Hasil untuk "Public finance"

W. Megginson, Kathleen A. Weiss

C. Flammer

This paper studies green bonds, a relatively new instrument in sustainable finance. I first describe the market for green bonds and characterize the “green bond boom” witnessed in recent years. Second, using firm-level data on green bonds issued by public companies, I examine companies’ financial and environmental performance following the issuance of green bonds. I find that the stock market responds positively to the announcement of green bond issues. Moreover, I document a significant increase in environmental performance, suggesting that green bonds are effective in improving companies’ environmental footprint. These findings are only significant for green bonds that are certified by independent third parties, suggesting that certification is an important governance mechanism in the green bond market. I conclude by discussing potential implications for public policy.

R. Musgrave, A. Peacock

Roy W. Bahl

José Luis Bustelo Gracia, Albert-P. Miró-Pérez

Purpose: This paper aims to examine how artificial intelligence (AI) adoption influences the construction, visibility, and valuation of intangible assets in Spanish publicly listed companies, specifically those included in the IBEX 35 index. Design/methodology/approach: The study employs a convergent mixed-methods approach, combining fixed-effects panel regression, natural language processing (NLP), sentiment analysis, and qualitative case profiling. A composite Intangible Asset Visibility Score (IAVS) is developed, incorporating disclosure frequency, reporting quality, and balance sheet data. The AI Adoption Intensity (AIAI) index is constructed based on the strategic scope and communicative presence of AI initiatives. Data is collected from 210 firm-year observations (2019–2024) and triangulated using financial reports, ESG disclosures, corporate communications, and media coverage. Findings: Results confirm a statistically significant and positive relationship between AI adoption and intangible asset visibility. Firms with higher AIAI scores tend to report intangible assets more frequently and with greater narrative quality. Sectoral asymmetries are notable: finance and telecom outperform traditional sectors like construction. Sentiment and topic modeling show that AI-enhanced disclosures are predominantly framed positively, emphasizing brand value, sustainability, and talent development. Interestingly, R&D intensity was not a significant predictor of intangible asset visibility, suggesting a paradigm shift toward narrative-driven valuation. Research limitations/implications: The reliance on disclosure-based proxies for AI and intangible value may not fully capture internal capabilities. Further studies should explore causality, investor perception, and cross-cultural differences in AI-enabled reporting Practical implications: Managers are encouraged to align AI strategies with corporate reporting frameworks to enhance transparency, stakeholder trust, and market valuation. Regulatory bodies should consider updating disclosure standards to reflect the role of emerging technologies in shaping intangible capital Social Implications: Transparent communication of AI initiatives can improve public trust, inform responsible innovation, and promote ethical AI governance—particularly relevant under the EU’s CSRD and AI Act. Originality/value: This study introduces novel indicators (AIAI and IAVS) to quantify the impact of AI on intangible asset disclosure. It offers empirical evidence from a European context and reframes AI not only as a technological asset but as a meta-capability that amplifies the strategic and symbolic value of intangibles.

Duo Zhang, Jiayu Li, Junyi Mo et al.

Accurate volatility forecasts are vital in modern finance for risk management, portfolio allocation, and strategic decision-making. However, existing methods face key limitations. Fully multivariate models, while comprehensive, are computationally infeasible for realistic portfolios. Factor models, though efficient, primarily use static factor loadings, failing to capture evolving volatility co-movements when they are most critical. To address these limitations, we propose a novel, model-agnostic Factor-Augmented Volatility Forecast framework. Our approach employs a time-varying factor model to extract a compact set of dynamic, cross-sectional factors from realized volatilities with minimal computational cost. These factors are then integrated into both statistical and AI-based forecasting models, enabling a unified system that jointly models asset-specific dynamics and evolving market-wide co-movements. Our framework demonstrates strong performance across two prominent asset classes-large-cap U.S. technology equities and major cryptocurrencies-over both short-term (1-day) and medium-term (7-day) horizons. Using a suite of linear and non-linear AI-driven models, we consistently observe substantial improvements in predictive accuracy and economic value. Notably, a practical pairs-trading strategy built on our forecasts delivers superior risk-adjusted returns and profitability, particularly under adverse market conditions.

