Hasil untuk "Public finance"

خالد سمير محمد حسن

تأتي هذه الدراسة في ظل تصاعد دعوات كثيرة من أجل إصدار قانون جديد لتنظيم الإدارة المحلية في مصر، نظرًا لأن القانون الحالي ( قانون رقم 43 لسنة 1979) لم يعد مواكباً لما جاء بالدستور الجديد ( دستور 2014 ) من صلاحيات جديدة ودعم صريح للامركزية و ترسيخ لديمقراطيتها و تعزيز لصلاحياتها .و تستعرض هذه الدراسة تاريخ الإدارة المحلية في مصر، مع التركيز على انتخابات المجالس المحلية خلال فترة حكم الرئيس الأسبق حسني مبارك ، وهي فترة غنية بالتفاعلات السياسية وشهدت عدة استحقاقات انتخابية محلية وقرارات بشأنها لم تتناولها دراسات سابقة بشكل كافٍ ، وقد استخدمت الدراسة المدخل التاريخي وذلك سعياً لحصر السلبيات والإستفادة منها وكذا الوقوف على مواضع القوة فى تجربة " إدارة المحليات " فى مصر بشكلها الحديث ومفهومها المعاصر عبر قرابة قرن كامل من الزمان ، سعيا للوصول إلى نتائج علمية يمكن أن نستقرأ منها توصيات فاعلة فى حالات مماثلة للدول النامية أو حديثة العهد بالتعددية السياسية والحريات والديمقراطية فى إدارة الأقاليم ..

B.S. Prashanth, Manoj Kumar, Ariful Hoque et al.

The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems by using TabNet, an adaptable deep learning framework, on a Kaggle dataset consisting of actual bank transactions in India. Maximizing operational risk management by improving the accuracy of transaction anomaly detection and ensuring regulatory compliance through transparent models is the goal.We utilize a supervised learning pipeline that incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to ensure that classes are balanced. Subsequently, we conduct thorough exploratory data analysis (EDA) to identify patterns of fraud, both during specific times and across behaviors. On this dataset, five different deep learning architectures are tested: DNN, GRU, LSTM, CNN1D, and TabNet. Assessment of predictive performance was carried out using a 3-fold cross-validation framework. With a ROC-AUC of 0.9739 and an accuracy of 97.39 %, TabNet considerably outperformed the competition. The method of sparse feature selection used improved interpretability, generalized better on tabular data, and produced fewer false positives and negatives.Critical insights for operational fraud detection systems and a contribution to the broader literature on explainable AI (XAI) in financial decision-making are offered by the findings. Goals 8 and 16 of the Sustainable Development Agenda are supported by this study, which promotes inclusive economic growth and institutional transparency. Supporting strong, policy-compliant, and interpretable decision-support systems, it also offers practical use for real-time implementation in banking infrastructure.

Dwi Ratna Puspita Sari, Sirli Fahriah, Kurnianingsih et al.

Gold is one of the most favored investment instruments due to its stability and its ability to preserve value against inflation. However, its price movements are volatile and influenced by various global economic factors, currency exchange rates, and geopolitical conditions, making gold price forecasting a significant challenge. This study aims to develop a gold price forecasting system using the Long Short-Term Memory (LSTM) algorithm, a variant of the Recurrent Neural Network (RNN) that excels in processing time-series data. The dataset consists of historical daily gold buying and selling prices from 2015 to 2025, collected from Yahoo Finance, Logam Mulia, and the official website of Bank Indonesia. The modeling process follows the CRISP-DM methodology, which includes business understanding, data preparation and exploration, modeling, and evaluation stages. Time Series Cross Validation (TSCV) is used to validate the model. LSTM performance is compared with other models such as GRU, CNN-1D, and Simple RNN to identify the best-performing architecture. Evaluation results indicate that LSTM achieved the highest performance with an R² score of 0.99 for selling prices and 0.98 for buying prices on the final test dataset. The system is deployed online, making it accessible in real-time. This research is expected to assist investors, financial analysts, and the general public in making smarter investment decisions based on valid historical data and advanced forecasting technology.

Ugur Korkut Pata, Mustafa Tevfik Kartal, Zahoor Ahmed et al.

