dynamics of the corporate bond debt market under the influence of the market itself and external factors were highlighted. Periodization made it possible to study the impact of short-term and long-term effects of shocks on the debt market. For the Russian Federation, the volatility weight of the previous period is more than twice the weight of the long-term average volatility. For China, this ratio is even higher. The debt market of the Russian Federation is influenced by the debt markets of China and the United States, but the opposite has not been proven. Three groups of branches of the Russian economy have been formed according to the generality of reaction to external influences.Conclusions and Relevance: during crisis periods, the weight of long-term average market volatility and local factors increases, while that of external factors decreases. Understanding the interactions of internal and external factors allows investors to anticipate market fluctuations, adapt and protect capital. For the Government, this provides an opportunity to develop scientifically sound risk forecasts and use new financial instruments such as yuan bonds.

Financial development and regional bank instability in the United States: An analysis of bank balance sheet structures

This article demonstrates that financial development in the United States has heightened the vulnerability of both sides of regional banks’ balance sheets to financial market fluctuations, drawing on the concept of financial circulation of bank deposits. It first argues that financial development provides diversified short-term investment channels for non-financial sectors to manage their liquid asset holdings. Since banks intermediate money circulation, changes in liquid asset portfolios inevitably result in sudden inflows and outflows of deposits to these banks. These deposit flows are highly clustered during specific financial phases and are subject to financial volatilities rather than local business cycles, making regional banks’ liabilities volatile with market fluctuations. Second, to mitigate increased deposit instability, regional banks hold a higher proportion of marketable securities to manage liquidity. But the mark-to-market nature of security prices further puts potential instability on the asset side of banks’ balance sheets. Extremely, the sudden volatility in the financial markets could simultaneously cause deposit flights and choke off refinancing capability, causing a bank run.

Macroeconomic factors of housing prices in Russia

The main household decisions are related to the consumer and housing market, while housing decisions are related to mortgages. Moreover, both markets are significantly influenced by the macroeconomic environment. The paper aims to study the structural relationships between the macroeconomic environment, consumer and housing markets. How do these aspects relate to each other in Russia? To analyze the associations, the Bayesian vector autoregression methodology with sign restrictions is used. The obtained results show that the housing market strongly depends on fluctuations in the consumer market, whereas the inverse dependence is not so strongly manifested. At the same time, the impact of mortgage shocks and monetary conditions has a greater impact on the volume indicators of the consumer and housing markets than on prices. The dynamics of housing prices is more related to the supply of housing, while consumer prices are related to the ruble exchange rate and supply and demand shocks. The impact e of oil prices on the dynamics of both markets is analyzed. Oil prices have the most significant impact on household consumption, mainly through the demand channel. One way of functioning of this channel are the fluctuations of real disposable incomes in response to fluctuations in oil prices. The constructed model built demonstrates acceptable predictive characteristics, outperfoming the best univariate time series models on certain horizons.

The Relationship Between Investor Sentiment and Chinese Stock Prices

We use data from 2003 to 2023 to investigate the effect of investor sentiment on Chinas stock market. First, we find a positive correlation between investor sentiment and stock market prices: when investor sentiment is higher, market prices tend to rise, while lower investor sentiment tends to decrease market prices. Our research can help enhance investors understanding of the stock market. Individual investors and researchers may more accurately predict short-term and long-term trends by analyzing the relationship between investor sentiment and stock market prices. Furthermore, our paper aims to improve investors risk awareness and promote a rational investment approach. With more investor education, investors can assess market fluctuations more rationally and avoid blindly following trends or panic selling, thereby contributing to market stability. Moreover, by acknowledging the influence of sentiment on market behavior, policymakers and regulators can better anticipate market reactions to various stimuli and devise more effective interventions to mitigate potential market disruptions.

