International Review of Financial Analysis最新文献

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How government-guided funds cultivate and expand patient capital in China's GEM market? 政府引导基金如何在中国创业板市场培育和扩大耐心资本？

IF 9.8 1区经济学 Q1 BUSINESS, FINANCE

International Review of Financial Analysis

Pub Date : 2025-12-05 DOI: 10.1016/j.irfa.2025.104871

Xiaoyuan Geng, Jiaxin Liu, Mengqi Dai

As an important policy instrument for promoting the high-quality development of the real economy, government-guided funds possess unique advantages in steering long-term capital allocation and fostering patient capital. Using GEM Listed Companies from 2012 to 2023 as the research sample, this paper empirically investigates the impact of government-guided funds on patient capital and explores its heterogeneity. The findings demonstrate that government-guided funds significantly promote the growth of patient capital, and the conclusions remain robust after multiple robustness checks. The study further reveals that government-guided funds enhance patient capital through three channels: reducing Type II agency costs, strengthening market power, and improving total factor productivity. Moreover, firms' innovation level negatively moderates the relationship between government-guided funds and patient capital. Heterogeneity analysis shows that government-guided funds play a stronger role in cultivating patient capital when firms have executives with overseas backgrounds, when the roles of chairman and CEO are separated, when the level of financing constraints is higher, and when analyst coverage is relatively low.

政府引导基金作为推动实体经济高质量发展的重要政策工具，在引导长期资本配置、培育耐心资本方面具有独特优势。本文以2012 - 2023年创业板上市公司为研究样本，实证考察政府引导资金对患者资本的影响，并探讨其异质性。研究结果表明，政府引导基金显著促进了患者资本的增长，并且经过多次稳健性检验，结论仍然稳健。研究进一步发现，政府引导基金通过降低第二类代理成本、增强市场支配力和提高全要素生产率三个渠道提升患者资本。企业创新水平负向调节政府引导资金与耐心资本的关系。异质性分析表明，当公司高管具有海外背景、董事长和CEO角色分离、融资约束程度较高、分析师覆盖率较低时，政府引导基金对培养耐心资本的作用更强。

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引用次数: 0

How do extreme climate can drive commercial banks' credit disbursement? 极端气候如何推动商业银行的信贷支出？

IF 9.8 1区经济学 Q1 BUSINESS, FINANCE

International Review of Financial Analysis

Pub Date : 2025-12-05 DOI: 10.1016/j.irfa.2025.104875

Chengzhi Qiao , Shan Chen

This study examines the impact of extreme climate events on the distribution of commercial bank credit. It analyzes the underlying mechanisms to enhance understanding of financial systems' responses to environmental risks and provide insights for climate-resilient financial policy. The study employs a two-way fixed effects model using unbalanced panel data from Chinese commercial banks, covering the period from 2008 to 2024, and reveals a substantial positive association between extreme climate occurrences and heightened credit provision. The factors influencing this relationship include increased governmental focus on environmental issues, which fosters policy backing for green lending and regulatory incentives, as well as intensified competition among regional banks striving to meet climate-related financing requirements for disaster recovery and adaptation. Subsequent heterogeneity analysis indicates that the impact of credit expansion differs by loan category, with consumer loans—such as personal loans for home repairs and auto loans for replacing vehicles damaged by climate change—demonstrating heightened sensitivity due to their role in fulfilling immediate household liquidity requirements following a disaster. These findings contribute to the literature by identifying extreme climate events as a pivotal factor influencing bank credit behavior, emphasizing the need for comprehensive climate risk management in financial regulation, and highlighting the potential of consumer credit as a crucial instrument for household climate resilience. The study provides policy recommendations for governments to strengthen climate finance frameworks, for banks to develop specialized risk assessment models, and for enterprises to enhance their climate adaptation strategies, thereby fostering a more resilient financial system amid the intensifying effects of climate change.

