Research in International Business and Finance最新文献

Bridging behavioral insights and quantitative finance: AI-powered Black–Litterman framework with technical and sentiment signals 连接行为洞察和定量金融：人工智能驱动的Black-Litterman框架与技术和情绪信号

IF 6.9 2区经济学 Q1 BUSINESS, FINANCE

Research in International Business and Finance

Pub Date : 2026-01-27 DOI: 10.1016/j.ribaf.2026.103329

Manish , Rishman Jot Kaur Chahal

Aiming to bridge quantitative finance with behavioral economics, this study harnesses artificial intelligence (AI) to integrate high-quality market sentiment into portfolio optimization. It evaluates the performance of the Black–Litterman (BL) asset allocation model, incorporating investor views generated from state-of-the-art deep learning (DL) models. These models are trained on three distinct datasets—technical (TD) derived from historical US sectoral ETF prices, sentiment (SD) obtained from Refinitiv’s MarketPsych Analytics (LSEG), and their combination (TSD). The proposed framework replaces subjective expert views with data-driven forecasts to enhance accessibility for retail investors. Portfolios are constructed with daily rebalancing based on DL-forecasted prices and account for transaction costs under different market regimes and risk aversion rates. The findings reveal that BL models incorporating the integrated TSD with lower risk aversion (

λ = 1

) significantly outperform those based on TD, SD, or traditional benchmarks, underscoring the robustness of combining technical and sentiment signals for view generation and highlighting its effectiveness for growth-oriented strategies. Under normal market conditions, TD and SD-based portfolios exhibit comparable average performance on risk-adjusted evaluation metrics; however, in high-volatility regimes, TD-based portfolios consistently outperform their SD counterparts on average. This study advocates for TSD-based, DL-enhanced BL models with lower risk aversion as a robust strategy in dynamic market environments, offering practical guidance for retail investors and insights for policymakers on harnessing AI to strengthen financial decision-making.

本研究旨在架起定量金融与行为经济学的桥梁，利用人工智能（AI）将高质量的市场情绪整合到投资组合优化中。它评估了Black-Litterman （BL）资产配置模型的表现，并结合了最先进的深度学习（DL）模型产生的投资者观点。这些模型是在三个不同的数据集上进行训练的：技术数据集（TD）来自美国行业ETF的历史价格，情绪数据集（SD）来自Refinitiv的市场心理分析（LSEG），以及它们的组合（TSD）。拟议的框架用数据驱动的预测取代了主观的专家观点，以提高散户投资者的可及性。投资组合的构建是基于dl预测价格的每日再平衡，并考虑不同市场制度和风险规避率下的交易成本。研究结果表明，将整合的TSD与较低的风险厌恶（λ=1）相结合的BL模型显著优于基于TD、SD或传统基准的模型，强调了将技术和情绪信号结合起来进行视图生成的鲁棒性，并强调了其对增长导向策略的有效性。在正常的市场条件下，基于TD和sd的投资组合在风险调整后的评估指标上表现出相当的平均表现；然而，在高波动性的情况下，基于td的投资组合的平均表现始终优于SD。本研究提倡将基于tsd的、具有较低风险厌恶度的dl增强BL模型作为动态市场环境中的稳健策略，为散户投资者提供实用指导，并为政策制定者提供利用人工智能加强金融决策的见解。

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引用次数: 0

China government accounting supervision and corporate debt default risk 中国政府会计监管与企业债务违约风险

IF 6.9 2区经济学 Q1 BUSINESS, FINANCE

Research in International Business and Finance

Pub Date : 2026-01-27 DOI: 10.1016/j.ribaf.2026.103328

Lin Zhang , Fan Yong

This paper takes A-share listed companies in Shanghai and Shenzhen from 2000 to 2024 as the research sample and examines the impact of government accounting supervision on corporate debt default risk and its underlying mechanisms. The results show that government accounting supervision significantly reduces firms’ default risk. Further spillover effect tests reveal that regulatory events also exert a deterrent effect on peer firms within the same industry that were not directly inspected. Mechanism analyses indicate that government accounting supervision works through two channels: mitigating both types of agency costs and improving the quality of information disclosure. In addition, heterogeneity analyses suggest that the supervisory effect is more pronounced in firms with high media attention, high industry concentration, and low ownership concentration. This study provides a new perspective on the relationship between government regulation and corporate debt risk and offers practical policy implications for enhancing corporate financial transparency and stability.

