North American Journal of Economics and Finance最新文献

Rival wealth effects in M&A: rethinking the competitive impact of horizontal transactions in the U.S. TMT sector 并购中的竞争财富效应：重新思考美国TMT行业横向交易的竞争影响

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-25 DOI: 10.1016/j.najef.2026.102595

Joshua Neel , Michel Charifzadeh , Tim A. Herberger

Recent antitrust scrutiny in the United States has led to the termination of major mergers in the technology, media, and telecommunications (TMT) sector, reflecting concerns about potential anticompetitive effects. However, sector-agnostic research on announcement effects finds little evidence of such harm from M&A, and TMT-related studies report conflicting results, raising questions about the rationale for aggressive regulatory intervention. This study provides the most comprehensive analysis to date of horizontal M&A effects on rivals within the U.S. TMT sector, analyzing 714 rival announcement reactions across 120 transactions over a period of 20 years. Our findings reveal significant positive shareholder wealth effects for rivals following merger announcements by competitors, suggesting that horizontal consolidation in this sector does not reduce competition. These results resolve previous contradictions in TMT-specific literature and call for a critical reassessment of stringent antitrust enforcement to avoid impeding overall firm value creation. Furthermore, the study challenges the notion that M&A in the TMT sector primarily serves as a competitive strategy for acquiring technological capabilities, offering important implications for both policymakers and industry leaders.

美国最近的反垄断审查导致技术、媒体和电信（TMT）领域的重大合并终止，反映了对潜在反竞争影响的担忧。然而，与行业无关的公告效应研究发现，很少有证据表明并购造成了这种伤害，而与tmt相关的研究报告了相互矛盾的结果，这引发了对积极监管干预理由的质疑。本研究提供了迄今为止最全面的横向并购对美国TMT行业竞争对手影响的分析，分析了20年间120笔交易中714个竞争对手的公告反应。我们的研究结果显示，在竞争对手宣布合并后，竞争对手的股东财富效应显著，这表明该行业的横向整合并未减少竞争。这些结果解决了以前在tmt特定文献中的矛盾，并呼吁对严格的反垄断执法进行批判性的重新评估，以避免阻碍企业的整体价值创造。此外，该研究挑战了TMT行业的并购主要作为获取技术能力的竞争战略的观念，为政策制定者和行业领导者提供了重要的启示。

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引用次数: 0

Inflation shocks: quantile unit root inference for panel data with cross-correlations 通货膨胀冲击：交叉相关面板数据的分位数单位根推断

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-22 DOI: 10.1016/j.najef.2026.102592

Saban Nazlioglu , Dogukan Tarakci , Cagin Karul , Lokman Salih Erdem

The growing empirical literature documents evidence on persistence of shocks to inflation; however, little is known about the nature of inflation shocks with asymmetric persistence and cross-correlations. By introducing panel quantile unit root approach with common shocks for a sample of 75 countries from January-1980 to December-2022, this study provides new insights on the persistence of inflation shocks. The panel quantile unit root analysis sheds light on that (i) inflation appears to exhibit significant cross-correlations across countries at all quantiles, and persistence of inflation shocks shifts notably between low and high quantiles, reflecting asymmetric persistence in inflation dynamics, (ii) inflation rates tend to be mean reverting during low to moderate inflation periods, but more persistent in high inflation period, (iii) inflation becomes persistent at higher thresholds in countries with a history of inflationary episodes. These findings reveal the importance of considering asymmetric persistence and cross-correlations for analyzing inflation shocks.

