This study examines the effects of democratic participation and energy transition on rice imports in Indonesia during 2010–2023 on a quarterly basis. Quarterly data were derived from annual data conversion and temporal disaggregation for volatile variables. A quantitative approach employing the Autoregressive Distributed Lag (ARDL) bound test was applied to estimate short- and long-term impacts. ARDL–ECM results indicate a strong short-term correction mechanism, with approximately 64% of deviations from long-term equilibrium adjusted within one quarter. Democratic participation exerts a significant negative effect on short-term rice imports, highlighting the role of political institutions in enhancing food self-sufficiency, while inflation shows a significant positive effect, reflecting the use of imports as a price stabilization tool. In the long term, most variables are not significant except for international rice prices, indicating the vulnerability of Indonesia’s imports to global market fluctuations. CUSUM and CUSUMSQ tests confirm model stability. These findings emphasize that political institutions influence short-term food policy, yet limited institutional capacity maintains long-term dependence on global prices.

In contemporary society, the prices of most goods and services are predetermined through market mechanisms, contractual agreements, or regulatory frameworks, thereby ensuring stability and predictability for consumers and producers alike. However, the agricultural sector confronted with a markedly different scenario. Unlike manufacture products or service-based industries, the pricing of agricultural commodities is subject to considerable uncertainty, shaped by seasonal variations, climatic conditions, market fluctuations, and the bargaining power of intermediaries etc. This inherent volatility often places farmers at a disadvantage, exposing them to income instability and financial vulnerability. To address these problems, the Government of India introduced the Minimum Support Price (MSP) as a policy instrument designed to safeguard farmers against distress sales and to provide a guaranteed floor price for their produce. The MSP not only serves as a protective measure but also plays a crucial role in shaping rural livelihoods, influencing cropping patterns, and contributing to food security. Examining its impact, particularly in regions such as Kalyana Karnataka, where agriculture remains the backbone of the economy, is essential for understanding both the socio-economic benefits and the awareness of this policy framework. The paper makes an attempt to assess the awareness of MSP among the farming community and its socio-economic impact on the farmers income in the selected districts of Kalyana Karnataka Region. The study found less awareness among the farmers of KK region about MSP implications even though the MSP is significantly impacting the farmers income and agricultural investment in the Kalyana Karnataka Region. Keywords: KK Region, Awareness, Socio-Economic Implication, Agricultural Produces

Sharia-compliant investment in Indonesia has experienced rapid growth, in line with increasing public interest in instruments compliant with Islamic principles. However, market fluctuations remain a major challenge in maintaining the performance of sharia investments, particularly sharia mutual funds. This article analyzes the dynamics of sharia investment in Indonesia in the face of market volatility, focusing on the performance of sharia mutual funds. The research method used is a quantitative approach, with secondary data analysis from various scientific studies and recent statistical data. The results indicate that macroeconomic fluctuations and market conditions significantly influence the performance of sharia mutual funds. Nevertheless, sharia mutual funds continue to demonstrate resilience and certain advantages compared to conventional mutual funds, particularly in the face of market uncertainty. These findings have important implications for sharia investors, investment managers, and policymakers in designing more optimal investment strategies and strengthening the position of sharia mutual funds in an increasingly dynamic market.

As agriculture faces increasing sustainability challenges, understanding the financial aspects of farm firms is critical for preparing future agricultural professionals. This research presents a simulation model to analyze multi-year farm profitability under different tenure structures, multiple leverage scenarios, and the presence or absence of government programs. The model considers crop prices, yields, government payments, and market fluctuations to assess the financial viability of the farm. By generating income statements, balance sheets, cash flow reports, and financial ratios, it evaluates farm stability across operations of 400, 800, and 1,200 acres. Preliminary findings suggest that government programs have a significant impact on financial resilience, risk management, and long-term profitability, with effects varying by farm size and market conditions. This study offers a valuable decision-making tool, enabling professionals to strike a balance between profitability and sustainability in an evolving agricultural landscape. KEYWORDS: Farm Financial Analysis; Farm Profitability; Farm Tenure Structures; Government Farm Programs; Agricultural Risk Management; Financial Resilience; Farm Size & Economic Stability; Farm Firm Financial Simulation Model; Agricultural Leverage; West Central Iowa Agriculture

