Hasil untuk "Public finance"

Morshadul Hasan

Abstract This study examines the role of biofuel production and consumption, alongside green finance, in driving economic expansion within the European Union (EU). Employing panel data from EU countries spanning 2001–2022, the analysis follows a rigorous econometric approach. The econometric approach includes unit root tests (Im–Persaran–Shin and ADF), panel cointegration tests (Pedroni and Kao residual tests), cointegrating regressions (FMOLS and DOLS), and robustness tests (fixed effects, random effects, and systems GMM). The findings reveal that both biofuel production and consumption significantly contribute to the EU’s economic expansion, reinforcing the circular economy framework. In addition, green finance enhances biofuel consumption, further supporting economic growth. The findings also reveal that biofuel production and green finance have a more significant effect on smaller economies than on larger ones. These insights underscore the need for EU policymakers to prioritize cost-effective biofuel production and implement strategies that stimulate consumption, fostering long-term sustainable development.

داليا أبوزيد

المستخلصهدفت الدراسة الحالية إلى البحث في تأثير الواقع المعزز باستخدام أبعاد (المعلومات- التفاعل- التوافق الواقعي- الابتكار) على الصورة الذهنية للعملاء بأبعاده (البعد المعرفي- البعد الوجداني- البعد السلوكي) في البيئة المصرية أثناء عملية التسوق، وقد بلغ حجم عينة الدراسة 384 مفردة وبلغت الاستمارات الصالحة للتحليل 300 استمارة وتم التحليل باستخدام برنامج التحليل الاحصائي SPSS نسخة 26، وقد توصلت البحث إلى وجود علاقة إيجابية بين الواقع المعزز بجميع أبعاده وبين الصورة الذهنية للعملاء بجميع أبعادها، كما توصلت النتائج إلى وجود تأثير معنوي ذو دلالة إحصائية بين أغلب ابعاد الواقع المعزز(المعلومات- التوافق الواقعي- الابتكار) على الصورة الذهنية للعملاء، ولكن بالنسبة لبُعد التفاعل في الواقع المعزز كان هناك علاقة ارتباط بينه وبين الصورة الذهنية للعملاء ولكن من ناحية التأثير كان سلبي ولكنه غير معنوي.التوصية: بما أن الواقع المعزز له تأثير معنوي قوي على الصورة الذهنية للعملاء، يُوصى بالمزيد من الاستثمار في هذه التقنية لتقوية الانطباع العام لدى العملاء وتطوير الواقع المعزز باعتباره أداة تسويقية استراتيجية.بالنسبة لبُعد التفاعل في علاقة الارتباط يظهر أنه عندما يزيد التفاعل تزيد الصورة الذهنية أيضًا (علاقة طردية)، لكن هذا لا يعني أن التفاعل يُسبب هذا التغيير، فقط أنه يتزامن معه أو يسير في نفس الاتجاه أما في نتائج تحليل الانحدار عندما نضع كل الأبعاد معًا في النموذج، فإن التفاعل لا يضيف تفسيرًا إضافيًا مهمًا للصورة الذهنية مقارنة ببقية الأبعاد والسبب في ذلك قد يكون أن تأثير "التفاعل" يتداخل أو يتغطى بتأثير أبعاد أقوى مثل "الابتكار" و"التوافق الواقعي".

Peixin Duan, Jiayi Wang, Ruisi Zhang et al.

BackgroundPolicy pilots and policy diffusion are important tools for national governance and policy innovation in China. Long-term care insurance (LTCI) offers a potential solution to the challenges posed by the aging population. Currently, the pilot program for the LTCI system in China has gone through two phases, but a unified national policy framework has yet to be established. Therefore, the outcomes of the policy pilots and the diffusion process of this policy warrant further exploration.MethodBased on the perspective of policy diffusion, this study employs textual analysis and applies the Policy Modeling Consistency (PMC) index model to evaluate the quality and consistency of LTCI policies. A quantitative evaluation is conducted on 29 LTCI policies from two groups of pilot cities in China. The characteristics of policy diffusion are analyzed from both temporal and spatial dimensions.Conclusion(1) According to the PMC index results, LTCI policies of 25 cities achieved an excellent level, 4 cities reached an acceptable level, and there were no substandard policies among the 29 pilot cities. The overall consistency of LTCI policies in the first batch of pilot cities in China is higher than that in the second batch, with PMC index of 6.61 and 6.23. The LTCI policy still faces many challenges, such as limited funding sources and a narrow scope of care services. (2) The diffusion of LTCI policy across different regions and batches has the following characteristics: the diffusion of LTCI policy has followed an “M-shaped” curve over time. In terms of spatial diffusion, there is an interaction between spatial proximity and social proximity effects. In terms of diffusion pathways, there is a combined effect of vertical and horizontal diffusion, reflecting a hierarchical diffusion pattern.DiscussionThis paper constructs a new analytical framework for studying policy diffusion. Based on the analysis of policy texts using the PMC model, we focus on the analysis of policy quality and consistency to the diffusion characteristics and exploring the underlying reasons for the diffusion of the LTCI policies. This extends the research of LTCI policy from traditional qualitative analysis to the quantitative research domain.