本文以2000 - 2024年沪深两市a股上市公司为研究样本，考察政府会计监管对企业债务违约风险的影响及其机制。结果表明，政府会计监管显著降低了企业的违约风险。进一步的溢出效应测试表明，监管事件也对同一行业内未被直接检查的同行公司产生威慑作用。机制分析表明，政府会计监管通过降低代理成本和提高信息披露质量两个渠道发挥作用。此外，异质性分析表明，在媒体关注度高、行业集中度高、所有权集中度低的公司中，监管效应更为显著。本研究为政府监管与企业债务风险之间的关系提供了一个新的视角，并为提高企业财务透明度和稳定性提供了实际的政策启示。

引用次数: 0

Do climate risk and ESG sentiment predict clean energy performance? Evidence from quantile-on-quantile analysis 气候风险和ESG情绪能否预测清洁能源绩效？来自分位数对分位数分析的证据

IF 6.9 2区经济学 Q1 BUSINESS, FINANCE

Research in International Business and Finance

Pub Date : 2026-01-22 DOI: 10.1016/j.ribaf.2026.103327

Nader Naifar

This study examines the predictive power of climate risk and ESG sentiment on the performance of clean energy markets using novel non-parametric econometric techniques. To capture the dependence structures and asymmetries across the conditional distribution of returns, we employ univariate quantile-on-quantile regression (QQR), multivariate quantile-on-quantile regression (MQQR), quantile-on-quantile Granger causality (QQGC), and quantile-on-quantile connectedness (QQC) approaches using data from May 2015 to March 2025. Our findings indicate that ESG momentum is a significant and state-contingent predictor of clean energy returns, with its influence most substantial under bullish market regimes or during recovery phases following downturns. Climate policy uncertainty (CPU) exhibits a nonlinear and asymmetric impact, exerting the most significant predictive power and connectedness during both pessimistic and euphoric states. Investor attention (ATT), while more erratic, plays an amplification role under extreme sentiment conditions. The MQQR model reveals that the effects of ESG, CPU, and ATT are not isolated but interactively reinforce or offset each other, depending on the prevailing market state. The QQGC and QQC results validate the robustness of these interdependencies, confirming that dynamic, joint effects of sentiment and policy signals shape the sensitivity of clean energy markets.

本研究使用新颖的非参数计量经济学技术，检验了气候风险和ESG情绪对清洁能源市场表现的预测能力。为了捕捉收益条件分布的依赖结构和不对称性，我们使用了单变量分位数对分位数回归（QQR）、多变量分位数对分位数回归（MQQR）、分位数对分位数格兰杰因果关系（QQGC）和分位数对分位数连通性（QQC）方法，使用了2015年5月至2025年3月的数据。我们的研究结果表明，ESG势头是清洁能源回报的重要预测指标，其影响在看涨市场制度或经济衰退后的复苏阶段最为显著。气候政策不确定性（CPU）表现出非线性和不对称的影响，在悲观和乐观状态下都发挥了最显著的预测能力和连通性。投资者关注（ATT）虽然更加不稳定，但在极端情绪条件下发挥放大作用。MQQR模型表明，ESG、CPU和ATT的影响不是孤立的，而是相互增强或相互抵消的，这取决于当前的市场状态。QQGC和QQC结果验证了这些相互依赖性的稳健性，证实了情绪和政策信号的动态联合效应塑造了清洁能源市场的敏感性。

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引用次数: 0

Systemic tail dependence in disruptive technology ETFs & crypto assets: A partial correlation network 颠覆性技术etf与加密资产的系统尾部依赖：一个部分相关网络