Abstract This study analyzes the influence of green bonds on carbon neutrality. It examines the daily data of sectoral CO2 emissions of the top five CO2-emitting nations from January 2, 2019 to December 30, 2022 using wavelet transform coherence, quantile-on-quantile regression, Granger causality in quantiles, and quantile regression approaches. The results revealed that (i) green bonds are strongly related to sectoral CO2 emissions; (ii) green bonds reduce transport sector CO2 emissions in China, the US, and Japan while causing an upsurge in India and Russia; (iii) green bonds reduce industrial sector CO2 emissions only in the US; (iv) green bonds have a declining influence in energy sector CO2 emissions at lower quantiles in India, China, and the US, whereas the impact increases at higher quantiles; and (v) green bonds decrease residential sector CO2 emissions in the US, Russia, and Japan. The study revealed that green bonds help reduce CO2 emissions in the residential sector in various quantiles. Therefore, the US, Russia, and Japan should raise household awareness of green energy utilization by promoting them with green bonds. In addition, green bonds can effectively reduce transportation sector CO2 emissions in China and the US. Therefore, the policymakers of the two global powers should contribute to global CO2 reduction by promoting green transportation and clean energy transition in the transportation sector through green bonds. Thus, green bonds can play an effective role in the fight against global warming.

Yinuo Zou, Yuefeng Lyu, Guan Li et al.

The relationship between ecosystem services (ESs) and human well-being (HWB) is a central issue of sustainable development. However, current research often relies on qualitative frameworks or indicator-based assessments, limiting a comprehensive understanding of the relationship between natural environment and human acquisition, which still needs to be strengthened. As an element transferred in the natural–society coupling system, carbon can assist in characterizing the dynamic interactions within coupled human–natural systems. Carbon, as a fundamental element transferred across ecological and social spheres, offers a powerful lens to characterize these linkages. This study develops and applies a novel analytical framework that integrates carbon flow as a unifying metric to quantitatively assess the spatiotemporal dynamics of the land use and land cover change (LUCC)–ESs–HWB nexus in Guizhou Province, China, from 2000 to 2020. The results show that: (1) Ecosystem services in Guizhou showed distinct trends from 2000 to 2020: supporting and regulating services declined and then recovered, and provisioning services steadily increased, while cultural services remained stable but varied across cities. (2) Human well-being generally improved over time, with health remaining stable and the HSI rising across most cities, although security levels fluctuated and remained low in some areas. (3) The contribution of ecosystem services to human well-being peaked in 2010–2015, followed by declines in central and northern regions, while southern and western areas maintained or improved their levels. (4) Supporting and regulating services were positively correlated with HWB security, while cultural services showed mixed effects, with strong synergies between culture and health in cities like Liupanshui and Qiandongnan. Overall, this study quantified the coupled dynamics between ecosystem services and human well-being through a carbon flow framework, which not only offers a unified metric for cross-dimensional analysis but also reduces subjective bias in evaluation. This integrated approach provides critical insights for crafting spatially explicit land management policies in Guizhou and offers a replicable methodology for exploring sustainable development pathways in other ecologically fragile karst regions worldwide. Compared with conventional ecosystem service frameworks, the carbon flow approach provides a process-based, dynamic mediator that quantifies biogeochemical linkages in LUCC–ESs–HWB systems, which is particularly important in fragile karst regions. However, we acknowledge that further empirical comparison with traditional ESs metrics could strengthen the framework’s generalizability.

Ana-Maria Coatu, Felix-Angel Popescu, Laurențiu Petrila

This study explores how socio-economic factors affect the effectiveness of public accountability frameworks in EU member states, with Romania as a case study. Using data from the World Bank, Eurobarometer, and cross-country comparisons, it identifies five key determinants: income inequality, education, healthcare access, political participation, and economic stability. Grounded in institutional theory, the research shows that inclusive institutions and lower disparities lead to stronger accountability, while weaker frameworks often reinforce inequality and corruption. For Romania, the study recommends boosting transparency, enforcing anti-corruption measures, improving rural-urban equity, and enhancing civic education to strengthen the link between citizens and institutions.