Dan MITRA, Ioana-Florina COITA

This paper investigates how people perceive artificial intelligence (AI), using both original survey data and insights from recent academic and policy literature. As AI technologies become increasingly embedded in daily life—from banking and healthcare to education and justice systems—it is important to understand public sentiment in order to better guide ethical integration and the implementation of effective legislative frameworks. We conducted a survey of 60 individuals with diverse backgrounds to assess their familiarity with AI, perceived benefits and risks, and level of comfort with AI making decisions across various domains. While respondents generally expressed openness toward AI in areas such as transportation and finance, some concerns have emerged around its application in legal proceedings and hiring practices. Some of the key issues included algorithmic bias, erosion of human oversight, and threats to privacy. These results highlight the importance of transparency, public education, and robust governance to correctly align AI deployment with societal expectations and ethical standards.

Hasan Ayaydın, Tolga Ergün, Abdulkadir Barut et al.

Abstract The increasing emphasis on climate change and environmental sustainability worldwide has brought about the convergence and increased focus on financial technologies (FinTech) and green energy initiatives. In light of this, the study’s objective is to investigate how FinTech moderates the relationship between the ecological footprint (EF) and green energy transition (GTE) in the BRICS-T nations between 1990 and 2021. This study examines how fintech moderated the GTE and EF in the BRICS-T from 1990 to 2021. We applied the Fully Modified Ordinary Least Squares (FMOLS) and used Dynamic Ordinary Least Squares (DOLS) as the main estimators for longitudinal analysis. In contrast, Driscoll-Kraay was used to verify the robustness of the results under cross-sectional dependence and heteroskedasticity. The results reveal that FinTech indirectly hinders EF by facilitating GTE. The outcomes also show that FinTech significantly constrains EF, while GDP and industrialization worsen EF. The results also confirm the important role of GTE and foreign direct investment (FDI) in reducing CO2 emissions in BRICS-T countries. Lastly, the paper offers policymakers useful recommendations for lowering EF in light of these outcomes. The study suggests establishing appropriate policies and strategies that encourage FinTech platforms to invest in green energy projects, including financial technology, promoting energy-efficient and low-carbon foreign direct investment, and encouraging GTE.

Glenn Matlin, Mika Okamoto, Huzaifa Pardawala et al.

Language Models (LMs) have demonstrated impressive capabilities with core Natural Language Processing (NLP) tasks. The effectiveness of LMs for highly specialized knowledge-intensive tasks in finance remains difficult to assess due to major gaps in the methodologies of existing evaluation frameworks, which have caused an erroneous belief in a far lower bound of LMs' performance on common Finance NLP (FinNLP) tasks. To demonstrate the potential of LMs for these FinNLP tasks, we present the first holistic benchmarking suite for Financial Language Model Evaluation (FLaME). We are the first research paper to comprehensively study LMs against 'reasoning-reinforced' LMs, with an empirical study of 23 foundation LMs over 20 core NLP tasks in finance. We open-source our framework software along with all data and results.

Arman Abgaryan, Utkarsh Sharma

IntraLayer presents an innovative framework that enables comprehensive interconnectivity in digital finance. The proposed framework comprises a core underlying infrastructure and an overarching strategy to create a pioneering "platform of platforms", serving as an algorithmic fiduciary. By design, this infrastructure optimises transactional efficiency for a broad spectrum of agents, thereby facilitating the sustainable creation of intrinsic economic value. Complementing the infrastructure, our forthcoming work will present an overarching adaptive fiscal policy to optimise IntraLayer's resources, striking a balance between sustaining the network and enhancing the proposal herein.

