Hasil untuk "Public finance"

Priya, Sharma Kavita, Bisht Vartika

The Internet of Things (IoT) is one of the biggest technical advances in recent years, improving our lives in many different ways. One important area of its application is sustainable development. Additionally, funds’ availability is as crucial for sustainable development as IoT. The relationship between technological advancements like big data, blockchain, artificial intelligence (AI), mobile platforms, and the IoT with finance is referred to as “digital finance”. The financial system has been digitalized for a while now. The capacity to quickly, accurately, affordably, and conveniently access vast amounts of complex data related to investments and sustainability consequences accelerates transparency and helps public institutions monitor the regulatory aspects of sustainable development. This study aims to investigate the characteristics of prior studies to comprehend the most recent developments in IoT and sustainable finance. A bibliometric analysis is performed on 306 research publications retrieved from the Scopus database and published between 2011 and 2024. Software tools like VOS-Viewer and Biblioshiny with R Studio are used for the analysis. The study is capable to summarise the traits and patterns of IoT and sustainable finance research. Moreover, the research identifies well-known authors, journals, and institutions and finds the research articles with the highest citation counts and the fastest-growing theme of the domain. This paper offers insightful recommendations to academicians for their future research.

Antoine Bigeard, Langston Nashold, Rayan Krishnan et al.

Artificial Intelligence (AI) technology has emerged as a transformative force in financial analysis and the finance industry, though significant questions remain about the full capabilities of Large Language Model (LLM) agents in this domain. We present the Finance Agent Benchmark, featuring challenging and diverse real-world finance research problems that require LLMs to perform complex analysis using recent SEC filings. We construct the benchmark using a taxonomy of nine financial task categories, developed in consultation with experts from banks, hedge funds, and private equity firms. The dataset includes 537 expert-authored questions covering tasks from information retrieval to complex financial modeling, each validated through a rigorous review process to ensure accuracy and relevance. Moreover, we implement an agentic harness that equips LLMs with tools sufficient to produce accurate responses, including Google Search and EDGAR database access. Overall, the Finance Agent Benchmark provides a comprehensive testbed for measuring the progress of LLM-driven finance agents. Our evaluation reveals significant limitations in current AI capabilities - even the best-performing model (OpenAI o3) achieved only 46.8% accuracy at an average cost of $3.79 per query. This underscores the need for further advancements before reliable deployment in high-stakes finance settings.

Kasymkhan Khubiev, Mikhail Semenov, Irina Podlipnova et al.

Deep Learning is evolving fast and integrates into various domains. Finance is a challenging field for deep learning, especially in the case of interpretable artificial intelligence (AI). Although classical approaches perform very well with natural language processing, computer vision, and forecasting, they are not perfect for the financial world, in which specialists use different metrics to evaluate model performance. We first introduce financially grounded loss functions derived from key quantitative finance metrics, including the Sharpe ratio, Profit-and-Loss (PnL), and Maximum Draw down. Additionally, we propose turnover regularization, a method that inherently constrains the turnover of generated positions within predefined limits. Our findings demonstrate that the proposed loss functions, in conjunction with turnover regularization, outperform the traditional mean squared error loss for return prediction tasks when evaluated using algorithmic trading metrics. The study shows that financially grounded metrics enhance predictive performance in trading strategies and portfolio optimization.

Andrew Blair, Peggy Gregory, Mary Ellen Foster

Though a goal of HRI is the natural integration of social robots into everyday public spaces, real-world studies still occur mostly within controlled environments with predetermined participants. True public spaces present an environment which is largely unconstrained and unpredictable, frequented by a diverse range of people whose goals can often conflict with those of the robot. When combined with the general unfamiliarity most people have with social robots, this leads to unexpected human-robot interactions in these public spaces that are rarely discussed or detected in other contexts. In this paper, we describe atypical users we observed interacting with our robot, and those who did not, during a three-day pilot deployment within a large working church and visitor attraction. We then discuss theoretical future advances in the field that could address these challenges, as well as immediate practical mitigations and strategies to help improve public space human-robot interactions in the present. This work contributes empirical insights into the dynamics of human-robot interaction in public environments and offers actionable guidance for more effective future deployments for social robot designers.