Kyeongryul Lee, Heehyeon Kim, Joyce Jiyoung Whang

The rapid adoption of generative AI in the public sector, encompassing diverse applications ranging from automated public assistance to welfare services and immigration processes, highlights its transformative potential while underscoring the pressing need for thorough risk assessments. Despite its growing presence, evaluations of risks associated with AI-driven systems in the public sector remain insufficiently explored. Building upon an established taxonomy of AI risks derived from diverse government policies and corporate guidelines, we investigate the critical risks posed by generative AI in the public sector while extending the scope to account for its multimodal capabilities. In addition, we propose a Systematic dAta generatIon Framework for evaluating the risks of generative AI (SAIF). SAIF involves four key stages: breaking down risks, designing scenarios, applying jailbreak methods, and exploring prompt types. It ensures the systematic and consistent generation of prompt data, facilitating a comprehensive evaluation while providing a solid foundation for mitigating the risks. Furthermore, SAIF is designed to accommodate emerging jailbreak methods and evolving prompt types, thereby enabling effective responses to unforeseen risk scenarios. We believe that this study can play a crucial role in fostering the safe and responsible integration of generative AI into the public sector.

Shristi Tandukar, Tek Maraseni, Tapan Sarker

Abstract Accelerating climate change has harmed food and water security and affected both terrestrial and aquatic systems, hindering efforts to meet many Sustainable Development Goals [SDGs]. Climate finance can help mobilize financial resources and tackle the effects of climate change. This study analyzes existing literature on climate finance more broadly from its beginning to its current status. It reviewed 311 relevant articles from 2005 to 2023 using qualitative content analysis [QCA] and meta-analysis to identify common themes and their classification based on pre-determined article criteria. We also identify research gaps within each theme and suggest priority finance areas. Our result suggests that the periodic publications have drastically increased in the past few years, especially after the Paris Agreement in 2015. With content analysis of prior research, most of the research used quantitative and econometric approaches. With the review of papers, it can be concluded that climate finance is mostly constrained in vulnerable regions in which the risk of climate change and its adverse impacts are delicate, including low-lying coastal areas, SIDS, deserts, mountains, and Polar Regions. Innovative climate finance funding should focus on renewable energy, energy efficiency, and infrastructure that aids adaptation in vulnerable communities. Emphasis should be placed on initiatives that provide both mitigation and adaptation advantages, ensuring a resilient and sustainable future. While research primarily focuses on adaptation and mitigation, the interplay between these two areas requires further exploration. We highlight the knowledge gap in this research domain examining the financing sources for mitigation of adverse climate change from private and public sectors.

Xiang Zhang, Xiaoyang Cui

This study explores the impact of hybrid electric vehicle technology innovation on China's carbon emissions (CO2e). Using Fully Modified Ordinary Least Squares and Dynamic Ordinary Least Squares, our econometric analysis shows that a 1 % increase in IHEVTs corresponds to a 0.17 % reduction in CO2e. In comparison, a 1 % increase in the green digital economy is associated with a 0.31 % decrease in CO2e. This emphasizes the role of innovation and digitalization in emission control. Expansionary fiscal policies and GDP growth boost CO2e by 0.34 % and 0.31 %, respectively, underscoring the environmental impact of economic expansion. Conversely, contractionary fiscal policies and renewable energy consumption lead to CO2e declines of 0.46 % and 0.31 %, highlighting the importance of prudent policies. These findings demonstrate the synergistic impact of technological innovation and policy on China's emissions, providing essential perspectives for achieving long-term growth.