本研究考察了极端气候事件对商业银行信贷分布的影响。它分析了潜在机制，以加强对金融体系应对环境风险的理解，并为气候适应型金融政策提供见解。该研究采用双向固定效应模型，使用中国商业银行的不平衡面板数据，涵盖2008年至2024年，揭示了极端气候事件与信贷供应增加之间存在实质性的正相关关系。影响这种关系的因素包括：政府更加重视环境问题，这促进了对绿色贷款的政策支持和监管激励措施，以及努力满足灾害恢复和适应的气候相关融资要求的区域银行之间的竞争加剧。随后的异质性分析表明，信贷扩张的影响因贷款类别而异，消费贷款——如用于房屋维修的个人贷款和用于更换因气候变化而损坏的车辆的汽车贷款——由于其在灾难发生后满足家庭即时流动性需求的作用，显示出更高的敏感性。这些发现通过确定极端气候事件是影响银行信贷行为的关键因素，强调在金融监管中需要全面的气候风险管理，并强调消费信贷作为家庭气候适应能力的关键工具的潜力，对文献做出了贡献。该研究为政府加强气候融资框架、银行开发专门的风险评估模型、企业加强气候适应战略提供了政策建议，从而在气候变化影响加剧的情况下建立更具弹性的金融体系。

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引用次数: 0

Financial agglomeration, enterprise structural upgrading, and economic growth disparities 金融集聚、企业结构升级与经济增长差距

IF 9.8 1区经济学 Q1 BUSINESS, FINANCE

International Review of Financial Analysis

Pub Date : 2025-12-05 DOI: 10.1016/j.irfa.2025.104853

Xu Du , Shuanxi Fang

This paper examines China's listed companies from 2013 to 2023, systematically exploring the relationship between financial agglomeration, enterprise structural upgrading, and economic growth disparities from the enterprise level. The research conclusions are as follows: First, an increase in the degree of financial agglomeration can reduce the economic growth disparities among enterprises; second, enterprise structural upgrading can effectively diminish the economic growth disparities among enterprises; third, enterprise structural upgrading serves as a mediating factor between financial agglomeration and economic growth disparities; fourth, the impact of financial agglomeration on reducing economic growth disparities is more pronounced in large enterprises, while its effects on small and medium-sized enterprises are relatively limited; fifth, in industries with high levels of competition, financial agglomeration has a stronger effect on reducing economic growth disparities among enterprises, whereas this effect is weaker in industries with low competition; sixth, in high-competition industries, the mediating effect of enterprise structural upgrading between financial agglomeration and economic growth disparities is more significant.

本文以2013 - 2023年中国上市公司为研究对象，从企业层面系统探索金融集聚、企业结构升级与经济增长差距之间的关系。研究结论如下：第一，金融集聚程度的提高可以减小企业间经济增长差距；第二，企业结构升级可以有效缩小企业间经济增长差距；第三，企业结构升级在金融集聚与经济增长差异之间起中介作用；④金融集聚对缩小经济增长差距的作用在大企业中更为明显，而对中小企业的作用相对有限；第五，在竞争水平高的行业，金融集聚对缩小企业间经济增长差距的作用更强，而在竞争水平低的行业，金融集聚对缩小企业间经济增长差距的作用较弱；第六，在高竞争产业中，企业结构升级在金融集聚与经济增长差距之间的中介作用更为显著。

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引用次数: 0

The sustainability payoff of AI: Revisiting TFP in corporate and societal performance 人工智能的可持续性回报：重新审视企业和社会绩效中的全要素生产率

IF 9.8 1区经济学 Q1 BUSINESS, FINANCE

International Review of Financial Analysis

Pub Date : 2025-12-05 DOI: 10.1016/j.irfa.2025.104891

Wenze Jian , Hang Lu , Zimo Yang , Ziqi Zhong

Using data on Chinese A-share listed firms and regions from 2011–2023, this paper employs a difference-in-differences (DID) framework to evaluate the productivity returns to artificial intelligence (AI) application from both firm-level and societal perspectives. The findings are as follows: First, AI intensity significantly increases firms' total factor productivity (TFP). Second, AI intensity significantly increases social TFP. Third, green financial innovation exerts a significant positive mediating effect on the pathway from AI intensity to firm TFP. Fourth, green financial innovation also partially mediates the pathway from AI intensity to social TFP. Substantively, the paper links micro-level firm transformation with macro-level regional performance, providing empirical evidence and policy implications for understanding the transmission mechanism from digitalization to greening to high-quality growth.