越来越多的实证文献记录了通胀冲击持续存在的证据；然而，人们对具有不对称持续性和相互相关性的通胀冲击的性质知之甚少。通过对1980年1月至2022年12月期间75个国家的常见冲击样本引入面板分位数单位根方法，本研究为通胀冲击的持久性提供了新的见解。面板分位数单位根分析揭示了(i)通货膨胀似乎在所有分位数的国家中都表现出显著的相互关联，通货膨胀冲击的持续性在低分位数和高分位数之间发生显著变化，反映了通货膨胀动态的不对称持续性；（ii）通货膨胀率在低至中等通货膨胀期间趋于均值回归，但在高通货膨胀期间更为持久。在有通货膨胀历史的国家，通货膨胀持续处于较高的阈值。这些发现揭示了在分析通货膨胀冲击时考虑不对称持久性和相互相关性的重要性。

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引用次数: 0

How do climate and economic policy uncertainties relate to global fossil fuel price dynamics? 气候和经济政策的不确定性与全球化石燃料价格动态有何关系？

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-22 DOI: 10.1016/j.najef.2026.102594

Ali Nawaz , Chi Wei Su , Shaher Yar Khan

As climate and economic policies become more volatile, uncertainty pervades global energy markets. Against this backdrop, we investigate how uncertainties in climate and economic policies relate to global fossil fuel prices, focusing on their asymmetric and time frequency associations. To capture complex dependencies, we apply a quantile-on-quantile regression along with a wavelet method to monthly data from January 1991 to September 2024, capturing dynamic relationships across market conditions and timescales. The findings reveal that heightened CPU tends to correlate with increased fossil fuel prices in the short to medium term, as investors delay decisions and incorporate higher risk premiums. However, in the long run, persistent CPU dampens investment in fossil fuel projects, reducing supply capacity and driving prices downward. Conversely, EPU exerts a mixed influence: it suppresses fossil fuel demand during short-term crises but often fuels price volatility and upward pressure over medium horizons. Overall, global fossil fuel prices respond unevenly to policy shocks, highlighting the need for stable and predictable policy frameworks. Clear, consistent climate and economic policies can dampen extreme price swings in oil, gas, and coal, thereby bolstering global energy security and economic resilience.

随着气候和经济政策变得更加不稳定，全球能源市场充满了不确定性。在此背景下，我们研究了气候和经济政策的不确定性与全球化石燃料价格的关系，重点关注它们的不对称和时频关联。为了捕获复杂的依赖关系，我们对1991年1月至2024年9月的月度数据应用分位数对分位数回归和小波方法，捕获市场条件和时间尺度之间的动态关系。研究结果显示，由于投资者推迟决策并纳入更高的风险溢价，在中短期内，CPU的升高往往与化石燃料价格的上涨相关。然而，从长远来看，持续的CPU抑制了对化石燃料项目的投资，减少了供应能力并推动价格下跌。相反，EPU的影响好坏参半：它在短期危机期间抑制化石燃料需求，但在中期往往加剧价格波动和上涨压力。总体而言，全球化石燃料价格对政策冲击的反应不均衡，凸显了建立稳定和可预测的政策框架的必要性。明确、一致的气候和经济政策可以抑制石油、天然气和煤炭价格的极端波动，从而增强全球能源安全和经济弹性。

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引用次数: 0

Environmental performance and institutions quality in Europe: A Bayesian model averaging approach 欧洲的环境绩效和制度质量：贝叶斯模型平均方法

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-14 DOI: 10.1016/j.najef.2026.102591

Alessandra Canepa , Bodgan Dima

In this paper, we examine the relationship between institutional quality and environmental performance using a Bayesian Model Averaging framework. This approach allows us to define model weights as posterior probabilities, which are consistent across a large number of specified models. Consequently, the methodology enables us to explore the impact of various cofactors that also contribute to the environmental performance of EU countries. Our findings indicate that different aspects of democracy, access to information, the creation and dissemination of knowledge, renewable energy consumption, health expenditures, and financial stability are among the most significant explanatory variables for environmental protection performance in European Union countries. Additionally, we identify at least three groups of countries within the European Union when the most relevant environmental protection variables are used to characterize member countries’ profiles. Our results appear robust to the selection of different prior distributions for regression coefficients.

