Hasil untuk "Public finance"

J. Alm, G. McClelland, W. Schulze

S. Durlauf

Hsuan‐Chi Chen, J. Ritter

Matthew T. Billett, T. D. King, D. Mauer

Yiyi Ning, Jacob Cherian, Muhammad Safdar Sial et al.

The purpose of the study is to test the role of green bond financing on energy efficiency investment and economic growth. To achieve the study objective, fuzzy decision-making modeling technique is applied. The results revealed that bank loans are now the main source of financing for energy efficiency projects. Project-based financing might be replaced with Energy Performance Contracts (EPC) warranting energy efficiency investment. Moreover, green banks invest both public and private funds in energy efficiency promoting economic growth. The usage of green bonds for financing environmentally beneficial projects or companies is limitless. Providing for screening energy efficiency investment proposals with small payback hurdle rates might have large opportunity costs. Green bonds can be used to remove the financing barriers for green finance and sustainability tool. On this, study provides policy implications to key stakeholders; if suggested policy suggestions implemented successfully, these would help to enhance scope of green bond financing to uplift energy efficiency financing and green growth successfully.

M. Kremer

P. Nightingale, B. Martin, J. Tidd et al.

Daniel W. Drezner

Fulong Wu

Abstract There is a growing body of literature on China’s land reform, land system and land-centred urbanisation. While the contribution of land proceeds to Chinese local public finance and infrastructure investment has been widely acknowledged, few studies examine land through the perspective of financialisation, namely how land development uses financial instruments to generate development finance. The process of land-driven financialisation in China has not been well understood. This paper examines the land mortgage, which has accelerated since 2008, and subsequent waves of financialisation through local government financial vehicles (LGFVs) and Chengtou Bonds (urban construction and investment bonds). We highlight that the adoption of a fiscal stimulus package triggered land financialisation, which started as a development strategy for crisis management in China.

R. Rajan, H. Servaes

E. Saez, J. Slemrod, Seth H. Giertz

Sara J. Scherr, Louise E. Buck, Bemmy Granados et al.

Integrated landscape (bioregional, territorial) management (ILM) is a model for place-based planning and development that integrates values of healthy nature, regenerative economies, human well-being, and social solidarity. This review paper analyzes the support system for ILM to achieve transformative change, highlighting 20 dimensions in five support sub-systems. Though landscape partnerships (LPs) are now widespread, they have little coordinated support to form and have weak capacities, inadequate long-term operational funding, and limited cultural resonance. Landscape programs have proliferated and gained notable system-level support, but LP coalitions and alliances are just emerging, and there is little coordinated provision of LP support services. Despite widespread developments in the knowledge base, methods, and tools for local ILM design, there is little coordinated system support and limited dedicated work on data and IT, impact assessment, or strategic research. Landscape finance tools and business engagement with LPs are being explored, but economic valuation is inadequate, and little financing has shifted to coordinated landscape investments. In public policy, professional planners and international policy frameworks are adopting ILM, but government policies and tenure systems provide sparse support. High-leverage actions can accelerate progress in each dimension. But to fully realize the transformative potential of ILM will require more coherent support strategies.

Min Zhou, Zhiheng Qi, Tianlin Zhu et al.

Chart reasoning, a critical task for automating data interpretation in domains such as aiding scientific data analysis and medical diagnostics, leverages large-scale vision language models (VLMs) to interpret chart images and answer natural language questions, enabling semantic understanding that enhances knowledge accessibility and supports data-driven decision making across diverse domains. In this work, we formalize chart reasoning as a sequential decision-making problem governed by a Markov Decision Process (MDP), thereby providing a mathematically grounded framework for analyzing visual question answering tasks. While recent advances such as multi-step reasoning with Monte Carlo tree search (MCTS) offer interpretable and stochastic planning capabilities, these methods often suffer from redundant path exploration and inefficient reward propagation. To address these challenges, we propose a novel algorithmic framework that integrates a pheromone-guided search strategy inspired by Ant Colony Optimization (ACO). In our approach, chart reasoning is cast as a combinatorial optimization problem over a dynamically evolving search tree, where path desirability is governed by pheromone concentration functions that capture global phenomena across search episodes and are reinforced through trajectory-level rewards. Transition probabilities are further modulated by local signals, which are evaluations derived from the immediate linguistic feedback of large language models. This enables fine grained decision making at each step while preserving long-term planning efficacy. Extensive experiments across four benchmark datasets, ChartQA, MathVista, GRAB, and ChartX, demonstrate the effectiveness of our approach, with multi-agent reasoning and pheromone guidance yielding success rate improvements of +18.4% and +7.6%, respectively.

Tatiana Вeridze, Tetiana Melikhova, Мaryna Аdamenko et al.