The COVID-19 pandemic, the energy crisis, the outbreak of the Russia-Ukraine war, and the rapid development of renewable energy electricity generation in European countries brought risks to electricity market operations, which are shown as high fluctuations in electricity prices. In order to explore the price risks of electricity markets in European countries, this paper studies the annual average electricity spot price and their fluctuations in each country, and compares them based on the renewable energy percentage of the country and inter-country power exchanges. It was found that under periods with extreme energy supply/consumption situations, a high proportion of hydropower generation may aggravate the price fluctuation of the electricity market. Meanwhile, electricity systems with high wind and solar generations are highly rely on natural gas, and hence be affected by gas/oil markets. Finally, cross-border electricity transactions might trigger electricity price fluctuations under extreme circumstances. Keywords Extreme scenarios, electricity market risk, electricity price fluctuation, high penetration renewable energy, cross-border electricity trading.

In this paper, we propose an innovative statistical approach for decoding volatility in complex financial environments. Volatility is a key element of the European Allowances (EUA) price behavior, shaping both market stability and policy credibility. Our contribution consists of a new metric, the Rank Graduation Volatility (RGV), which plays a threefold role: linking realized and implied volatility through expected volatility, which acts as an intermediate and unobservable bridge between ex-post market fluctuations and forwardlooking expectations; assessing a model's ability to explain implied volatility within a Gini-based framework; and integrating accuracy and interpretability by quantifying the share of implied volatility explained by model-based expectations. We validate our proposal through an empirical application to EUA return, using a rolling-window forecasting framework and a set of Machine and Deep Learning models. The results show that RGV effectively captures differences in explanatory power across models, with Random Forest and Support Vector Machine generally exhibiting the most stable and consistent performance. Overall, the approach provides a transparent and interpretable way to assess the share of implied volatility explained by predictive models, offering new insights into volatility dynamics and risk assessment in the EU Emission Trading System. In addition, our proposal introduces a model-agnostic explainability framework for volatility analysis, grounded in a general statistical structure and designed to be applicable across different market environments.

The plantation sector is vital to Indonesia's economic development, with coffee being a key commodity that contributes 7% to global production. Wonogiri Regency possesses significant potential for coffee cultivation, supported by favorable geographical conditions. Although coffee farming has a long tradition in the region, many farmers temporarily shifted to cloves due to market fluctuations. A subsequent 35% decline in clove prices has now prompted a return to coffee. However, farmers continue to face challenges in managing their farms systematically. This study evaluates the benefits and feasibility of coffee cultivation as a strategy to enhance farmer income and economic resilience. Using a descriptive quantitative method, data was collected from 60 local coffee farmers. The analysis employed cash flow indicators, Gross Profit Margin (GPM), Net Profit Margin (NPM), Benefit-Cost Ratio (B/C), and Revenue-Cost Ratio (R/C). The results indicated a consistent positive cash flow, with revenues surpassing production costs. The farming demonstrated high profitability (GPM: 83%; NPM: 66%) and excellent viability (B/C Ratio: 5.10; R/C Ratio: 6.10), confirming it as a robust and profitable business model. Furthermore, these findings underscore the role of coffee cultivation in advancing the Sustainable Development Goals (SDGs), particularly in reducing poverty (SDG 1).

Exploring financial sentiment analysis via fine-tuning large language model and attributed graph neural network.

An integrated farming system (IFS) is a holistic approach to sustainable agriculture that optimizes resource utilization while ensuring economic viability. The value chain in IFS encompasses several key stages, including input supply, production, processing and marketing, each playing a crucial role in enhancing efficiency and profitability. Effective resource utilization within IFS ensures optimal use of land, water, labour and inputs, minimizing waste and maximizing productivity. The economic benefits of value addition are significant, as they enhance product quality, extend shelf life and create diversified income streams for farmers. Managing the value chain strategically is essential for improving supply chain efficiency, reducing losses and ensuring better price realization for producers. In the livestock component of IFS, a well-structured value chain supports feed management, disease control, quality assurance and market access, leading to higher productivity and profitability. Similarly, in aquaculture and fisheries, value chain integration strengthens sustainable harvesting, efficient processing and reliable distribution, improving both output and economic returns. Agro-tourism integration within IFS provides additional value by promoting farm-based experiences, attracting tourists and generating supplementary income while fostering rural development. Risk management strategies within the value-added chain, such as diversification, insurance mechanisms and technological innovations, play a critical role in mitigating uncertainties related to climate change, market fluctuations and supply chain disruptions. The study takes a forward-looking approach by highlighting how IFS can evolve through stronger value chain integration. Emphasis is placed on enhancing resource-use efficiency, promoting on-farm value addition and aligning production with market demands. Overall, the study outlines how IFS can serve as a future-ready model for sustainable and profitable agriculture.