在本文中，我们使用贝叶斯模型平均框架来检验制度质量与环境绩效之间的关系。这种方法允许我们将模型权重定义为后验概率，这在大量指定模型中是一致的。因此，该方法使我们能够探索各种辅助因素的影响，这些因素也有助于欧盟国家的环境绩效。我们的研究结果表明，民主、信息获取、知识的创造和传播、可再生能源消费、卫生支出和金融稳定等不同方面是欧盟国家环境保护绩效的最重要解释变量。此外，当使用最相关的环境保护变量来描述成员国的概况时，我们确定了欧盟内至少三组国家。我们的结果对选择不同的回归系数先验分布具有稳健性。

引用次数: 0

MRN-based connectedness: A nonlinear approach for capturing systemic risk dynamics in financial systems 基于核磁共振的连通性：捕捉金融系统系统性风险动态的非线性方法

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-08 DOI: 10.1016/j.najef.2025.102572

Shijia Song , Handong Li

Measuring connectedness among financial institutions is critical for monitoring systemic risk, understanding its formation and transmission, identifying key institutions, and formulating effective regulatory policies. Traditional methods, often based on parametric models, typically represent financial relationships using linear correlations or rely on idealized nonlinear mappings, limiting their ability to capture the inherent nonlinear dynamics and complex interdependencies in financial systems. To address this limitation, this study constructs connectedness indicators using multiplex recurrence networks (MRNs). The MRN-based approach embeds time series into phase space to capture their temporal structures and leverages mutual information to quantify nonlinear dependencies among institutions. Additionally, it requires minimal preprocessing, avoids strong assumptions, and reduces reliance on precise parameter estimation. Simulation experiments demonstrate that the MRN-based approach effectively captures changes in tail dependencies across multidimensional returns, closely reflecting systemic risk dynamics. Empirical analyses of China’s publicly listed banks further illustrate its ability to track the evolution of systemic risk, identify systemically important banks, and highlight the increasing role of state-owned banks in economic adjustments. These results suggest that the MRN-based method offers advantages over VAR-based approaches, providing a more nuanced and timely reflection of systemic risk. By emphasizing the nonlinear characteristics of financial variables, this study complements prudential regulatory tools and enhances the understanding of systemic risk evolution in complex financial systems.

衡量金融机构之间的连通性对于监测系统性风险、了解其形成和传播、确定关键机构以及制定有效的监管政策至关重要。传统方法通常基于参数模型，通常使用线性相关性或依赖于理想化的非线性映射来表示金融关系，这限制了它们捕捉金融系统中固有的非线性动态和复杂的相互依赖性的能力。为了解决这一限制，本研究使用多重递归网络（mrn）构建了连通性指标。基于核磁共振的方法将时间序列嵌入相空间以捕获其时间结构，并利用互信息量化机构之间的非线性依赖关系。此外，它需要最少的预处理，避免强假设，并减少对精确参数估计的依赖。模拟实验表明，基于核磁共振的方法有效地捕获了多维回报中尾部依赖关系的变化，密切反映了系统风险动态。对中国上市银行的实证分析进一步说明了其跟踪系统性风险演变、识别系统重要性银行的能力，并突出了国有银行在经济调整中的日益重要的作用。这些结果表明，基于核磁共振的方法比基于var的方法更有优势，可以更细致、更及时地反映系统风险。通过强调金融变量的非线性特征，本研究补充了审慎监管工具，增强了对复杂金融系统中系统性风险演变的理解。

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引用次数: 0

Entropy-Based portfolio optimization under Varma–Tsallis Statistics: Evidence from stock markets Varma-Tsallis统计下基于熵的投资组合优化：来自股票市场的证据

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-03 DOI: 10.1016/j.najef.2026.102581

Muhammad Sheraz , Mihăiță Drăgan , Vasile Preda

In this paper, we propose a novel entropic portfolio model inspired by Cover’s universal portfolio framework, incorporating Tsallis statistics to generalize the traditional approach. Utilizing an