The article investigates and models the process of forming financial results of an enterprise operating in technogenic conditions on the basis of the social responsibility of business. It is proposed to consider technogenic conditions that are formed directly by the enterprise itself as self-pollution. The formed economic and mathematical model allowed to analyze the financial results of an enterprise operating in technogenic conditions. The optimal value of technogenic self-pollution was determined. It is shown that when the values ​​of technogenic pollution are less than the optimal value, the profit of the enterprise increases. The application of methods of similarity theory made it possible to present the economic and mathematical model of the enterprise's profit in a dimensionless form, replacing individual parameters with analytical complexes that are recorded in the form of products. This allowed for a reduction in the volume of necessary calculations during research. The importance of the synergy of environmental standards and the financial component of the enterprise, in combination with the component of social responsibility of business, is proven. The methodology for the formation of economic and mathematical modeling of the financial component of enterprises operating in technogenic conditions has been developed. The possibility of using differential equations at a qualitative level is shown. This made it possible to determine the feasibility of using "soft" economic and mathematical models. It has been proven that there is an optimal value of the enterprise's income with the corresponding value of man-made pollution, which is characterized by structural stability. Economic and mathematical modeling of the financial indicators of the enterprise's activity in relation to technogenic self-pollution has made it possible to determine, using a dimensionless approach, the optimal conditions of the cost-target components. The conducted research provides enterprises with a tool for forming a financial strategy for their activities in technogenic conditions on the basis of social responsibility.

Haoyu Dong, Pengkun Zhang, Yan Gao et al.

We introduce FinWorkBench (a.k.a. Finch), a benchmark for evaluating agents on real-world, enterprise-grade finance and accounting workflows that interleave data entry, structuring, formatting, web search, cross-file retrieval, calculation, modeling, validation, translation, visualization, and reporting. Finch is built from authentic enterprise workspaces from Enron (15,000 files and 500,000 emails) and other financial institutions spanning 2000 to 2025, preserving the in-the-wild messiness of multimodal artifacts such as tables and charts across diverse domains including budgeting, trading, and asset management. We propose a workflow construction process that combines LLM-assisted mining of workflows from authentic enterprise environments with expert annotation. Specifically, we use LLM-assisted, expert-verified derivation of workflows from real-world email threads and spreadsheet version histories, followed by meticulous workflow annotation requiring more than 700 hours of expert effort. This process yields 172 composite workflows with 384 tasks, involving 1,710 spreadsheets with 27 million cells, along with PDFs and other artifacts, capturing the intrinsically messy, long-horizon, knowledge-intensive, and collaborative nature of enterprise work. We conduct both human and automated evaluations of frontier AI systems, including GPT 5.1, Claude Sonnet/Opus 4.5, Gemini 3 Pro, Grok 4, and Qwen 3 Max. GPT 5.1 Pro spends an average of 16.8 minutes per workflow yet passes only 38.4% of workflows. Comprehensive case studies further highlight the challenges that real-world enterprise workflows pose for AI agents.

Rafik Mankour, Yassine Chafai, Hamada Saleh et al.

Climate Finance Bench introduces an open benchmark that targets question-answering over corporate climate disclosures using Large Language Models. We curate 33 recent sustainability reports in English drawn from companies across all 11 GICS sectors and annotate 330 expert-validated question-answer pairs that span pure extraction, numerical reasoning, and logical reasoning. Building on this dataset, we propose a comparison of RAG (retrieval-augmented generation) approaches. We show that the retriever's ability to locate passages that actually contain the answer is the chief performance bottleneck. We further argue for transparent carbon reporting in AI-for-climate applications, highlighting advantages of techniques such as Weight Quantization.

Kevin McNamara, Rhea Pritham Marpu

The global financial system stands at an inflection point. Stablecoins represent the most significant evolution in banking since the abandonment of the gold standard, positioned to enable "Banking 2.0" by seamlessly integrating cryptocurrency innovation with traditional finance infrastructure. This transformation rivals artificial intelligence as the next major disruptor in the financial sector. Modern fiat currencies derive value entirely from institutional trust rather than physical backing, creating vulnerabilities that stablecoins address through enhanced stability, reduced fraud risk, and unified global transactions that transcend national boundaries. Recent developments demonstrate accelerating institutional adoption: landmark U.S. legislation including the GENIUS Act of 2025, strategic industry pivots from major players like JPMorgan's crypto-backed loan initiatives, and PayPal's comprehensive "Pay with Crypto" service. Widespread stablecoin implementation addresses critical macroeconomic imbalances, particularly the inflation-productivity gap plaguing modern monetary systems, through more robust and diversified backing mechanisms. Furthermore, stablecoins facilitate deregulation and efficiency gains, paving the way for a more interconnected international financial system. This whitepaper comprehensively explores how stablecoins are poised to reshape banking, supported by real-world examples, current market data, and analysis of their transformative potential.