Ping Zhang, Yiru Wang

Digital transformation significantly impacts firm investment, financing, and value enhancement. A systematic investigation from the corporate finance perspective has not yet been formed. This paper combines bibliometric and content analysis methods to systematically review the evolutionary trend, status quo, hotspots and overall structure of research in digital transformation from 2011 to 2024. The study reveals an emerging and rapidly growing focus on digital transformation research, particularly in developed countries. We categorize the literature into three areas according to bibliometric clustering: the measurements (qualitative and quantitative), impact factors (internal and external), and the economic consequences (investment, financing, and firm value). These areas are divided into ten sub-branches, with a detailed literature review. We also review the existing theories related to digital transformation, identify the current gaps in these papers, and provide directions for future research on each sub-branches.

Alice Da Fonseca, Peter Lake, Ariana Barrenechea

This project is a collaboration between industry and academia to delve into Finance Social Networks, specifically the Board of Directors of public companies. Knowing the connections between Directors and Executives in different companies can generate powerful stories and meaningful insights on investments. A proof of concept in the form of a Data Visualization tool reveals its strength in investigating corporate governance and sustainability, as well as in the partnership between industry and academic institutions.

Jiajian Zheng, Duan Xin, Qishuo Cheng et al.

The stock market is a crucial component of the financial market, playing a vital role in wealth accumulation for investors, financing costs for listed companies, and the stable development of the national macroeconomy. Significant fluctuations in the stock market can damage the interests of stock investors and cause an imbalance in the industrial structure, which can interfere with the macro level development of the national economy. The prediction of stock price trends is a popular research topic in academia. Predicting the three trends of stock pricesrising, sideways, and falling can assist investors in making informed decisions about buying, holding, or selling stocks. Establishing an effective forecasting model for predicting these trends is of substantial practical importance. This paper evaluates the predictive performance of random forest models combined with artificial intelligence on a test set of four stocks using optimal parameters. The evaluation considers both predictive accuracy and time efficiency.

Sina Montazeri, Akram Mirzaeinia, Haseebullah Jumakhan et al.

The published MLP-based DRL in finance has difficulties in learning the dynamics of the environment when the action scale increases. If the buying and selling increase to one thousand shares, the MLP agent will not be able to effectively adapt to the environment. To address this, we designed a CNN agent that concatenates the data from the last ninety days of the daily feature vector to create the CNN input matrix. Our extensive experiments demonstrate that the MLP-based agent experiences a loss corresponding to the initial environment setup, while our designed CNN remains stable, effectively learns the environment, and leads to an increase in rewards.

Hadi Pahlevan Yazdanabad, Mohammad Hoseini, Mahdi Fadaee

Combinatorial choice models are based on the implicit assumption that decision-makers consider all possible combinations that can be made by the options in a given set. Therefore, these models assumed that the chosen combination is the most preferable combination. However, decision-makers may not consider all possible combinations due to the limited attention. Thus, the chosen combination is not necessarily the best. This paper presents a model that can explain such choice behaviors. After presenting the model, we investigate its revealed preference implications and explain how one can make inferences about individuals’ preferences considering their choices in the new context. Finally, for the model to be testable, we present its characterizing axiom and show that it is equivalent to the model.

Jenny Jing Wang, Jianfu Shen, Frederik Pretorius

Abstract In this study, we develop and empirically test a valuation model for a commonly encountered option in office leases: a tenant’s option to renew at future market rent (a fair market value) with lease termination as the maturity date. The model integrates decision analysis with real options analysis and market risk with private risks. “Option value” is defined as the private value of the option to either party pre-contract, while “option price” assumes a fair agreement between transacting parties and can be positive (rental premium paid) or negative (rental discount offered). Without manifest expectations, an analysis of a sample of office leases supports the model’s logic with price estimates in a practical range. The tenants’ option price/value is shown to have a negative relationship with the original/renewal lease term; conversely, the landlords’ option value is positively related to the original/renewal term. Comparative analyses show that transaction costs have a positive effect on tenants’ option value and on prices, while vacancy costs and the vacancy period are both positively related to the landlords’ option value and negatively related to price. Market rent is found to have a negative relationship with option price. Overall, this study provides a theoretical analysis and empirical tests of the value of a real option that allows option holders to renew/extend their contracts at a fair market value.