Sajid Iqbal, A. Bilal

PurposeThe study aims to empirically estimate the role of public supports for energy efficiency financing and presents the way forward to mitigate the energy financing barriers that incurred during the COVID-19 crisis.Design/methodology/approachUsing the G7 countries data, the study estimated the nexus between the constructs. Generalized method of moments (GMM) and conventional increasing-smoothing asymptotic of GMM are applied to justify the study findings. Wald econometric technique is also used to robust the results.FindingsThe study findings reported a consistent role of public support on energy efficiency financing indicators, during the COVID-19 crisis period. G7 countries raised funds around 17% through public supports for energy efficiency financing, and it raised 4% of per unit energy usage to GDP, accelerated 16% energy efficiency and 24% output of renewable energy sources, during COVID-19. By this, study findings warrant a maximum support from public offices, energy ministries and other allied departments for energy efficiency optimization.Practical implicationsThe study presents multiple policy implications to enhance energy efficiency through different alternative sources, such as, on-bill financing, direct energy efficiency grant, guaranteed financial contracts for energy efficiency and energy efficiency credit lines. If suggested policy recommendations are applied effectively, this holds the potential to diminish the influence of the COVID-19 crisis and can probably uplift the energy efficiency financing during structural crisis.Originality/valueThe originality of the recent study exists in a novel framework of study topicality. Despite growing literature, the empirical discussion in the field of energy efficiency financing and COVID-19 is still shattered and less studied, which is contributed by this study.

Nikolaos Pippas, Elliot A. Ludvig, Cagatay Turkay

Reinforcement Learning (RL) has experienced significant advancement over the past decade, prompting a growing interest in applications within finance. This survey critically evaluates 167 publications, exploring diverse RL applications and frameworks in finance. Financial markets, marked by their complexity, multi-agent nature, information asymmetry, and inherent randomness, serve as an intriguing test-bed for RL. Traditional finance offers certain solutions, and RL advances these with a more dynamic approach, incorporating machine learning methods, including transfer learning, meta-learning, and multi-agent solutions. This survey dissects key RL components through the lens of Quantitative Finance. We uncover emerging themes, propose areas for future research, and critique the strengths and weaknesses of existing methods.

Yuelin Han, Zhifeng Wu, Pengfei Li et al.

The surging demand for AI has led to a rapid expansion of energy-intensive data centers, impacting the environment through escalating carbon emissions and water consumption. While significant attention has been paid to data centers' growing environmental footprint, the public health burden, a hidden toll of data centers, has been largely overlooked. Specifically, data centers' lifecycle, from chip manufacturing to operation, can significantly degrade air quality through emissions of criteria air pollutants such as fine particulate matter, substantially impacting public health. This paper introduces a principled methodology to model lifecycle pollutant emissions for data centers and computing tasks, quantifying the public health impacts. Our findings reveal that training a large AI model comparable to the Llama-3.1 scale can produce air pollutants equivalent to more than 10,000 round trips by car between Los Angeles and New York City. The growing demand for AI is projected to push the total annual public health burden of U.S. data centers up to more than $20 billion in 2028, rivaling that of on-road emissions of California. Further, the public health costs are more felt in disadvantaged communities, where the per-household health burden could be 200x more than that in less-impacted communities. Finally, we propose a health-informed computing framework that explicitly incorporates public health risk as a key metric for scheduling data center workloads across space and time, which can effectively mitigate adverse health impacts while advancing environmental sustainability. More broadly, we also recommend adopting a standard reporting protocol for the public health impacts of data centers and paying attention to all impacted communities.

Hanyu Zhang, Boyu Qiu, Yuhao Feng et al.