Impacts of the Russia–Ukraine war on the welfare of palm oil producers and consumers in Indonesia

PurposeThis research aims to examine the impacts of the Russia–Ukraine war on the welfare of Indonesian palm oil producers and consumers.Design/methodology/approachThe research uses time-series data from 2000 to 2022 for inferential, simulation and descriptive analyses, coupled with a world vegetable oil trade model in the form of a system of simultaneous equations. The two-stage least squares (2SLS) estimation method with 37 simultaneous equations is used to estimate parameters.FindingsThe research results show that the Russia–Ukraine conflict has led to an increase in the welfare of Indonesian palm oil producers and consumers. A combination of domestic policies (replanting, export tax, mandatory biodiesel and domestic market obligation) has boosted Indonesian palm oil industry players welfare after the Russia–Ukraine conflict. On the other hand, it caused a decline in the welfare of cooking oil and biodiesel producers due to the surge in raw material costs and the decline in selling prices. Therefore, market differentiation for palm oil derivative products into international markets is required.Originality/valueThe results of this research provide empirical evidence to the Indonesian government regarding the importance of policies that regulate the demand and supply of palm oil compared to increasing palm oil production. The results of this research emphasize that the development of policies for the Indonesian palm oil industry needs to pay attention to global vegetable oil market fluctuations as well as the importance of holistic policies from the on-farm to the off-farm level to ensure that producers and consumers experience increased welfare.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2024-0531

STRATEGIES OF VERTICAL INTEGRATION IN THE ENERGY SECTOR OF UKRAINE’S ECONOMY: LEADERS’ EXPERIENCE IN THE CONTEXT OF CONTEMPORARY CHALLENGES

The study provides an insightful analysis of the role of vertical integration in the Ukrainian energy sector, emphasizing its strategic importance for the national economy and energy security. The research highlights several critical challenges facing the industry, including over-dependence on fossil fuels, declining domestic energy resource extraction, underdeveloped technologies, and limited renewable energy use. The ongoing war, infrastructure damage, decreasing investments, and increasing dependency on energy imports exacerbate these issues, further complicating efforts to stabilize the sector. The study supports the hypothesis that vertical integration is an effective strategy for ensuring long-term competitiveness and resilience in Ukraine’s energy companies. It argues that vertical integration, through efficient use of strategic resources, helps companies better withstand external challenges and adapt to fluctuations in the energy market. Including case studies on significant energy players like DTEK and Naftogaz is a practical illustration, demonstrating how vertical integration can provide a competitive edge and enhance adaptability. The methodological approach, combining structural analysis, VRIO analysis, case studies, and systemic analysis, effectively evaluates the strategic potential of vertical integration in the Ukrainian context. This multi-faceted approach offers a comprehensive understanding of how vertical integration can contribute to the stability and adaptability of the energy sector. However, while the study confirms the relevance of vertical integration, it also acknowledges the need for further development to exploit the sector’s innovative potential fully. As Ukraine moves toward Industry 4.0 and 5.0, which promote reduced reliance on hydrocarbons, vertical integration models must be refined to facilitate innovation and adaptability. In conclusion, the study underlines that enhancing vertical integration models could be essential for overcoming the sector’s contemporary challenges. It also suggests that future research should continue exploring strategies that prioritize innovation, renewable energy adoption, and reducing import dependence, ultimately contributing to the long-term stability of Ukraine’s fuel and energy sector (FES).

Adoption of Sustainable Production Process to Bring Forth Value Addition in the Indian Seafood Products

Value addition refers to enhancements made to a product before it reaches customers, altering its nature to command a higher price. Currently, the fishing sector grapples with economic challenges like market fluctuations, Illegal, Unreported, and Unregulated (IUU) fishing, and global access constraints. One issue is that fish processors often prioritize maximizing the value of primary products, neglecting potential gains from by-products. The Indian seafood industry faces sustainability, value addition, and market access challenges, particularly in utilizing by-products effectively due to insufficient investment in enhancing their value. This study examines the fish value chain involving fishermen, cooperatives, wholesalers, retailers, and processors. Qualitative analysis sheds light on industry practices, regulatory dynamics, and socio-cultural influences, enhancing resource management considerations. Integration of qualitative insights with quantitative data promises advancements in fishery science and management. Key findings highlight opportunities in value-added exports, seasonal variability impacts, and the critical role of food safety training. Wholesalers in India enjoy higher profit margins, while retailers could enhance profitability through cost reduction and increased sales. Retailers in India could enhance profitability through cost reduction and increased sales. Sustainable practices and efficient supply chains are imperative for the long-term sustainability of India’s seafood sector.