Dangxing Chen

In recent years, machine learning models have achieved great success at the expense of highly complex black-box structures. By using axiomatic attribution methods, we can fairly allocate the contributions of each feature, thus allowing us to interpret the model predictions. In high-risk sectors such as finance, risk is just as important as mean predictions. Throughout this work, we address the following risk attribution problem: how to fairly allocate the risk given a model with data? We demonstrate with analysis and empirical examples that risk can be well allocated by extending the Shapley value framework.

Ayobami Paul Abolade, Ibrahim Olanrewaju Lawal, Kamoru Lanre Akanbi et al.

Access to finance is vital for improving food security, particularly in developing nations where agricultural production is crucial. Despite several financial interventions targeted at increasing agricultural production, smallholder farmers continue to lack access to reasonable, timely, and sufficient financing, limiting their ability to invest in improved technology and inputs, lowering productivity and food supply. This study examines the relationship between access to finance and food security among smallholder farmers in Ogun State, employing institutional theory as a theoretical framework. The study takes a quantitative method, with a survey for the research design and a population of 37,200 agricultural smallholder farmers. A sample size of 380 was chosen using probability sampling and simple random techniques. The data were analysed via Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings demonstrate a favourable relationship between access to finance and food security, with an R2-value of 0.615 indicating a robust link. These findings underline the need of improving financial institutions and implementing enabling policies to enable farmers have access to the financial resources they need to achieve food security outcomes.

Ziyi Yin, Guowei Huang, Rui Zhao et al.

Abstract Crowdfunding has become important in increasing financial support for the development of green technologies. Self-disclosed information significantly affects supporters’ decisions and is important for the success of green project funding. However, current studies still lack investigations into the impact of information disclosure on green crowdfunding performance. This research aims to fill this knowledge gap by exploring eight information disclosure-relevant factors in green crowdfunding performance. Applying machine learning techniques (e.g., Natural Language Processing and Computer Vision) and logistic regression, this study investigates 720 green crowdfunding campaigns on GoFundMe and empirically finds that the duration, length of campaign introductions, and length of the title influence fundraising outcomes. However, no evidence supports the impact of goal size, emotion of campaign introduction, or image content on funding success. This study clarifies the information disclosure-related data that green crowdfunding campaigns should consider and provides founders with a constructive guide to smoothly raise money for a green crowdfunding campaign. This study also contributes to data processing methods by providing future studies with an approach for transferring unstructured data to structured data.

Jingfu Lu, Fatime Gulzar, Yifan Lai

In the context of China's transportation sector, which has faced escalating challenges in carbon emissions, this study delves into the intricate nexus between sustainable finance strategies and the imperative of achieving carbon neutrality. Spanning the years 2010–2022 across 30 provinces of China and employing a rigorous Panel Model methodology, our research sets out to achieve several pivotal objectives. These include assessing the tangible impact of sustainable finance initiatives on curtailing carbon emissions within the transportation domain, discerning the pivotal drivers that influence the trajectory of carbon neutrality endeavors, and critically evaluating the efficacy of policy interventions aimed at fostering sustainability. Our findings unearth a compelling narrative. Firstly, we observe a discernible positive correlation between the implementation of sustainable finance mechanisms—such as green bonds, sustainable investment portfolios, and innovative financial instruments—and the tangible reduction of carbon emissions within the transportation sector. Secondly, our analysis underscores the indispensable role of key drivers, ranging from technological advancements and regulatory frameworks to evolving consumer behavior and public consciousness, in steering the course towards carbon neutrality. Thirdly, our research underscores the pivotal impact of targeted policy interventions, emphasizing the efficacy of measures aimed at incentivizing sustainable practices, fostering stakeholder collaborations, and bolstering industry-wide accountability frameworks. In light of these insights, our study advocates for a nuanced policy landscape characterized by a multifaceted approach. By aligning financial incentives with sustainability goals, fostering technological innovation, and fostering robust regulatory frameworks, policymakers can catalyze a paradigm shift towards carbon neutrality in the transportation sector.