本文利用2011-2023年中国a股上市公司和上市地区的数据，采用差异中差异（DID）框架，从企业层面和社会层面评估了人工智能（AI）应用的生产率回报。研究发现：第一，人工智能强度显著提高了企业的全要素生产率。第二，人工智能强度显著提高了社会全要素生产率。第三，绿色金融创新对人工智能强度到企业全要素生产率的路径具有显著的正向中介作用。第四，绿色金融创新也在人工智能强度向社会全要素生产率的传导过程中起到部分中介作用。实质上，本文将微观层面的企业转型与宏观层面的区域绩效联系起来，为理解从数字化到绿色化再到高质量增长的传导机制提供了实证证据和政策启示。

引用次数: 0

Can the application of artificial intelligence improve corporate governance level of listed companies? The moderating role and heterogeneity of patient capital 人工智能的应用能否提升上市公司的公司治理水平？耐心资本的调节作用及异质性

IF 9.8 1区经济学 Q1 BUSINESS, FINANCE

International Review of Financial Analysis

Pub Date : 2025-12-05 DOI: 10.1016/j.irfa.2025.104869

Nana Yang , Yongqian Tu

Based on data from Chinese A-share listed companies from 2012 to 2023, this paper employs a fixed effects model to examine the impact of artificial intelligence application (AIA) on the corporate governance level of listed firms. The study finds that AIA can significantly enhance corporate governance level. Patient capital plays a significant moderating role between AIA and the level of corporate governance. Heterogeneity analysis indicates that the effect of AIA on corporate governance level differs between state-owned enterprises (SOEs) and private enterprises (PEs). The moderating effect of patient capital on the relationship between AIA and corporate governance level of listed companies also exhibits heterogeneity between high-tech and non-high-tech firms.

本文基于2012 - 2023年中国a股上市公司数据，采用固定效应模型考察人工智能应用对上市公司治理水平的影响。研究发现友邦保险可以显著提升公司治理水平。耐心资本在友邦保险与公司治理水平之间起显著的调节作用。异质性分析表明，友邦保险对公司治理水平的影响在国有企业和民营企业之间存在差异。耐心资本对友邦保险与上市公司治理水平关系的调节作用在高新技术公司与非高新技术公司之间也表现出异质性。

引用次数: 0

Log-periodicity: Fact or fiction? 日志周期性：事实还是虚构？

IF 9.8 1区经济学 Q1 BUSINESS, FINANCE

International Review of Financial Analysis

Pub Date : 2025-12-04 DOI: 10.1016/j.irfa.2025.104848

Klaus Grobys

A common empirical practice in LPPLS applications is to calibrate the model under parameter bounds and then declare an “LPPLS signature” when ADF/PP tests on calibration residuals reject a unit root at conventional tabulated critical values. We show that this procedure exhibits substantial size distortion. Using synthetic series that preserve the roughness and volatility of financial data while excluding log-periodic structure, we compute bootstrap critical values by re-estimating the full two-stage procedure on each synthetic sample. Applied to S&P 500 monthly and daily data, conventional thresholds yield inflated rejection rates. In contrast, the bootstrap restores empirical size to nominal levels and overturns many purported signatures. These findings highlight the need for estimation-aligned inference in LPPLS diagnostics and call for a re-examination of published LPPLS evidence that may reflect size-induced false positives.