IF 6.9 2区经济学 Q1 BUSINESS, FINANCE

Research in International Business and Finance

Pub Date : 2026-01-22 DOI: 10.1016/j.ribaf.2026.103316

Nader Naifar

This study investigates the systemic dependence structure among Disruptive Technology Exchange-Traded Funds (ETFs) and Crypto Assets, focusing on dynamic, directional, and tail-specific connectedness. We explore static and dynamic dependence under normal and extreme return regimes from January 2020 to April 2025, utilizing the novel partial correlation-based network framework combined with quantile-specific connectedness measures. Unlike traditional GFEVD-based models, our methodology isolates direct, tail-sensitive interdependencies while filtering out standard shocks. Our results indicate strong state-dependent asymmetries, with significantly intensified co-movements in the lower tail of the return distribution. Dynamic connectedness spikes notably during key events such as the GME/Crypto retail rally, the Terra-Luna crash, and the NVIDIA-driven AI surge. The partial correlation framework further distinguishes itself by capturing sharper structural breaks and more meaningful directional asymmetries than traditional variance-based networks. Portfolio optimization results demonstrate that strategies minimizing connectedness in tail states outperform traditional variance- and correlation-based approaches. Our findings offer insights for tail-sensitive portfolio construction, ETF regulation, and crypto-market risk monitoring.

本研究探讨了颠覆性技术交易所交易基金（etf）和加密资产之间的系统依赖结构，重点关注动态、定向和尾部特定的连通性。我们利用新的基于部分相关的网络框架结合分位数特异性连通性指标，研究了2020年1月至2025年4月正常和极端回报制度下的静态和动态依赖关系。与传统的基于gfevd的模型不同，我们的方法分离了直接的、尾部敏感的相互依赖关系，同时过滤掉了标准冲击。我们的结果表明了强状态依赖的不对称性，在回报分布的下尾部显著加强了共同运动。在GME/Crypto零售反弹、Terra-Luna崩盘以及英伟达（nvidia）驱动的人工智能激增等关键事件期间，动态连通性会显著上升。与传统的基于方差的网络相比，部分相关框架通过捕捉更尖锐的结构断裂和更有意义的方向不对称，进一步使自己与众不同。投资组合优化结果表明，最小化尾部状态连通性的策略优于传统的基于方差和相关性的方法。我们的研究结果为尾部敏感的投资组合构建、ETF监管和加密市场风险监控提供了见解。

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引用次数: 0

Retail investor sentiment in China 中国散户投资者情绪

IF 6.9 2区经济学 Q1 BUSINESS, FINANCE

Research in International Business and Finance

Pub Date : 2026-01-22 DOI: 10.1016/j.ribaf.2026.103315

Yuntian Zhang , Yongjie Zhang , Shen Lin

This paper introduces a stock-level sentiment measure, “Retail Flows” (

R F

), and evaluates its ability to price anomalies and mutual-fund performance in China. We extend a standard model to a setting with strict short-sale constraints and retail dominance, providing a microfoundation for why cross-sectional variation in

R F

reflects retail sentiment and yields testable pricing predictions. We then construct Outflow-Minus-Inflow (

O M I

) by shorting high-

R F

stocks and going long low-

R F

stocks, and benchmark

O M I

against the

P M O

factor of Liu, Stambaugh and Yuan (2019). Empirically,

O M I

delivers an average monthly excess return of 1.03% that remains unexplained by established Chinese factors. A four-factor model including

O M I

better explains prominent Chinese anomalies, accounts for 30% of aggregate mutual-fund excess returns, and explains 70% of performance of mutual fund momentum. Because daily price limits frequently distort prices and turnover,

R F

captures stock-level retail sentiment more reliably than abnormal turnover, helping to explain

O M I

’s superior pricing performance in China.