Jifeng Li, Arnav Grover, Abraham Alpuerto et al.

The financial market is a mission-critical playground for AI agents due to its temporal dynamics and low signal-to-noise ratio. Building an effective algorithmic trading system may require a professional team to develop and test over the years. In this paper, we propose an orchestration framework for financial agents, which aims to democratize financial intelligence to the general public. We map each component of the traditional algorithmic trading system to agents, including planner, orchestrator, alpha agents, risk agents, portfolio agents, backtest agents, execution agents, audit agents, and memory agent. We present two in-house trading examples. For the stock trading task (hourly data from 04/2024 to 12/2024), our approach achieved a return of $20.42\%$, a Sharpe ratio of 2.63, and a maximum drawdown of $-3.59\%$, while the S&P 500 index yielded a return of $15.97\%$. For the BTC trading task (minute data from 27/07/2025 to 13/08/2025), our approach achieved a return of $8.39\%$, a Sharpe ratio of $0.38$, and a maximum drawdown of $-2.80\%$, whereas the BTC price increased by $3.80\%$. Our code is available on \href{https://github.com/Open-Finance-Lab/AgenticTrading}{GitHub}.

Burak Öz, Christof Ferreira Torres, Christoph Schlegel et al.

Decentralized finance (DeFi) markets spread across Layer-1 (L1) and Layer-2 (L2) blockchains rely on arbitrage to keep prices aligned. Today most price gaps are closed against centralized exchanges (CEXes), whose deep liquidity and fast execution make them the primary venue for price discovery. As trading volume migrates on-chain, cross-chain arbitrage between decentralized exchanges (DEXes) will become the canonical mechanism for price alignment. Yet, despite its importance to DeFi-and the on-chain transparency making real activity tractable in a way CEX-to-DEX arbitrage is not-existing research remains confined to conceptual overviews and hypothetical opportunity analyses. We study cross-chain arbitrage with a profit-cost model and a year-long measurement. The model shows that opportunity frequency, bridging time, and token depreciation determine whether inventory- or bridge-based execution is more profitable. Empirically, we analyze one year of transactions (September 2023 - August 2024) across nine blockchains and identify 242,535 executed arbitrages totaling 868.64 million USD volume. Activity clusters on Ethereum-centric L1-L2 pairs, grows 5.5x over the study period, and surges-higher volume, more trades, lower fees-after the Dencun upgrade (March 13, 2024). Most trades use pre-positioned inventory (66.96%) and settle in 9s, whereas bridge-based arbitrages take 242s, underscoring the latency cost of today's bridges. Market concentration is high: the five largest addresses execute more than half of all trades, and one alone captures almost 40% of daily volume post-Dencun. We conclude that cross-chain arbitrage fosters vertical integration, centralizing sequencing infrastructure and economic power and thereby exacerbating censorship, liveness, and finality risks; decentralizing block building and lowering entry barriers are critical to countering these threats.

Mahmut Baydaş, Orhan Emre Elma, Željko Stević

Abstract Financial performance analysis is of vital importance those involved in a business (e.g., shareholders, creditors, partners, and company managers). An accurate and appropriate performance measurement is critical for decision-makers to achieve efficient results. Integrated performance measurement, by its nature, consists of multiple criteria with different levels of importance. Multiple Criteria Decision Analysis (MCDA) methods have become increasingly popular for solving complex problems, especially over the last two decades. There are different evaluation methodologies in the literature for selecting the most appropriate one among over 200 MCDA methods. This study comprehensively analyzed 41 companies traded on the Borsa Istanbul Corporate Governance Index for 10 quarters using SWARA, CRITIC, and SD integrated with eight different MCDA method algorithms to determine the position of Turkey's most transparent companies in terms of financial performance. In this study, we propose "stock returns" as a benchmark in comparing and evaluating MCDA methods. Moreover, we calculate the "rank reversal performance of MCDA methods". Finally, we performed a "standard deviation" analysis to identify the objective and characteristic trends for each method. Interestingly, all these innovative comparison procedures suggest that PROMETHEE II (preference ranking organization method for enrichment of evaluations II) and FUCA (Faire Un Choix Adéquat) are the most suitable MCDA methods. In other words, these methods produce a higher correlation with share price; they have fewer rank reversal problems, the distribution of scores they produce is wider, and the amount of information is higher. Thus, it can be said that these advantages make them preferable. The results show that this innovative methodological procedure based on 'knowledge discovery' is verifiable, robust and efficient when choosing the MCDA method.