Application of AR, MA, and ARMA Models in Financial Time Series Analysis

This research analyzes and predicts the daily return on the SP500 and FTSE 100 indices from 2015 to 2023 using the autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) models. The AR model asserts that both indices have the mean reversion feature of returns, which means that past performances do not predict future results. The analysis of the findings in this paper demonstrates this. Nevertheless, it was evident that the FTSE 100's mean reversion was more reliable than that of the SP500, suggesting that the FTSE 100 has the capacity to promptly reverse such a situation. The MA model of the preceding period's return forecast discrepancy was used to adjust the subsequent returns by both indices. Market fluctuations, like those that occurred at the inception of the COVID-19 pandemic, exacerbate these effects. Therefore, as in previous analyses, ARMA derived advantages from the inclusion of errors and information about the prior returns. In contrast to the SP500, which is more sensitive and based on shocks, this index exhibits more accurate mean reversion and superior correction mechanisms than the FTSE 100. Consequently, the research confirms that the ARMA model can effectively forecast the financial markets by leveraging historical results and forecast error. These models may be beneficial to policymakers and investors due to their ability to capture the non-parametric characteristics of financial time series.

An Advanced Time-Varying Capital Asset Pricing Model via Heterogeneous Autoregressive Framework: Evidence from the Chinese Stock Market

The capital asset pricing model (CAPM) is a foundational asset pricing model that is widely applied and holds particular significance in the globally influential Chinese stock market. This study focuses on the banking sector, enhancing the performance of the CAPM and further assessing its applicability within the Chinese stock market context. This study incorporates a heterogeneous autoregressive (HAR) component into the CAPM framework, developing a CAPM-HAR model with time-varying beta coefficients. Empirical analysis based on high-frequency data demonstrates that the CAPM-HAR model not only enhances the capability of capturing market fluctuations but also significantly improves its applicability and predictive accuracy for stocks in the Chinese banking sector.

Machine Learning Solutions for Predicting Stock Trends in BRICS amid Global Economic Shifts and Decoding Market Dynamics

In this paper we examine the role of machine learning (ML) in predicting stock market trends within BRICS economies. Complex, interdependent global and regional economic factors are today and will in the future increasingly influence stock markets, which necessitates innovative techniques for trend analysis. Using this state of the art ML models Support Vector Machines (SVMs), Random Forests, and neural networks the study predicts market fluctuations based on historical stock data, economic indicators and geopolitical events. This research emphasizes the increasing role of deep learning, especially with models such as Transformers and LSTMs, to meet the demand for high accuracy predictive systems in the volatile market. Its analysis brings model performance into comparison of BRICS nations, taking into account the peculiar financial and economic behavior peculiar to each of them. These results illuminate how ML can provide actionable intelligence for investors and policymakers to better manage risk and better make strategic investments. Findings from the study underscore the requirement for adopting sophisticated data driven tools in order to negotiate the intricacies of globalized financial systems. This study also explains the basis in helping us understand how machine learning changes the perspective on stock market analysis. It is a great source to understand how different ML techniques such as Support Vector Machines which are good at classification problems and Random Forests, which are known to handle over fitting, can be used on a financial dataset. It shows cutting edge tools for market prediction such as deep learning models like LSTMs, which are able to handle sequential time series data, or Transformers that further improve the traditional architectures with attention mechanisms. The paper also discusses data preprocessing methods, such as feature engineering and normalization, and the importance of their inclusion in improving model performance. This research shows the value of ML literacy and provides future financial analysts and decision makers with tools for addressing market volatility in a data driven and strategic context.