在LPPLS应用中，一个常见的经验做法是在参数边界下校准模型，然后在校准残差的ADF/PP测试拒绝常规制表临界值的单位根时声明“LPPLS签名”。我们发现这个过程显示出相当大的尺寸畸变。使用合成序列，保留金融数据的粗糙度和波动性，同时排除对数周期结构，我们通过重新估计每个合成样本的完整两阶段过程来计算自启动临界值。应用于标准普尔500指数的月度和每日数据，传统的门槛会产生过高的拒绝率。相比之下，bootstrap将经验大小恢复到名义水平，并推翻了许多据称的签名。这些发现强调了在LPPLS诊断中需要估计一致的推断，并呼吁重新检查可能反映大小诱导假阳性的已发表的LPPLS证据。

引用次数: 0

Which companies are most at low-carbon transition risks? Evidence from ripple effects in multi-order moments 哪些公司面临的低碳转型风险最大？多阶矩的涟漪效应证据

IF 9.8 1区经济学 Q1 BUSINESS, FINANCE

International Review of Financial Analysis

Pub Date : 2025-12-03 DOI: 10.1016/j.irfa.2025.104843

Haiying Wang , Ting Luo , Chonghui Jiang , Jiangze Du

This study introduces an attribute classification method to identify aggregation centers for low-carbon transition risks across multi-order moments. Specifically, we classify China’s A-share listed companies by their carbon emission and Fama–French three-factor attributes to construct stock return series with different attribute characteristics. We then quantify the ripple effects of low-carbon transition risks through multi-order moments and identify their aggregation centers using the GJRSK model and the Diebold–Yilmaz method. Our empirical results reveal that risk aggregation centers are heterogeneous across different moments, and skewness outperforms other moments in capturing ripple effects of low-carbon transition risks. Further analysis via the Barunik–Krehlik method shows that these risk aggregation centers remain generally consistent across various time horizons. Additionally, low-carbon policies significantly amplify ripple effects for each moment, with aggregation centers experiencing the most pronounced intensification.

提出了一种多阶矩低碳转型风险聚集中心的属性分类方法。具体而言，我们根据中国a股上市公司的碳排放和Fama-French三因素属性对其进行分类，构建具有不同属性特征的股票收益序列。然后，我们通过多阶矩量化低碳转型风险的连锁反应，并使用GJRSK模型和Diebold-Yilmaz方法识别其聚集中心。我们的实证结果表明，风险聚集中心在不同时刻具有异质性，偏度在捕捉低碳转型风险的涟漪效应方面优于其他时刻。通过Barunik-Krehlik方法的进一步分析表明，这些风险聚集中心在不同的时间范围内大致保持一致。此外，低碳政策显著放大了每时每刻的连锁反应，聚集中心的强化最为明显。

引用次数: 0

Green infrastructure and corporate green transformation: Evidence from photovoltaic power stations in China 绿色基础设施与企业绿色转型：来自中国光伏电站的证据

IF 9.8 1区经济学 Q1 BUSINESS, FINANCE

International Review of Financial Analysis

Pub Date : 2025-12-03 DOI: 10.1016/j.irfa.2025.104846

Xinzhi Zhang , Yi Zhou , Xinya Wang

With the intensification of global climate change and rapid urbanization, green infrastructure development has become a critical strategy for balancing ecological environmental protection with socioeconomic development. This study innovatively adopts the construction area of photovoltaic power stations as a proxy variable for green infrastructure. By integrating data from A-share listed companies (2010−2022), empirical analysis reveals that green infrastructure construction significantly promotes corporate green transformation by enhancing the green technological progress efficiency, alleviating financing constraints, and strengthening green innovation capabilities. The heterogeneity analysis shows that the effect is more pronounced in non-heavily polluting industries, high-tech enterprises, and companies located in eastern regions, economically developed areas, and regions with stronger environmental regulation. This study provides micro-level evidence on the economic consequences of green infrastructure, expands the external environmental perspective on corporate green transformation, and provides implications for governments to formulate differentiated green investment policies and for enterprises to optimize location strategies.