The current financial market is affected by interest rate adjustments, policy changes, and other factors, resulting in frequent phase transitions. Chinese funded enterprise investment portfolios are facing problems such as income fluctuations and difficulty in risk management. Traditional optimization models are difficult to adapt to dynamic market environments. This article focuses on the phase transition characteristics of financial markets and the investment allocation needs of Chinese enterprises. It proposes an optimization scheme that couples TOPSIS and Markowitz models. Firstly, TOPSIS multi index screening is used to adapt assets, and risk parameters are adjusted based on phase transition prediction. Then, relying on the Markowitz model, weight optimization is completed to form a closed-loop system of "screening adjustment configuration". By sorting out the laws of market transformation, analyzing the limitations of a single model, clarifying the coupling logic and verifying the adaptability in combination with industry historical data, it provides an operational path for Chinese enterprises to achieve controllable portfolio risk and stable returns in volatile markets. Research has shown that coupled models can compensate for the shortcomings of traditional methods in considering multiple objectives and ignoring market fluctuations. They are in line with the investment preferences of Chinese enterprises for low risk and stable returns, and have strong practical application value.

The mean reversion approach in stock prices refers to the tendency of prices to return to their average over a certain period. This concept assumes that price fluctuations deviate temporarily from the mean and eventually revert to it. At this point, investors can take advantage of market fluctuations and explore alternative opportunities by identifying stocks that exhibit mean reversion behaviour. Mean reversion implies that past stock prices can be used to predict future price movements. Conversely, when prices do not revert to the mean, it suggests that shocks have permanent effects; therefore, past prices are insufficient for forecasting the future, and long-term market volatility may increase. In this context, the present study examines the mean reversion behaviour of sustainability-themed stock indices in Borsa Istanbul (BIST). Sustainability indices include companies that consider Environmental, Social, and Corporate Governance (ESG) criteria and offer investors the opportunity to invest in firms that adopt these principles. For this purpose, the mean reversion tendency of the BIST Sustainability Index (XUSRD) and the BIST Sustainability 25 Index (XSD25) was investigated using daily data covering the period from November 22, 2022, to August 15, 2025. To test the mean reversion of prices, the study employs the Augmented Dickey-Fuller (ADF), Kwiatkowski et al. (KPSS) (1992), Christopoulos and León-Ledesma (2010) Fourier ADF, Becker et al. (2006) Fourier KPSS (FKPSS), and Christopoulos and León-Ledesma (2011) Fractional Frequency Fourier ADF (FFFADF) tests. According to the test results, both stock indices contain a unit root at their level values. In other words, the indices do not exhibit mean reversion behaviour. These findings indicate that the future prices of the BIST XUSRD and XSD25 indices cannot be predicted based on past price movements and that price volatility may persist in the long term. Considering these results, it is recommended that investors in BIST sustainability indices focus on fundamental analysis and long-term strategies rather than price-based forecasting methods. Moreover, since prices do not exhibit mean reversion, investors should effectively utilize risk management tools.

The mutual fund industry has emerged as a significant investment avenue for individual investors by offering diversification, professional management, and liquidity. The present study aims to analyse the satisfaction levels of mutual fund investors in the Kurnool District of Andhra Pradesh and to identify the major factors influencing their investment satisfaction. The study examines investor awareness, investment objectives, risk perception, fund performance, transparency, and the quality of services provided by mutual fund companies and intermediaries. Primary data were collected from mutual fund investors in Kurnool district through a structured questionnaire, and the data was analysed using descriptive and appropriate inferential statistical tools. The results reveal that investor satisfaction is largely driven by consistent returns, ease of transactions, timely information disclosure, and effective advisory services, while dissatisfaction arises due to market fluctuations, inadequate product knowledge, and lack of personalised guidance. The study suggests that enhancing financial literacy, improving communication strategies, and strengthening investor support services can significantly improve investor satisfaction and encourage long-term investment in mutual funds.