Nohayla Badrane, Zineb Bamousse

In a rapidly evolving landscape marked by continuous change and complex challenges, effective cash management stands as a cornerstone for ensuring business sustainability and driving performance. To address these pressing demands, cash managersare increasingly turning to innovative financing solutions such as venture capital, green finance, crowdfunding, advanced services from Pan-African banks, and blockchain technology. These cutting-edge tools are pivotal in bolstering resilience against market volatility, ecological transitions, and the accelerating pace of technological change. The present article aims to examine how such innovative financial approaches can serve as strategic drivers, enabling businesses to transform challenges into opportunities. The analysis underscores that rethinking cash management through innovation is a critical pathway toboost the performance of Moroccan companies. Therefore, embracing these forward-thinking strategies unlocks new avenues for development empowering them to adapt with agility amidst the uncertainties of a shifting environment.

Hasan Fallahgoul

Recent advances in machine learning have shown promising results for financial prediction using large, over-parameterized models. This paper provides theoretical foundations and empirical validation for understanding when and how these methods achieve predictive success. I examine two key aspects of high-dimensional learning in finance. First, I prove that within-sample standardization in Random Fourier Features implementations fundamentally alters the underlying Gaussian kernel approximation, replacing shift-invariant kernels with training-set dependent alternatives. Second, I establish information-theoretic lower bounds that identify when reliable learning is impossible no matter how sophisticated the estimator. A detailed quantitative calibration of the polynomial lower bound shows that with typical parameter choices, e.g., 12,000 features, 12 monthly observations, and R-square 2-3%, the required sample size to escape the bound exceeds 25-30 years of data--well beyond any rolling-window actually used. Thus, observed out-of-sample success must originate from lower-complexity artefacts rather than from the intended high-dimensional mechanism.

Panpan Fu, Yi-Shuai Ren, Yonggang Tian et al.

This study examines the relationship between environmental, social, and corporate governance (ESG) and firm performance, with a focus on the impact of Buddhism. Our findings suggest the following: (1) The local Buddhism environment weakens the positive relationship between ESG and firm performance, indicating that ESG practices motivated by internal altruism may not contribute to firm performance. (2) The moderating effect of Buddhism is more pronounced in firms with stronger alignment or monitoring, in which ESG practices are more likely to be motivated by the desire for profitability, i.e., privately owned firms and those with higher institutional ownership and media attention. (3) The attenuating effect of Buddhism's moderating role is observed in two categories of firms: those with heightened exposure to ESG-related risks and those operating in recent eras with a greater focus on ESG, which are more likely to benefit from ESG practices with greater external utility.

Rinat Tantashev, Bahtiyor Eshchanov

This article explores the current state and future prospects of developing a green economy in Afghanistan, focusing on renewable energy and fossil resources. It also examines regional cooperation and Afghanistan’s politico-economic relations with its neighbors, especially Uzbekistan. Afghanistan has a significant potential for a green economy due to its reserves of lithium and rare earth metals, essential for modern green technologies. The country is rich in renewable energy resources, which could address environmental challenges, reduce fossil fuel dependence, and create new economic opportunities. This study looks into renewable energy infrastructure, sustainable agriculture, and related challenges and opportunities. The paper starts by providing a literature review which analyzes the data on Afghanistan’s geology, economy, and environmental issues. It conducts stakeholder analysis by collecting data on perceptions and expectations from local communities, environmental organizations, and industry experts. The analysis is conducted through reviewing the current mining sector policies and comparing them with successful international models to propose policy reforms. Key areas for development include expanding renewable energy infrastructure, such as solar and wind power projects, and promoting sustainable agriculture practices. International organizations and donors are supporting these initiatives. In conclusion, Afghanistan’s transition to a green economy is viable and beneficial, requiring sustained efforts from the government, international partners, and the private sector. Strategic investments and cooperation can unlock the full potential of Afghanistan’s green economy, contributing to sustainable development and environmental protection.

Sukhpal Singh Gill, Minxian Xu, Panos Patros et al.

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported “hallucinations” within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial.

Guangye Cao

Why would a blockchain-based startup and its venture capital investors choose to finance by issuing tokens instead of equity? What would be their rates of return for each asset? This paper focuses on the liquidity difference between the two fundraising methods. I build a three-period model of an entrepreneur, two types of investors, and users. Some investors have unforeseen liquidity needs in the middle period that can only be met with tokens. The entrepreneur obtains higher payoff by issuing tokens instead of equity, and the payoff difference increases with investors risk-aversion and need for liquidity in the middle period, as well as the depth of the token market.