Sustainable Management Strategy for Shifting Livelihoods from Charcoal Producer Communities to Coffee Farmers in Jatiarjo Village, Prigen District, Pasuruan Regency

Communities at the foot of Mount Arjuno not only have the potential for coffee cultivation but also cattle rearing. Indirectly, the forest fires that occurred were the cause of the reduction in supporting materials to meet their livestock needs. However, this change brings a number of problems that need to be addressed. The SWOT method is used to understand in depth the internal and external factors that influence job transitions. AHP method is used to measure and assess the relative weight of each factor identified through SWOT analysis. The research results show that the management of the socio-economic impact of changing the behavior of charcoal makers to coffee farmers in Jatiarjo Village shows that this village has great potential to utilize supporting natural resources and rich local knowledge in coffee cultivation. The threat of climate change and fluctuations in coffee market prices must be anticipated with appropriate adaptation strategies. The AHP results show that human resource (HR) capacity development has the highest weight in strategic priorities, followed by socio-economic impacts, policy, market access, and the environment.

Stock Price Forecasting Based on the Gauss-Newton Method

The purpose of this study is to explore the stock price forecasting method based on the Gauss-Newton method. In this work, through a literature review, the existing stock forecasting methods were analyzed, and it was noted that there is a lack of attempts to construct nonlinear regression models using the Gauss-Newton method in existing research. The practical operation section provides a detailed description of data reading, fitting of the logarithmic regression model, the construction process of the exponential regression model and the power function regression model, as well as parameter estimation and forecasting using the Gauss-Newton iterative method. By comparing the mean square error and graphical analysis of different models, the optimal model is determined. The significance of this study lies in providing investors with a new tool for understanding market fluctuations, which helps in formulating more scientific risk management strategies.

Meet the neighbor: framing the European Union and African Union perspectives on the Farm to Fork goals

ABSTRACT The debate surrounding the Farm to Fork Strategy and the European Green Deal with the new sustainability standards proposed by the European Commission to achieve a more sustainable food system and climate-neutral continent by 2050 is increasing concerns and questions among different stakeholders along the food chain and third countries that will be impacted by these measures, if adopted. This analysis aims to expand the debate studying the different perspectives on the relationship food security/sustainability of the European Union and the African Union through the framing of the documents produced by these institutions since 2007. The results show differences in the approach about this relationship seen by the European Union as a long-term goal that looks at the protection of the environment and natural resources while the production changes to be more resilient to external shocks and market fluctuations. Meanwhile, the African Union uses a short-term approach due to the urgency felt by the continental unique environment that makes production development and adapting a priority over environmental issues. These differences need to be targeted to avoid current and future divergences that could impact on the application of the aforementioned standards and to ensure food for all without compromising the future of our ecosystems.

A Risk Management Framework for Catfish Farming in Ghana

Purpose: The growth of the catfish farming industry in Ghana has created opportunities for economic advancement and food security; however, significant risks threaten its sustainability. This study aims to propose a comprehensive risk management framework specifically tailored for catfish farming in Ghana, addressing production and financial risks. Methodology: Data was collected from 120 respondents at Catfish Adwoa Atta Farms. The study analyzed production risks such as disease outbreaks and poor water quality, alongside financial risks including market fluctuations and limited access to credit. Findings: The research revealed that production risks account for 40% of total yield losses, with disease-related mortality causing 25% of these losses. Financial risks were also prominent, with 21% of respondents reporting a profitability decline of over 20% due to limited access to financial support. Unique contribution to theory, practice, and policy:The proposed framework integrates modern technologies such as IoT sensors for water quality monitoring, structured contract farming, and government-backed insurance schemes. Pilot testing in regions like the Volta and Ashanti regions is recommended to evaluate its effectiveness. Implementation of the framework could significantly enhance the resilience of catfish farms, reduce production losses, and improve profitability, positioning Ghana’s aquaculture sector as a model for sustainable development.