Purpose This study examines the macroeconomic and financial effects of geopolitical risk in Oman. Oman is an oil-dependent economy facing ongoing regional instability. The study explores dynamic links among geopolitical risk, private investment, household savings, and stock market performance. Methodology Monthly data from 2004 to 2023 are used for the Geopolitical Risk Index, gross fixed capital formation, private sector deposits, and the Muscat Securities Market index (MSX30). A Vector Autoregressive (VAR) framework captures endogenous interactions and feedback. Unit root tests confirm stationarity. The analysis uses impulse response functions, forecast-error variance decompositions, Granger causality, and CUSUM tests to assess shock transmission, causal links, and model stability. Findings Geopolitical risk strongly affects financial variables. Over time, it has a growing impact on private deposits and stock market fluctuations. There is bidirectional causality between savings and equity market performance. This suggests increased precautionary behavior and greater financial sensitivity during periods of uncertainty. Gross fixed capital formation, however, responds little to short-term geopolitical shocks. This implies investment decisions mainly depend on structural and macroeconomic factors. Conclusion The findings show that geopolitical risk in Oman mainly acts through financial and behavioral channels. There is little evidence of an immediate decline in investment. This highlights the importance of financial resilience and savings-based stabilization in resource-dependent economies.

The volatility of electricity market prices poses significant challenges for forecasting models, particularly amid dynamic economic conditions and fluctuating energy demands. This research introduces a novel model for electricity market price prediction that combines advanced machine learning (ML) techniques with economic energy factors to enhance forecasting accuracy and reliability. The proposed model integrates key economic energy indicators such as fuel prices (natural gas, coal, crude oil), inflation rates, currency exchange rates, industrial production indices, and electricity demand-supply ratios. These economic variables are combined with historical electricity price and load data to capture both short-term market fluctuations and broader economic influences. Data preprocessing using Z-score normalization was applied to standardize the input features, ensuring consistent scaling and improved model stability. The forecasting architecture employs a multi-model ensemble strategy, utilizing a Least Squares Support Vector-fused Adaptive Random Forest (LSSV-ARF) model for electricity price prediction. The LSSV component captures nonlinear short-term fluctuations, while the ARF component adapts to evolving economic patterns. Empirical validation is conducted using real-world market data. The model is implemented in the Python platform, and the proposed method demonstrated substantial improvements over traditional models, such as MAE (0.79) and the MAPE (5.43%). These results confirm the effectiveness of integrating economic energy indicators with ML algorithms for electricity price forecasting, providing valuable insights for market participants, grid operators, and policymakers in managing energy pricing strategies.

Farming families in Sukakarsa Village often face challenges due to income uncertainty, limited access to financial information, and low digital literacy. Therefore, the training program focuses on utilizing information technology-based financial applications as tools for budget planning, expense management, income and expenditure recording, and real-time financial monitoring. This training aims to improve the capacity of farming families to plan and manage household finances effectively through the implementation of digital applications. The implementation method includes interactive training modules, direct mentoring through application tutorial sessions, and simulations of using digital budgeting and recording features. The training also provides guidance on savings strategies, business capital management, and short- and long-term goal planning. The results of this activity demonstrated increased financial and digital literacy, motivation for more systematic financial planning, and the ability to utilize digital applications to maintain family financial stability. This training is expected to have a positive impact on the economic resilience of farming families in the face of market fluctuations and income uncertainty in a sustainable manner.

Bananas are a key staple food and cash crop in Tanzania, supporting millions of smallholder farmers with food security and income. However, its production is increasingly constrained by climate change, pests and diseases, soil degradation, and market fluctuations, which threaten both crop output and household welfare. This study examined the vulnerability of banana growers to climate change, and its key drivers. Using the first three waves (2008/09, 2010/11 and 2012.13) of the Tanzania National Panel Survey data (TNPS), a vulnerability index was constructed through principal component analysis (PCA), while drivers of vulnerability were established using a fixed effects model. The results from the PCA show that 89.34% of banana-growing households are highly vulnerable, primarily due to low adaptive capacity. The average vulnerability index for the entire period covered by this study was -56.28, which is considered high; and is mainly attributed to drought and flood shocks, plot slope, and soil erosion. The fixed effects analysis revealed that household education spending and banana sales value significantly reduce vulnerability. In contrast, higher productivity, rising average temperatures, and year fixed effects were found to exacerbate vulnerability. Based on these findings, key policy options include enhancing access to education, agricultural extension services, and good markets; as well as promoting climate-smart agricultural practices. Such measures are vital for building resilience and securing the sustainably of banana-dependent livelihoods.