随着全球气候变化的加剧和城市化的快速发展，绿色基础设施建设已成为平衡生态环境保护与社会经济发展的重要战略。本研究创新性地采用光伏电站建设面积作为绿色基础设施的代理变量。通过整合a股上市公司2010 ~ 2022年的数据，实证分析发现，绿色基础设施建设通过提高绿色技术进步效率、缓解融资约束、增强绿色创新能力，显著促进企业绿色转型。异质性分析表明，在非重污染行业、高新技术企业、东部地区、经济发达地区和环境监管较强地区的企业中，这种效应更为明显。本研究为绿色基础设施的经济后果提供了微观层面的证据，拓展了企业绿色转型的外部环境视角，为政府制定差别化绿色投资政策和企业优化区位战略提供了启示。

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引用次数: 0

Stock returns forecasting via a novel machine learning method 基于机器学习的股票收益预测方法

IF 9.8 1区经济学 Q1 BUSINESS, FINANCE

International Review of Financial Analysis

Pub Date : 2025-12-03 DOI: 10.1016/j.irfa.2025.104829

Xuan Ouyang , Weiguo Zhang , Xue Gong

Financial time series are difficult to predict due to their non-stationarity and noise. This paper develops a hybrid machine learning model that combines Ensemble Empirical Mode Decomposition (EEMD), Gated Recurrent Units (GRU), and the Informer network. In the task of forecasting S&P 500 index returns with a large set of predictors, we find that the proposed method significantly outperforms thirteen competing models. Compared to traditional recurrent architectures, the proposed model achieves a 61.4 % reduction in root mean square error and a 61 % improvement in mean absolute error and passes statistical tests. The results are robust across various checks. Our findings highlight the value of combining signal decomposition with deep learning, offering practical insights for trading and risk management.

金融时间序列由于具有非平稳性和噪声，难以预测。本文开发了一种混合机器学习模型，该模型结合了集成经验模式分解（EEMD）、门控循环单元（GRU）和Informer网络。在使用大量预测因子预测标准普尔500指数收益的任务中，我们发现所提出的方法显著优于13种竞争模型。与传统的循环模型相比，该模型的均方根误差降低了61.4%，平均绝对误差提高了61%，并通过了统计检验。其结果在各种检查中都是可靠的。我们的研究结果突出了信号分解与深度学习相结合的价值，为交易和风险管理提供了实用的见解。

引用次数: 0

Bank as financial educator: How childhood financial development shapes investment behaviors 银行作为金融教育者：儿童金融发展如何塑造投资行为

IF 9.8 1区经济学 Q1 BUSINESS, FINANCE

International Review of Financial Analysis

Pub Date : 2025-12-03 DOI: 10.1016/j.irfa.2025.104847

Yusen Lei , Longyao Zhang , Yan Zhang , Hao Lu

Human behavior is profoundly shaped by childhood experiences. This study examines how childhood financial development influences investment behavior by using the 1979 banking system reform in China as an exogenous shock. We find that birth cohorts exposed to higher levels of financial development in childhood are more likely to enter the securities market, hold a larger share of risky assets, and construct more diversified and efficient portfolios in adulthood. The potential mechanism is that early exposure to bank branches fosters higher financial literacy, which later drives better financial outcomes. The effect is broadly inclusive, with similar benefits for rural and urban populations. Improvements in financial development later in life only partially offset the influence of early exposure. Our work deepens the understanding of how early-life environments shape financial behavior.

人类的行为深受童年经历的影响。本研究以1979年中国银行体制改革为外生冲击，考察童年金融发展对投资行为的影响。我们发现，在童年时期接受较高金融发展水平的出生队列更有可能进入证券市场，持有更大比例的风险资产，并在成年后构建更多元化和高效的投资组合。潜在的机制是，早期接触银行分支机构培养了更高的金融知识，这后来推动了更好的财务结果。这种影响具有广泛的包容性，对农村和城市人口都有类似的好处。晚年经济发展的改善只能部分抵消早期暴露的影响。我们的工作加深了对早期环境如何影响金融行为的理解。

引用次数: 0

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