Quang Khai Nguyen, Van Cuong Dang

Stock price crash risk is of particular interest in developing countries as it poses a significant threat to investors and can have detrimental effects on the stability of emerging markets. This study investigates the role of financial flexibility in preventing stock price crash risk in the Vietnamese stock market, with a specific focus on the COVID-19 pandemic. Using the fixed-effect, system GMM, and quantile regression methods on a sample of 645 Vietnamese listed firms from 2011 to 2021, this study found that financial flexibility has a significant impact on preventing stock price crash risk. This effect was augmented during the COVID-19 crisis. Furthermore, this study found that financial flexibility mitigated the impact of the COVID-19 crisis on stock price crash risk. The findings provide important implications for firm regulators, shareholders, and investors to respond to similar future crises.

Yasmin Lurusati, René Torenvlied

Abstract Local democratization aims to improve the decentralized capacity of governance regimes to generate meaningful municipal spending geared towards realizing societal outcomes. In the late 1990s, following the Asian financial crisis, Indonesia initiated a significant institutional transition from centralistic and authoritarian rule towards decentralized and more democratic governance through the introduction of direct mayoral elections. Extant research analyzed the effects of the introduction of these elections on local public spending and local societal outcomes separately. This paper offers an integrated analysis of the impact of the introduction of direct mayoral elections on both local public spending and local societal outcomes in 456 Indonesian municipalities between 2002 and 2012. Analyses of growth models, using panel data on three domains (education, health, and infrastructure) provided by Indonesian Ministry of Finance, Indonesian Ministry of Home Affairs, and Statistics Indonesia, show that the introduction of direct mayoral elections in Indonesia resulted in an increased growth in educational expenditures. It also improved outcomes in health and infrastructure domains. However, the introduction of direct mayoral elections reversed a positive association between public spending and the attainment of societal outcomes or worsened a negative association between them. These results would support a view on local democratization in Indonesia asserting that the introduction of direct mayoral elections stimulated local clientelist practices rather than local accountability and policy responsiveness.

Dominik Kubacki

The purpose of this article. The purpose of this study is to identify the occurrence of misselling in the process of offering and selling corporate bonds of GetBack SA. Methodology. The study included a literature review, analysis of secondary data derived from official documents such as decisions issued by the President of the Office of Competition and Consumer Protection, reports of the Supreme Audit Office, and studies by the Financial Ombudsman. The result of the research. The area where the phenomenon of misselling occurred is undoubtedly the case regarding the process of offering and selling bonds of GetBack SA. The circumstances of the case indicate that there were irregularities in the sales process, which consisted in misleading the customers about the offered products, which were not adapted to their needs and carried a high investment risk, disregarding their investor knowledge. Furthermore, in the opinion of the Office of Competition and Consumer Protection and the Financial Ombudsman.

Stefan Loesch

Automated Market Makers (AMMs) are a class of smart contracts on Ethereum and other blockchains that "make markets" autonomously. In other words, AMMs stand ready to trade with other market participants that interact with them, at the conditions determined by the AMM. In this this paper, which relies on the existing and growing corpus of literature available, we review and present the key mathematical and quantitative finance aspects that underpin their operations, including the interesting relationship between AMMs and derivatives pricing and hedging.

Michael Darlin, Georgios Palaiokrassas, Leandros Tassiulas

The rise of Decentralized Finance ("DeFi") on the Ethereum blockchain has enabled the creation of lending platforms, which serve as marketplaces to lend and borrow digital currencies. We first categorize the activity of lending platforms within a standard regulatory framework. We then employ a novel grouping and classification algorithm to calculate the percentage of fund flows into DeFi lending platforms that can be attributed to debt created elsewhere in the system ("debt-financed collateral"). Based on our results, we conclude that the wide-spread use of stablecoins as debt-financed collateral increases financial stability risks in the DeFi ecosystem.