The paper examines the influence of narrative economics on financial markets and employment outcomes, with a particular focus on job creation and labour sentiment. It examines how prevailing economic narratives shape investor behaviour, affect hiring decisions, and contribute to broader market fluctuations. This research has been conducted using secondary and tertiary sources, including economic journals, financial reports, and previously published studies. The analysis highlights that collective economic narratives, driven by optimism or pessimism, play a critical role in determining both financial performance and labour market dynamics.

The dynamic pricing of electronic products is of great significance in the market economy, particularly for revenue optimization in e-commerce. The traditional pricing methods show obvious limitations in the face of the rapidly changing market environment and fluctuations in consumer behavior. This study constructs a systematic dynamic pricing ablation experimental framework, through the accurate deconstruction of the market complexity and quantifies the contribution of four key market factors (price elasticity, brand competitiveness, category popularity, and market randomness) that affect the effect of merchants pricing strategy. At the same time, At the same time, the research compares the performance of traditional optimization algorithms and reinforcement learning algorithms in different complexity market environments, and uses multiple rounds of repeated experiments, ANOVA significance test and effect quantity analysis to ensure statistical credibility, providing a reproducible experimental paradigm and empirical basis for dynamic pricing related research. The research realizes market factor decoupling and verifies the natural advantages of the reinforcement learning algorithm in the face of a highly complex market environment; at the same time, it explains the key role of price elasticity in commodity pricing and provides merchants with scientific pricing and investment strategies for algorithm-environment matching.

This study investigates the relationship between stock market volatility and key macroeconomic factors in emerging markets. Using panel data from multiple emerging economies over the past two decades, we examine how indicators such as inflation, interest rates, exchange rates, and economic growth influence stock market fluctuations. Our empirical analysis employs advanced econometric models to capture both short-term and long-term dynamics. The findings suggest that macroeconomic instability significantly contributes to increased market volatility, with variations across different regions and market structures. The study provides insights for investors, policymakers, and financial analysts seeking to understand and manage risks in emerging market equities.

Background: This study examines the influence of stock market performance on Nepal’s economic growth from 1994 to 2024. Stock market variables, such as paid-up capital, the number of listed companies, market capitalisation, and turnover, are critical to understanding their role in the country's long-term economic performance. Previous literature suggests a potential relationship between financial market development and economic growth; nevertheless, the level of this relationship in the context of Nepal remains underexplored. Method: The analysis employs the Autoregressive Distributed Lag (ARDL) approach to measure the short- and long-term relationships between key stock market variables and Gross Domestic Product. This method is suitable given the mixed-order of integration in the data, covering the period from 1994 to 2024. Additionally, Granger causality tests are conducted to examine potential causal relationships between stock market performance and economic growth. Results: The study finds a significant long-term relationship between stock market performance, specifically paid-up capital and the number of listed companies, and Nepal's GDP. This highlights the importance of capital accumulation and market development in fostering sustained economic growth. Equally, market capitalisation, turnover, and government expenditure on education show limited or no significant effect on long-term economic growth. In the short run, real market capitalisation is found to negatively impact GDP. At the same time, turnover has no significant effect, suggesting that short-term market fluctuations do not directly contribute to economic growth. Diagnostic tests confirm the robustness and stability of the econometric model. Conclusion: The study stresses the importance of strengthening capital formation, promoting market listings, and encouraging investor participation to support economic growth in Nepal. Policy interventions should focus on improving market efficiency, investor protection, and integrating financial and human capital growth. The findings suggest that these measures can help align Nepal's stock market with its broader economic goals, promoting sustainable and inclusive growth. Novelty: This research offers new insights into the specific stock market variables that most significantly affect economic growth in Nepal, emphasising paid-up capital and the number of listed companies. By employing a robust econometric methodology and examining short-term and long-term relationships, this study contributes to the literature on financial market development. It suggests actionable policy recommendations for Nepal's economic policymakers.