本文引入了一个股票层面的情绪指标“零售流量”（RF），并评估了其在中国的价格异常和共同基金业绩的能力。我们将标准模型扩展到具有严格卖空限制和零售主导地位的环境中，为RF的横截面变化反映零售情绪并产生可测试的定价预测提供了微观基础。然后，我们通过做空高射频股票和做多低射频股票来构建流出-流入（OMI），并根据Liu， Stambaugh和Yuan（2019）的PMO因子对OMI进行基准测试。从经验上看，OMI的月平均超额回报率为1.03%，这仍然无法解释中国现有的因素。包括OMI在内的四因素模型更好地解释了中国显著的异常现象，占共同基金总超额回报的30%，并解释了共同基金势头表现的70%。由于每日价格限制经常扭曲价格和成交量，RF比异常成交量更可靠地捕捉到库存水平的零售情绪，这有助于解释OMI在中国的优越定价表现。

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引用次数: 0

The impact of extreme temperature on firm entry: Evidence from Guangdong 极端气温对企业进入的影响：来自广东的证据

IF 6.9 2区经济学 Q1 BUSINESS, FINANCE

Research in International Business and Finance

Pub Date : 2026-01-21 DOI: 10.1016/j.ribaf.2026.103314

Jie Zhang , Xin Zhang

The frequent occurrence of extreme temperature events under the background of global climate change may have a far-reaching effect on the vitality of enterprise entry. This study uses the registration information of enterprises in Guangdong Province, China from 2000 to 2020 to explore the relationship between industrial activities and extreme temperatures through a high-dimensional fixed effects model. Results show that for each day of increase in extreme temperature, the entry degree of enterprises decreases by 0.007 points, which is equivalent to 0.7937 % of the mean level. The effect of extreme temperatures on the entry of enterprises is mostly concentrated in municipal areas, the Greater Bay Area, and areas with high power consumption and housing prices. In particular, this effect is more pronounced after the implementation of high-temperature subsidies. In the mechanism analysis, we explore and demonstrate the potential effect channels from the perspectives of enterprise cost, investment, and adaptability. This study contributes to regional economic development by enhancing adaptation to climate risks. It aids in formulating and adjusting climate policies in vulnerable regions, alleviating local environmental inequalities.

在全球气候变化的背景下，极端温度事件的频繁发生可能对企业进入的活力产生深远的影响。本研究利用2000 - 2020年广东省企业注册信息，通过高维固定效应模型探讨工业活动与极端气温的关系。结果表明，极端气温每升高一天，企业的进入程度降低0.007点，相当于平均水平的0.7937 %。极端气温对企业进入的影响多集中在市区、大湾区、高用电量、高房价地区。特别是实行高温补贴后，这种效果更为明显。在机制分析中，我们从企业成本、投资和适应性的角度探索和论证了潜在的影响渠道。本研究通过加强对气候风险的适应，为区域经济发展做出贡献。它有助于制定和调整脆弱地区的气候政策，减轻当地的环境不平等。

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引用次数: 0

Assessing the role of central banks in addressing financial sector carbon emissions 评估央行在解决金融部门碳排放问题中的作用

IF 6.9 2区经济学 Q1 BUSINESS, FINANCE

Research in International Business and Finance

Pub Date : 2026-01-21 DOI: 10.1016/j.ribaf.2026.103318

Farzan Yahya , Chien-Chiang Lee

This study examines how central bank institutional characteristics influence financial sector carbon emissions using a comprehensive dataset of 377 financial institutions across 45 countries from 2002 to 2020. We find that central bank size increases financial sector emissions, while independence, transparency, and solvency reduce emissions, with independence emerging as the dominant determinant followed by transparency. These effects are more pronounced in countries with higher financial development levels. Macroprudential policy development moderates these relationships by attenuating central bank size's positive effects while amplifying institutional quality characteristics' negative effects, confirming that coordinated monetary and prudential policies shape environmental outcomes. Our study also shows that imbalanced transparency-independence configurations prove ineffective, with balanced integration essential for emission reduction. These findings suggest that policymakers should implement differentiated collateral frameworks constraining balance sheet expansion's environmental externalities while prioritizing foundational financial infrastructure development in less developed economies before attempting sophisticated green monetary interventions.