Large language models (LLMs) have demonstrated strong capabilities in language understanding, generation, and reasoning, yet their potential in finance remains underexplored due to the complexity and specialization of financial knowledge. In this work, we report the development of the Baichuan4-Finance series, including a comprehensive suite of foundational Baichuan4-Finance-Base and an aligned language model Baichuan4-Finance, which are built upon Baichuan4-Turbo base model and tailored for finance domain. Firstly, we have dedicated significant effort to building a detailed pipeline for improving data quality. Moreover, in the continual pre-training phase, we propose a novel domain self-constraint training strategy, which enables Baichuan4-Finance-Base to acquire financial knowledge without losing general capabilities. After Supervised Fine-tuning and Reinforcement Learning from Human Feedback and AI Feedback, the chat model Baichuan4-Finance is able to tackle various financial certification questions and real-world scenario applications. We evaluate Baichuan4-Finance on many widely used general datasets and two holistic financial benchmarks. The evaluation results show that Baichuan4-Finance-Base surpasses almost all competitive baselines on financial tasks by significant margins without sacrificing performance on general LLM benchmarks. At the same time, Baichuan4-Finance demonstrates even more impressive performance on financial application scenarios, showcasing its potential to foster community innovation in the financial LLM field.

Michael Cary

Abstract Although the 2022 cryptocurrency market crash prompted despair among investors, the rallying cry, “wagmi” (We’re all gonna make it.) emerged among cryptocurrency enthusiasts in the aftermath. Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors? Using natural language processing techniques applied to Twitter data, this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors. The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors. In particular, cryptocurrency enthusiasts’ tweets became more neutral and, surprisingly, less negative. This result appears to be primarily driven by a deliberate, collectivist effort to promote positivity within the cryptocurrency community (“wagmi”). Considering the more nuanced emotional content of tweets, it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors. Moreover, cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash, with a relative increase in tweet frequency of approximately one tweet per day. An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts.

Yubo Xiao, Muxi Lin, Lu Wang

Abstract To investigate the impact of Green Digital Finance (GDF) policies on sustainable regional development goals, this study exploits the implementation of China’s green finance reform and innovation pilot zones as a quasi-natural experiment to examine the theory and impact of policy channels on sustainable development. A difference-in-differences model was applied to evaluate the impact of policies in these zones based on data from 285 cities in China from 2014 to 2020. Research has shown that the GDF is conducive to achieving sustainable development goals through the effects of financial inclusion and energy transitions, which promote the transformation and upgrading of industrial structures. The impact of the GDF pilot-zone policies on the sustainable development of cities at different levels, locations, resource endowments, and green total factor productivity is heterogeneous. This study provides accurate empirical evidence of the effects of the extensive implementation of the policies adopted in the pilot zones and the expansion of the scale of these zones, and it provides policy recommendations for the GDF.

Sajid Iqbal, Farhad Taghizadeh‐Hesary, M. Mohsin et al.

A substantial amount of green finance is required for the transition to green energy, in order to control global warming. We used a common weight DEA composite indicator to develop a green finance index to measure the combined effects of energy, environment, and financial variables. The resulting green finance index values range from 0.98 to 0.71. According to results, Iceland and Nepal both have a score of 1.00, while Australia is third with 0.98 and Malta the lowest value of 0.71. The UK has a score of 0.23 and India has a score of 0.15.  The findings of this study offer an understanding of the role of green finance in environmental pollution reduction. We suggest several policy implications or solutions for governments, institutions, industries and the public to work towards environmental pollution reduction.A substantial amount of green finance is required for the transition to green energy, in order to control global warming. We used a common weight DEA composite indicator to develop a green finance index to measure the combined effects of energy, environment, and financial variables. The resulting green finance index values range from 0.98 to 0.71. According to results, Iceland and Nepal both have a score of 1.00, while Australia is third with 0.98 and Malta the lowest value of 0.71. The UK has a score of 0.23 and India has a score of 0.15.  The findings of this study offer an understanding of the role of green finance in environmental pollution reduction. We suggest several policy implications or solutions for governments, institutions, industries and the public to work towards environmental pollution reduction.