Guoli Rao, Chengru Ju, Zhen Feng

This research examines the critical dependencies within US-China technology supply chains through advanced artificial intelligence methodologies, addressing significant economic security implications in an era of strategic competition. The study develops and applies novel machine learning algorithms, network analysis techniques, and predictive models to identify, quantify, and visualize complex dependencies across semiconductor, telecommunications, and emerging technology sectors. Findings reveal pronounced asymmetric vulnerabilities, with semiconductor manufacturing equipment and advanced node production representing severe chokepoints in the global technology ecosystem. The research documents how AI-driven dependency mapping can detect non-obvious relationships and predict potential disruptions with 91.5% accuracy, outperforming traditional analytical approaches by 37.5%. Case studies demonstrate that critical technology supply chains exhibit increasing concentration despite diversification efforts, with vulnerability metrics particularly elevated in EUV lithography equipment, specialized telecommunications components, and quantum computing materials. The study proposes an integrated economic security framework incorporating targeted industrial policies, public-private resilience partnerships, and multilateral governance mechanisms calibrated to dependency severity levels. This research contributes to the emerging field of technology security by establishing quantitative vulnerability thresholds and developing AI-enhanced methodologies for strategic dependency management in complex global supply networks.

Parisa Foroutan, Salim Lahmiri

Abstract The notion that investors shift to gold during economic market crises remains unverified for many cryptocurrency markets. This paper investigates the connectedness between the 10 most traded cryptocurrencies and gold as well as crude oil markets pre-COVID-19 and during COVID-19. Through the application of various statistical techniques, including cointegration tests, vector autoregressive models, vector error correction models, autoregressive distributed lag models, and Granger causality analyses, we explore the relationship between these markets and assess the safe-haven properties of gold and crude oil for cryptocurrencies. Our findings reveal that during the COVID-19 pandemic, gold is a strong safe-haven for Bitcoin, Litecoin, and Monero while demonstrating a weaker safe-haven potential for Bitcoin Cash, EOS, Chainlink, and Cardano. In contrast, gold only exhibits a strong safe-haven characteristic before the pandemic for Litecoin and Monero. Additionally, Brent crude oil emerges as a strong safe-haven for Bitcoin during COVID-19, while West Texas Intermediate and Brent crude oils demonstrate weaker safe-haven properties for Ether, Bitcoin Cash, EOS, and Monero. Furthermore, the Granger causality analysis indicates that before the COVID-19 pandemic, the causal relationship predominantly flowed from gold and crude oil toward the cryptocurrency markets; however, during the COVID-19 period, the direction of causality shifted, with cryptocurrencies exerting influence on the gold and crude oil markets. These findings provide subtle implications for policymakers, hedge fund managers, and individual or institutional cryptocurrency investors. Our results highlight the need to adapt risk exposure strategies during financial turmoil, such as the crisis precipitated by the COVID-19 pandemic.

Ziliang Gan, Yu Lu, Dong Zhang et al.

In recent years, multimodal benchmarks for general domains have guided the rapid development of multimodal models on general tasks. However, the financial field has its peculiarities. It features unique graphical images (e.g., candlestick charts, technical indicator charts) and possesses a wealth of specialized financial knowledge (e.g., futures, turnover rate). Therefore, benchmarks from general fields often fail to measure the performance of multimodal models in the financial domain, and thus cannot effectively guide the rapid development of large financial models. To promote the development of large financial multimodal models, we propose MME-Finance, an bilingual open-ended and practical usage-oriented Visual Question Answering (VQA) benchmark. The characteristics of our benchmark are finance and expertise, which include constructing charts that reflect the actual usage needs of users (e.g., computer screenshots and mobile photography), creating questions according to the preferences in financial domain inquiries, and annotating questions by experts with 10+ years of experience in the financial industry. Additionally, we have developed a custom-designed financial evaluation system in which visual information is first introduced in the multi-modal evaluation process. Extensive experimental evaluations of 19 mainstream MLLMs are conducted to test their perception, reasoning, and cognition capabilities. The results indicate that models performing well on general benchmarks cannot do well on MME-Finance; for instance, the top-performing open-source and closed-source models obtain 65.69 (Qwen2VL-72B) and 63.18 (GPT-4o), respectively. Their performance is particularly poor in categories most relevant to finance, such as candlestick charts and technical indicator charts. In addition, we propose a Chinese version, which helps compare performance of MLLMs under a Chinese context.