本研究利用2002年至2020年45个国家377家金融机构的综合数据集，考察了央行制度特征对金融部门碳排放的影响。我们发现，央行规模增加了金融部门的排放，而独立性、透明度和偿付能力则减少了排放，其中独立性成为主要决定因素，其次是透明度。这些影响在金融发展水平较高的国家更为明显。宏观审慎政策的发展通过弱化央行规模的积极影响，同时放大制度质量特征的负面影响来调节这些关系，从而证实协调的货币政策和审慎政策会影响环境结果。我们的研究还表明，不平衡的透明度独立配置证明是无效的，平衡的整合对于减排至关重要。这些发现表明，在尝试复杂的绿色货币干预之前，政策制定者应该实施差异化的抵押品框架，约束资产负债表扩张的环境外部性，同时优先考虑欠发达经济体的基础金融基础设施建设。

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引用次数: 0

Intangible capital, digitalisation and scale: Evidence and interactions in firm-level production and competition dynamics 无形资本、数字化和规模：企业层面生产和竞争动态的证据和相互作用

IF 6.9 2区经济学 Q1 BUSINESS, FINANCE

Research in International Business and Finance

Pub Date : 2026-01-20 DOI: 10.1016/j.ribaf.2026.103326

Michael McMahon , Eleanor Doyle , Stephen Kinsella

This paper investigates the role of intangible capital in transforming production dynamics, firm performance, and competition. Using firm-level data and a refined Cobb-Douglas production function framework, we compute output elasticities for inputs across seven business cycles, 1970–2019. Estimates reveal a consistent rise in the output elasticity of intangible capital, surpassing physical capital in the most recent cycle, and a corresponding decline in the variable input elasticity. Trends are pronounced in digital-intensive and service sectors. We explore how digitalisation interacts with intangible investment and contributes to firm growth, profitability, and scale advantages. Results show that intangible capital has a significantly greater marginal impact on profitability for top-performing firms, with one component of intangibles, i.e. organisational capital, emerging as the primary driver of this effect. Larger firms exhibit lower overhead input shares and higher EBIT margins, suggesting economies of scale linked primarily to organisational capital. Results indicate that intangible assets are increasingly non-rival and scalable, reinforcing performance premia of sector/industry leaders. We extend the literature on intangible-biased technological change by identifying mechanisms through which digitalisation and intangible investment reshape firm-level and sector-level dynamics. Findings have implications for competition policy, innovation strategy, and the design of productivity-enhancing interventions in an increasingly intangible-driven economy.

本文研究了无形资本在改变生产动态、企业绩效和竞争中的作用。利用企业层面的数据和改进的柯布-道格拉斯生产函数框架，我们计算了1970-2019年七个商业周期投入的产出弹性。估计显示，在最近的周期中，无形资本的产出弹性持续上升，超过了实物资本，而可变投入弹性相应下降。数字密集型和服务业的发展趋势十分明显。我们将探讨数字化如何与无形投资相互作用，并有助于企业增长、盈利能力和规模优势。研究结果表明，对于表现优异的公司来说，无形资本对盈利能力的边际影响要大得多，其中无形资本的一个组成部分，即组织资本，是这种影响的主要驱动力。较大的公司表现出较低的间接投入份额和较高的息税前利润率，这表明规模经济主要与组织资本有关。研究结果表明，无形资产越来越具有非竞争性和可扩展性，从而强化了行业/行业领导者的绩效溢价。我们通过确定数字化和无形投资重塑公司层面和行业层面动态的机制，扩展了无形偏见技术变革的文献。研究结果对竞争政策、创新战略以及在日益无形驱动的经济中提高生产率的干预措施的设计具有启示意义。

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引用次数: 0

Value effect of AI innovation zones: Green premium and cost reduction pathways in environmental disclosure 人工智能创新园区的价值效应：环境披露中的绿色溢价与成本降低路径