N. Ameli, P. Drummond, A. Bisaro et al.

The finance sector’s response to pressures around climate change has emphasized disclosure, notably through the recommendations of the Financial Stability Board’s Task Force on Climate-related Financial Disclosures (TCFD). The implicit assumption—that if risks are fully revealed, finance will respond rationally and in ways aligned with the public interest—is rooted in the “efficient market hypothesis” (EMH) applied to the finance sector and its perception of climate policy. For low carbon investment, particular hopes have been placed on the role of institutional investors, given the apparent matching of their assets and liabilities with the long timescales of climate change. We both explain theoretical frameworks (grounded in the “three domains”, namely satisficing, optimizing, and transforming) and use empirical evidence (from a survey of institutional investors), to show that the EMH is unsupported by either theory or evidence: it follows that transparency alone will be an inadequate response. To some extent, transparency can address behavioural biases (first domain characteristics), and improving pricing and market efficiency (second domain); however, the strategic (third domain) limitations of EMH are more serious. We argue that whilst transparency can help, on its own it is a very long way from an adequate response to the challenges of ‘aligning institutional climate finance’.

Álvaro Cartea, Fayçal Drissi, Marcello Monga

Constant product markets with concentrated liquidity (CL) are the most popular type of automated market makers. In this paper, we characterise the continuous-time wealth dynamics of strategic LPs who dynamically adjust their range of liquidity provision in CL pools. Their wealth results from fee income, the value of their holdings in the pool, and rebalancing costs. Next, we derive a self-financing and closed-form optimal liquidity provision strategy where the width of the LP's liquidity range is determined by the profitability of the pool (provision fees minus gas fees), the predictable losses (PL) of the LP's position, and concentration risk. Concentration risk refers to the decrease in fee revenue if the marginal exchange rate (akin to the midprice in a limit order book) in the pool exits the LP's range of liquidity. When the drift in the marginal rate is stochastic, we show how to optimally skew the range of liquidity to increase fee revenue and profit from the expected changes in the marginal rate. Finally, we use Uniswap v3 data to show that, on average, LPs have traded at a significant loss, and to show that the out-of-sample performance of our strategy is superior to the historical performance of LPs in the pool we consider.

Kaihua Qin, Jens Ernstberger, Liyi Zhou et al.

Liquidations in Decentralized Finance (DeFi) are both a blessing and a curse -- whereas liquidations prevent lenders from capital loss, they simultaneously lead to liquidation spirals and system-wide failures. Since most lending and borrowing protocols assume liquidations are indispensable, there is an increased interest in alternative constructions that prevent immediate systemic-failure under uncertain circumstances. In this work, we introduce reversible call options, a novel financial primitive that enables the seller of a call option to terminate it before maturity. We apply reversible call options to lending in DeFi and devise Miqado, a protocol for lending platforms to replace the liquidation mechanisms. To the best of our knowledge, Miqado is the first protocol that actively mitigates liquidations to reduce the risk of liquidation spirals. Instead of selling collateral, Miqado incentivizes external entities, so-called supporters, to top-up a borrowing position and grant the borrower additional time to rescue the debt. Our simulation shows that Miqado reduces the amount of liquidated collateral by 89.82% in a worst-case scenario.

Daniel Polakow, Tim Gebbie, Emlyn Flint

The clarion call for causal reduction in the study of capital markets is intensifying. However, in self-referencing and open systems such as capital markets, the idea of unidirectional causation (if applicable) may be limiting at best, and unstable or fallacious at worst. In this work, we critically assess the use of scientific deduction and causal inference within the study of empirical finance and financial econometrics. We then demonstrate the idea of competing causal chains using a toy model adapted from ecological predator/prey relationships. From this, we develop the alternative view that the study of empirical finance, and the risks contained therein, may be better appreciated once we admit that our current arsenal of quantitative finance tools may be limited to ex post causal inference under popular assumptions. Where these assumptions are challenged, for example in a recognizable reflexive context, the prescription of unidirectional causation proves deeply problematic.