Carlos Alberto Durigan Junior, Fernando Jose Barbin Laurindo

Cryptocurrency can be understood as a digital asset transacted among participants in the crypto economy. Every cryptocurrency must have an associated Blockchain. Blockchain is a Distributed Ledger Technology (DLT) which supports cryptocurrencies, this may be considered as the most promising disruptive technology in the industry 4.0 context. Decentralized finance (DeFi) is a Blockchain-based financial infrastructure, the term generally refers to an open, permissionless, and highly interoperable protocol stack built on public smart contract platforms, such as the Ethereum Blockchain. It replicates existing financial services in a more open and transparent way. DeFi does not rely on intermediaries and centralized institutions. Instead, it is based on open protocols and decentralized applications (Dapps). Considering that there are many digital coins, stablecoins and central bank digital currencies (CBDCs), these currencies should interact among each other sometime. For this interaction the Information Technology elements play an important whole as enablers and IT strategic alignment. This paper considers the strategic alignment model proposed by Henderson and Venkatraman (1993) and Luftman (1996). This paper seeks to answer two main questions 1) What are the common IT elements in the DeFi? And 2) How the elements connect to the IT strategic alignment in DeFi? Through a Systematic Literature Review (SLR). Results point out that there are many IT elements already mentioned by literature, however there is a lack in the literature about the connection between IT elements and IT strategic alignment in a Decentralized Finance (DeFi) architectural network. After final considerations, limitations and future research agenda are presented. Keywords: IT Strategic alignment, Decentralized Finance (DeFi), Cryptocurrency, Digital Economy.

Petter Gottschalk

Half of all finance and insurance firms in Norway report that they are exposed to economic crime, particularly fraud, every year. On the other hand, only eighteen percent in public administration and defense perceive similar exposure to economic crime. However, the estimated fraction of unreported, non-registered economic crime in the country is ninety-four percent. These numbers are some of the results from surveys conducted in Norway in 2005, 2010, and 2023. This article applies the main economic crime categories of fraud, theft, manipulation, and corruption as used by scholars to study the survey results. The corruption category shows the largest gap between perceived exposure and police statistics. Comparison to white-collar crime research indicates higher frequency of theft at the street level and higher frequency of manipulation at the upper echelon. Comparison to future surveys in other countries is encouraged.

Li-Chen Cheng, Wei-Ting Lu, Benjamin Yeo

Abstract In 2021, the abnormal short-term price fluctuations of GameStop, which were triggered by internet stock discussions, drew the attention of academics, financial analysts, and stock trading commissions alike, prompting calls to address such events and maintain market stability. However, the impact of stock discussions on volatile trading behavior has received comparatively less attention than traditional fundamentals. Furthermore, data mining methods are less often used to predict stock trading despite their higher accuracy. This study adopts an innovative approach using social media data to obtain stock rumors, and then trains three decision trees to demonstrate the impact of rumor propagation on stock trading behavior. Our findings show that rumor propagation outperforms traditional fundamentals in predicting abnormal trading behavior. The study serves as an impetus for further research using data mining as a method of inquiry.

Kapil Panda

Public finances are one of the fundamental mechanisms of economic governance that refer to the financial activities and decisions made by government entities to fund public services, projects, and operations through assets. In today's globalized landscape, even subtle shifts in one nation's public debt landscape can have significant impacts on that of international finances, necessitating a nuanced understanding of the correlations between international and national markets to help investors make informed investment decisions. Therefore, by leveraging the capabilities of artificial intelligence, this study utilizes neural networks to depict the correlations between US and International Public Finances and predict the changes in international public finances based on the changes in US public finances. With the neural network model achieving a commendable Mean Squared Error (MSE) value of 2.79, it is able to affirm a discernible correlation and also plot the effect of US market volatility on international markets. To further test the accuracy and significance of the model, an economic analysis was conducted that aimed to correlate the changes seen by the results of the model with historical stock market changes. This model demonstrates significant potential for investors to predict changes in international public finances based on signals from US markets, marking a significant stride in comprehending the intricacies of global public finances and the role of artificial intelligence in decoding its multifaceted patterns for practical forecasting.

Yinheng Li, Shaofei Wang, Han Ding et al.