IF 6.9 2区经济学 Q1 BUSINESS, FINANCE

Research in International Business and Finance

Pub Date : 2026-01-19 DOI: 10.1016/j.ribaf.2026.103317

Mengping Liu , Wenjie Zhang , Hao Liang

As artificial intelligence drives the technological revolution, understanding its impact on corporate non-financial governance is essential. Using unbalanced panel data of China’s A-share listed firms, in this study, we employ a multi-period difference-in-differences (DID) model to examine the impact of the Artificial Intelligence Innovation and Development Pilot Zones (AIIDPZ) on corporate environmental disclosure quality, treating the policy rollout as a quasi-natural experiment. We find that the establishment of AIIDPZ significantly elevates the quality of environmental information disclosure. This finding remains robust after a series of tests, including propensity score matching-DID, placebo tests, and corrections for potential biases in two-way fixed effects models. Moreover, AIIDPZ enhances environmental information disclosure quality by improving green total factor productivity and reducing R&D costs. Heterogeneity analysis indicates that larger firms, newer firms, highly competitive industries, and firms facing higher financing constraints benefit most from the establishment of AIIDPZ. Government subsidies and organizational inertia further amplify the impact of AIIDPZ on corporate environmental information disclosure. This study contributes empirical evidence on the micro-level environmental consequences of AI industrial policies and offers policy implications for promoting green development through digital intelligence.

随着人工智能推动技术革命，了解其对公司非财务治理的影响至关重要。本研究利用中国a股上市公司的非平衡面板数据，采用多期差分法（DID）模型，将人工智能创新发展试验区（AIIDPZ）政策的推出视为准自然实验，考察其对企业环境信息披露质量的影响。我们发现，AIIDPZ的建立显著提升了环境信息披露的质量。经过一系列的测试，包括倾向得分匹配did、安慰剂测试和对双向固定效应模型中潜在偏差的修正，这一发现仍然是强有力的。通过提高绿色全要素生产率和降低研发成本，提高环境信息披露质量。异质性分析表明，大企业、新企业、高竞争行业和融资约束程度较高的企业受益最大。政府补贴和组织惯性进一步放大了AIIDPZ对企业环境信息披露的影响。本研究为人工智能产业政策的微观环境后果提供了实证证据，并为通过数字智能促进绿色发展提供了政策启示。

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引用次数: 0

Liquidity creation in dual banking systems: Do insolvency reforms matter? 双重银行体系下的流动性创造：破产改革重要吗？

IF 6.9 2区经济学 Q1 BUSINESS, FINANCE

Research in International Business and Finance

Pub Date : 2026-01-17 DOI: 10.1016/j.ribaf.2026.103312

Saibal Ghosh

Although several aspects of liquidity creation have been addressed in the literature, one area that appears to have been largely bypassed is the impact of insolvency reforms. To address this issue, we utilise bank-level data for countries with dual banking systems from 2010 to 2020 and assess its impact. In this regard, we exploit the staggered nature of insolvency reforms across countries and use a difference-in-differences specification to assess their impact. The findings reveal that insolvency reforms reduce banks' liquidity creation by an average of 2.8 per cent, primarily on the asset side. This impact differs between conventional and Islamic banks, as well as in terms of asset and liability side drivers. We view this as an early exercise for countries with dual banking systems to assess the association between insolvency reforms and liquidity creation.

尽管流动性创造的几个方面已经在文献中得到了解决，但有一个领域似乎在很大程度上被忽略了，那就是破产改革的影响。为了解决这一问题，我们利用2010年至2020年实行双重银行制度的国家的银行层面数据，并评估其影响。在这方面，我们利用各国破产改革的交错性质，并使用差异中之差规范来评估其影响。研究结果显示，破产改革使银行的流动性创造平均减少2.8%，主要是在资产方面。这种影响在传统银行和伊斯兰银行之间是不同的，在资产和负债方面也是不同的。我们认为，这是实行双重银行制度的国家评估破产改革与创造流动性之间关系的一项早期工作。

引用次数: 0