Elvira Nica, Gheorghe H. Popescu, Milos Poliak et al.

Relevant research has investigated how predictive modeling algorithms, deep-learning-based sensing technologies, and big urban data configure immersive hyperconnected virtual spaces in digital twin cities: digital twin modeling tools, monitoring and sensing technologies, and Internet-of-Things-based decision support systems articulate big-data-driven urban geopolitics. This systematic review aims to inspect the recently published literature on digital twin simulation tools, spatial cognition algorithms, and multi-sensor fusion technology in sustainable urban governance networks. We integrate research developing on how blockchain-based digital twins, smart infrastructure sensors, and real-time Internet of Things data assist urban computing technologies. The research problems are whether: data-driven smart sustainable urbanism requires visual recognition tools, monitoring and sensing technologies, and simulation-based digital twins; deep-learning-based sensing technologies, spatial cognition algorithms, and environment perception mechanisms configure digital twin cities; and digital twin simulation modeling, deep-learning-based sensing technologies, and urban data fusion optimize Internet-of-Things-based smart city environments. Our analyses particularly prove that virtual navigation tools, geospatial mapping technologies, and Internet of Things connected sensors enable smart urban governance. Digital twin simulation, data visualization tools, and ambient sound recognition software configure sustainable urban governance networks. Virtual simulation algorithms, deep learning neural network architectures, and cyber-physical cognitive systems articulate networked smart cities. Throughout January and March 2023, a quantitative literature review was carried out across the ProQuest, Scopus, and Web of Science databases, with search terms comprising “sustainable urban governance networks” + “digital twin simulation tools”, “spatial cognition algorithms”, and “multi-sensor fusion technology”. A Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow diagram was generated using a Shiny App. AXIS (Appraisal tool for Cross-Sectional Studies), Dedoose, MMAT (Mixed Methods Appraisal Tool), and the Systematic Review Data Repository (SRDR) were used to assess the quality of the identified scholarly sources. Dimensions and VOSviewer were employed for bibliometric mapping through spatial and data layout algorithms. The findings gathered from our analyses clarify that Internet-of-Things-based smart city environments integrate 3D virtual simulation technology, intelligent sensing devices, and digital twin modeling.

Firsty Izzata Bella, Inas Inas

This study aims to examine the short-term and long-term effects of Islamic banking financing and the development of sovereign Retail Sukuk (SR) on Indonesia's economic growth during the period 2009: Q1 to 2019: Q3. Islamic banking and Sukuk have the same essential role, namely in terms of financing or raising funds. Outstanding SR is used as an indicator of SR’s development in seeing its impact on Indonesia's economic growth. Through Dickey Fuller-Generalized Least Square (DF-GLS) analysis, Lag-Length Test, Auto-Regressive Distributed Lag (ARDL), Cointegration Bound Testing, this study examines the effect of Islamic banking financing and the development of SR on economic growth. Total Sharia Bank financing and outstanding SR do not have long-term cointegration with Indonesia's economic growth. Meanwhile, in the short term, Indonesia's GDP is influenced positively by total Islamic Bank financing (TFIN) at lag 3 and negatively by the outstanding SR at lag 3 and 4. Researchers only examined the Islamic banking sector, specifically highlighting financing in Islamic banking and SR’s development through nominal outstanding on a quarterly scale. The limitations of the variables studied are becoming the limitations of this study. The government as a policymaker have to provide a support through cooperation between institutions and Medium and Small Enterprises (MSMEs) with Islamic banks in collecting and channeling financing, education, and outreach to the public. Consequently, the deepest layers need to be improved to make SR an individual investment instrument that can support Indonesia's economic growth. The research that examined SR with quantitative methods is still limited. Therefore, this study is expected to contribute to increasing liabilities in Islamic Finance, particularly in Retail Sukuk.

Jiahua Xu, Yebo Feng, Daniel Perez et al.