Recent advances in large language models (LLMs) have opened new possibilities for artificial intelligence applications in finance. In this paper, we provide a practical survey focused on two key aspects of utilizing LLMs for financial tasks: existing solutions and guidance for adoption. First, we review current approaches employing LLMs in finance, including leveraging pretrained models via zero-shot or few-shot learning, fine-tuning on domain-specific data, and training custom LLMs from scratch. We summarize key models and evaluate their performance improvements on financial natural language processing tasks. Second, we propose a decision framework to guide financial professionals in selecting the appropriate LLM solution based on their use case constraints around data, compute, and performance needs. The framework provides a pathway from lightweight experimentation to heavy investment in customized LLMs. Lastly, we discuss limitations and challenges around leveraging LLMs in financial applications. Overall, this survey aims to synthesize the state-of-the-art and provide a roadmap for responsibly applying LLMs to advance financial AI.

عبدالله عبدالعال محمد خزيم خزيم

ملخص الدراسة : Abstract يهدف هذا البحث إلى تسعير تأمين التوقف عن العمل بسبب الحريق وذلک باستخدام نظرية المصداقية Credibility Theory بالتطبيق على شرکة مصر للتأمين ، ويعتمد الباحث فى سبيل الوصول إلى السعر العادل والکافى لتأمين التوقف عن العمل بسبب الحريق على استخدام نظرية المصداقية وذلک من خلال تحديد التوزيع الاحتمالى المناسب لعدد الحوادث (التوزيعات المنفصلة) ، وکذلک تحديد التوزيع الاحتمالى المناسب لقيم الخسائر(التوزيعات المتصلة) ، ثم تطبيق معادلة المصداقية فى تسعير تأمين التوقف عن العمل بسبب الحريق وذلک بتقدير معامل المصداقية Credibility Factor للوصول للقسط الصافى والسعر العادل ، وقد توصلت الدراسة إلى أن التوزيع الاحتمالى المناسب لعدد الحوادث هو التوزيع البواسونى Poisson Distribution ، والتوزيع الاحتمالى المناسب لحجم الخسارة هو توزيع باريتو pareto Distribution ، کما توصلت الدراسة إلى السعر العادل والکافى لتأمين التوقف عن العمل بسبب الحريق باستخدام نظرية المصداقية بالتطبيق على شرکة مصر للتأمين وهو 0.006 ، وبمقارنة السعر العادل الذى توصلت إليه الدراسة لتأمين التوقف عن العمل بسبب الحريق بالسعر السائد والمطبق فى شرکة مصر للتأمين نجد أن الشرکة محل الدراسة تحصل على أسعار أقل من السعر العادل التى توصلت إليه الدراسة وبالتالى فقد أوصت الدراسة شرکة مصر للتأمين بضرورة تعديل أسعار تأمين التوقف عن العمل بسبب الحريق لديها وذلک بزيادة ورفع هذه الأسعار؛ حتى لا تحقق خسائر فى هذا النوع من التأمين فى المستقبل وحتى تتناسب الأسعار مع درجة الخطورة ولکى تتمکن هذه الشرکة من تکوين محفظة اکتتاب متوازنة تمکنها من تخفيض معدلات الخسائر لديها.

Bonnie Burnham

The inclusion of heritage conservation in the United Nations Sustainable Development Goals for 2030, target 11.4, stimulated a broad dialogue among heritage conservation practitioners intent on framing a meaningful role for heritage assets in historic built environments as contributors to sustainable development. Heritage-led regeneration positively impacts many aspects of society, community life, and the public realm, and can also play an important role in reaching zero-carbon environmental conservation goals by slowing the extraction of natural resources for construction, reducing the quantity of building materials sent to landfills, and using traditional technologies and knowledge to reduce operational energy use. Heritage regeneration can also be a strong contributor to economic growth, as restored and reused properties create wealth, serve as community social magnets, and attract prestige and visitors. However, there is little progress towards positioning heritage conservation as a focal point for multilateral public-private co-financing projects and partnerships. In 2021, the Cultural Heritage Finance Alliance (CHiFA) published research about successful models of urban heritage regeneration that engage public-private cooperation. CHiFA now presents a process, developed as part of a study commissioned by the Inter-American Development Bank (IDB), for advancing projects that maximize investment in heritage-led urban regeneration, matching financing strategies with local opportunities, legal frameworks, enabling tools, and the requirements of prospective investors. The result is a marketplace and ecosystem that support civic and community interests through long-term, multi-party collaboration using blended capital investment in heritage as a sustainable development strategy.