Exploring environmental and managerial impacts of crude oil volatility on QUAD financial markets: Insights into dynamic connectedness.

The QUAD nations' (United States, India, Japan, and Australia) financial markets are deeply integrated with global markets. The burgeoning surge of risk in global markets requires caution, which can be possible by determining the connectedness. This study elucidates the dynamic connectedness between crude oil and the financial markets of QUAD nations, aiming to enhance understanding of its environmental and managerial implications. This study uses Diebold and Yilmaz (2012) model for the empirical investigation. After investigation the connectedness, portfolio weight, and hedge ratio are determined to know the hedge ratio. It is found that the Japanese market and crude oil emerge as the least transmitter and least receiver of the shock, respectively. Additionally, the result reveals that hedging is most expensive in the US market while cheapest in the Australian market. This paper contributes to understanding the influence of energy market fluctuations on financial markets amidst recent global crises, such as the COVID-19 pandemic and the Russia-Ukraine conflict, with implications for both environmental and economic resilience.

Sustaining Rice Farming: Challenges and Solutions for Agribusiness in Barangay Tagum Norte, Trinidad, Bohol

In the Philippines, rice is a vital crop that serves as the primary food source for 80% of the population in Barangay Tagum Norte, Trinidad, Bohol. Despite the potential of rice agribusiness, farmers in this area need help maintaining consistent production due to limited resources, economic uncertainties, and market fluctuations. This research identifies and addresses these challenges to promote sustainability in rice agribusiness. The study employs a descriptive-exploratory qualitative research design, focusing on a case study approach. Ten rice farmers actively involved in agribusiness in Barangay Tagum Norte were interviewed using structured researcher-made guides. Thematic Analysis was utilized to analyze the gathered information, assigning codes to relevant data segments and organizing them into themes or categories. The findings revealed that financial constraints force farmers to sell their rice at lower prices, and borrowing from lending institutions results in high interest rates, leading to solo farming practices and potential efficiency issues. Manpower challenges, machinery accessibility issues, and significant expenses for seeds, fertilizers, and pesticides also impact productivity. Price fluctuations during harvest create economic challenges, threatening the sustainability of agribusiness. Limited market access and information exacerbate these issues. Farmers need collaborative government-private sector initiatives to address their irrigation, machinery, seed, and fertilizer needs. The study concludes that rice farmers face interconnected challenges throughout the production cycle, including financial constraints, labor shortages, machinery access, seed and fertilizer expenses, government-supplied shortages, and market volatility. The research highlights adaptive strategies such as the “Rollover method,” where farmers strategically time last season’s harvest sale to avoid price declines, showcasing a proactive approach to market fluctuations. Keywords: Agribusiness, Challenges to Sustainability, Qualitative Research, Thematic Analysis

Comparative Analysis of Policy Effect Evaluation of New-energy Vehicle Industry in China and the U.S.

Against fluctuations in the global automotive market, the electric vehicle (EV) industry has demonstrated a positive development trend overall. China has become a leader in battery production and related infrastructure construction, while the United States, though ranking third, also holds an important position. This paper analyzes the effects of government-led policies and market-driven policies on the electric vehicle policies of China and the United States. It explains how the industries and policies of the two countries interact. By analyzing the current state of the industries in both countries and their different policy preferences in terms of fiscal policy, infrastructure allocation, and other dimensions, the following conclusions can be drawn. Firstly, compared to the incoherent central policies and decentralized local policies lacking central authority in the U.S. electric vehicle industry, China's top-down long-term policies lead to a more consistent and efficient development of its electric vehicle industry. This article concludes that China's strong authoritative and consistent policies in the electric vehicle industry play an important role in maintaining its leading position in the global electric vehicle industry.