Decentralized finance (DeFi) is an integral component of the blockchain ecosystem, enabling a range of financial activities through smart-contract-based protocols. Traditional DeFi governance typically involves manual parameter adjustments by protocol teams or token holder votes, and is thus prone to human bias and financial risks, undermining the system's integrity and security. While existing efforts aim to establish more adaptive parameter adjustment schemes, there remains a need for a governance model that is both more efficient and resilient to significant market manipulations. In this paper, we introduce "Auto$.$gov", a learning-based governance framework that employs a deep Qnetwork (DQN) reinforcement learning (RL) strategy to perform semi-automated, data-driven parameter adjustments. We create a DeFi environment with an encoded action-state space akin to the Aave lending protocol for simulation and testing purposes, where Auto$.$gov has demonstrated the capability to retain funds that would have otherwise been lost to price oracle attacks. In tests with real-world data, Auto$.$gov outperforms the benchmark approaches by at least 14% and the static baseline model by tenfold, in terms of the preset performance metric--protocol profitability. Overall, the comprehensive evaluations confirm that Auto$.$gov is more efficient and effective than traditional governance methods, thereby enhancing the security, profitability, and ultimately, the sustainability of DeFi protocols.

Xihan Xiong, Zhipeng Wang, Tianxiang Cui et al.

Technological advancement drives financial innovation, reshaping the traditional finance landscape and redefining user-market interactions. The rise of blockchain and Decentralized Finance (DeFi) underscores this intertwined evolution of technology and finance. While DeFi has introduced exciting opportunities, it has also exposed the ecosystem to new forms of market misconduct. This paper aims to bridge the academic and regulatory gaps by addressing key research questions about market misconduct in DeFi. We begin by discussing how blockchain technology can potentially enable the emergence of novel forms of market misconduct. We then offer a comprehensive definition and taxonomy for understanding DeFi market misconduct. Through comparative analysis and empirical measurements, we examine the novel forms of misconduct in DeFi, shedding light on their characteristics and social impact. Subsequently, we investigate the challenges of building a tailored regulatory framework for DeFi. We identify key areas where existing regulatory frameworks may need enhancement. Finally, we discuss potential approaches that bring DeFi into the regulatory perimeter.

Irwan Suliantoro, Bambang Soedaryono, Muhammad Zilal Hamzah

In state budget preparations in Indonesia, the Ministry of Finance has authority over state budgeting functions, and the National Development Planning Agency has authority over planning functions. Prior to 2003, the planning function was dominant. After 2003, the budgeting function was more dominant. After 2017, planning and budgeting functions were synchronized in almost all annual-budget preparation processes. Based on a focus-group discussion, it was shown that by completing the synchronization process through the Memorandum of Understanding, the relationship between planning and budgeting functions remains separated in two different entities. However, the results of the NVivo analysis show that despite the adjustment of interests, institutional pride and institutional competition still exist. To solve this problem, it is necessary to synchronize legal products not only at the level of government regulations but also at the level of laws.

WANG Jinsong, YANG Weizheng, ZHAO Zening, WEI Jiajia

Blockchain technology has been widely used in finance, public services, the Internet of Things(IoT), network security, supply chains, and other fields.However, the traditional blockchain with a single chain structure has some deficiencies in throughput, transaction confirmation speed, and scalability, which makes it difficult to apply it in some short-term and high concurrency data scenarios.In this paper, the Directed Acyclic Graph(DAG) based blockchain technology has attracted extensive attention and studied by scholars because of its advantages, such as concurrent transaction confirmation, high throughput, and strong scalability.By analyzing and studying the development and evolution, evaluation methods, optimization direction, and application scenarios of the existing DAG based blockchains, this paper explores the feasibility of DAG based blockchains in landing applications.Through the development of a mainstream DAG based blockchain, it compares the advantages and disadvantages of traditional blockchains and DAG based blockchains, analyzes the existing blockchain attribute evaluation methods, and summarizes the current DAG based blockchain evaluation results.On this basis, this paper summarizes the optimization methods of the existing DAG based blockchain from the aspects of transaction confirmation speed, system throughput, system security, and storage structure, and summarizes the application of a DAG based blockchain in data management, data sharing based on edge computing and federated learning, and data security for access control and privacy protection.Finally, it points out the main problems and challenges in the current studies, and provides further research directions.

Pawel Dlotko, Wanling Qiu, Simon Rudkin

Finance is heavily influenced by data-driven decision-making. Meanwhile, our ability to comprehend the full informational content of data sets remains impeded by the tools we apply in analysis, especially where the data is high-dimensional. Presenting the Topological Data Analysis Ball Mapper algorithm this paper illuminates a new means of seeing the detail in data from data shape. With comparisons to existing approaches and illustrative examples, the value of the new tool is shown. Directions for employing Ball Mapper in practice are given and the benefits are reviewed.