سعد سامى فتحى الغندور

يستهدف البحث التعرف على الواقع الميدانى فى الشرکات المساهمة العامة المصرية، لتأصيل وتوثيق المدخل المقترح لتفعيل المعلومات المحاسبية لترشيد التعاقدات المالية بالاختبار والدليل العملى، وذلک بالاستعانة بأسلوب الاستقصاء على عينة الدراسة الممثلة لأربعة قطاعات حيوية فى الاقتصاد الوطني: (الأدوية- البترول- الصناعات المعدنية- الاتصالات والمعلومات)، وتفريغ البيانات وتحليلها باستخدام الطرق والأساليب الإحصائية المناسبة، لتبيان مدى صلاحية تطبيق المدخل المقترح من عدمه، وتحديد مدى کفاءته وفاعليته فى تحقيق أهدافه المنشودة، واختبار افتراضات البحث. وتوصل الباحث من خلال تلک الدراسة إلى توافر المتطلبات التى تکفل توافر بيئة العمل اللازمة لنجاح تطبيق المدخل المقترح بالشرکات المساهمة العامة المصرية حيث إن تطبيقه يؤدى إلى رفع کفاءة الهياکل التمويلية بتلک الشرکات، حيث يسهم فى مساعدة متخذي القرارات على ترشيد تعاقداتهم المالية، واختيار الهيکل التمويلى الأنسب لظروف وإمکانيات الشرکات الحالية والمستقبلية، وبما ينعکس فى النهاية على تعظيم قيمة الشرکة المساهمة فى الوقت الحالى والمستقبلى، ويوصى الباحث بضروة التزام الشرکات المساهمة المصرية العامة بالتطبيق الصحيح للمدخل المقترح لتفعيل المعلومات المحاسبية، لما له من أثر على النهوض بالاقتصاد الوطنى.

Anatoliy Swishchuk

We show how to solve Merton optimal investment stochastic control problem for Hawkes-based models in finance and insurance, i.e., for a wealth portfolio X(t) consisting of a bond and a stock price described by general compound Hawkes process (GCHP), and for a capital R(t) of an insurance company with the amount of claims described by the risk model based on GCHP. The novelty of the results consists of the new Hawkes-based models and in the new optimal investment results in finance and insurance for those models.

Joanna Łubina

This article aims at identification of the challenges which local government units are facing. Particular emphasis will be put on issues related to the financial management of these units. The article describes problems of performing tasks at a level of a community, county and self-government province. The text presents a range of own and delegated duties of the previously-mentioned units. It also refers to an organizational – legal form of performed tasks. The article puts a special emphasis on dilemmas of financial economy of local government sector. The aim of this paper is to study a transformation of a present financial policy of local government units, to point out weaknesses and strengths of current solutions. A definition of a balanced development is given to identify measures of implementation of this development, and to refer to problems of financial policy which require a joint engagement in solving them. The article also estimates a real chance for a change of the local government sector financial structure (regulations and functioning rules), as well as for an implementation of new decision-making methods that would improve the effectiveness of public finance management.

Zhiheng Yang, Chenxi Li, Yongheng Fang

More and more studies on land transfer prices have been carried out over time. However, the influencing factors of the industrial land transfer price from the perspective of spatial attributes have rarely been explored. Selecting 25 towns as the basic research unit, based on industrial land transfer data, this paper analyzes the influencing factors of the price distribution of industrial land in Dingzhou City, a rural land system reform pilot in China, by using a geographically weighted regression (GWR) model. Eight evaluation factors were selected from five aspects: economy, population, topography, landform, and resource endowment. The results showed that: (1) Compared with the traditional ordinary least squares (OLS) model, the GWR model revealed the spatial differentiation characteristics of the industrial land transfer price in depth. (2) Factors that have a negative correlation with the industrial land transfer price include the proportion of cultivated land area and distance to the city. Factors that have a positive correlation with the industrial land transfer price include the population growth rate, economic growth rate, population density, and number of hospitals per unit area. (3) The results of GWR model analysis showed that the impact of different factors on the various towns of different models had significant spatial differentiation characteristics. This paper will provide a reference for the sustainable use of industrial land in developing countries.

Daniel J. Egger, Claudio Gambella, Jakub Marecek et al.

This article outlines our point of view regarding the applicability, state-of-the-art, and potential of quantum computing for problems in finance. We provide an introduction to quantum computing as well as a survey on problem classes in finance that are computationally challenging classically and for which quantum computing algorithms are promising. In the main part, we describe in detail quantum algorithms for specific applications arising in financial services, such as those involving simulation, optimization, and machine learning problems. In addition, we include demonstrations of quantum algorithms on IBM Quantum back-ends and discuss the potential benefits of quantum algorithms for problems in financial services. We conclude with a summary of technical challenges and future prospects.