(a, b)

-deformed logarithmic function derived from Tsallis entropy, we introduce the concept of

(a, b)

-growth rate for stock market portfolios and extend it to the Varma–Tsallis entropic framework. Within this setting, we define the optimal

(a, b)

-growth rate and derive the growth-optimal portfolio that maximizes terminal

(a, b)

-wealth over

n

-trading periods. We further establish the asymptotic optimality of our approach, proving that the generalized logarithmic utility portfolio achieves expected returns at least as high as any other strategy under this entropy-based paradigm, ensuring long-run performance dominance. By introducing parameters that govern tail sensitivity and non-extensive entropy effects, our model provides a flexible alternative to conventional strategies. Empirical analyses demonstrate that the Varma–Tsallis portfolio not only adapts more effectively to complex market dynamics but also delivers competitive and often superior performance relative to benchmark Cover’s portfolio strategies, particularly during periods of financial turbulence.

本文在Cover的通用投资组合框架的启发下，提出了一种新的熵投资组合模型，并结合Tsallis统计对传统方法进行了推广。利用由Tsallis熵导出的（a,b）变形对数函数，我们引入了股票市场投资组合的(a,b)-增长率的概念，并将其推广到Varma-Tsallis熵框架。在这种情况下，我们定义了最优（a,b）增长率，并推导出在n个交易周期内使终端（a,b）财富最大化的增长最优投资组合。我们进一步建立了我们的方法的渐近最优性，证明了广义对数效用组合在这种基于熵的范式下实现的预期收益至少与任何其他策略一样高，确保了长期绩效优势。通过引入控制尾部灵敏度和非广泛熵效应的参数，我们的模型为传统策略提供了一个灵活的替代方案。实证分析表明，Varma-Tsallis投资组合不仅能更有效地适应复杂的市场动态，而且相对于基准的Cover投资组合策略，尤其是在金融动荡时期，还能提供有竞争力的、往往更优的表现。

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引用次数: 0

Sustainability disclosure and bank liquidity risk: evidence from global banking sector 可持续性信息披露与银行流动性风险：来自全球银行业的证据

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-03 DOI: 10.1016/j.najef.2026.102582

Jianjin Huang , Song-Lin(Sony) Hsieh , Jia Wang

We examine whether sustainability disclosure mitigates banks’ liquidity risk using an international panel of 640 listed banks from 52 countries over 2008–2023. Liquidity risk is a core yet understudied stability dimension in the ESG–banking literature, despite its critical role in financial resilience. Employing a dynamic difference GMM estimator, propensity score matching, and a multi-period difference-in-differences design exploiting staggered ESG disclosure regulations, we find that higher sustainability disclosure significantly reduces banks’ liquidity risk. This effect is economically meaningful and robust across alternative liquidity measures and extensive sensitivity tests. Decomposing ESG into its components, we show that environmental and social disclosures drive the reduction in liquidity risk, whereas governance disclosure has no discernible effect. The impact is stronger for larger banks and in jurisdictions with voluntary rather than mandatory disclosure regimes, consistent with signaling and credibility theories of voluntary reporting. Our results highlight a novel risk channel through which ESG disclosure influences bank stability, offering actionable insights for bank managers and regulators seeking to enhance liquidity resilience through disclosure policy.

我们使用来自52个国家的640家上市银行在2008-2023年的国际面板来研究可持续性披露是否减轻了银行的流动性风险。尽管流动性风险在金融弹性中起着关键作用，但在esg银行文献中，流动性风险是一个核心但尚未得到充分研究的稳定性维度。采用动态差分GMM估计、倾向得分匹配和利用交错ESG披露规则的多期差异中差异设计，我们发现较高的可持续性披露显著降低了银行的流动性风险。这种效应在替代流动性措施和广泛的敏感性测试中具有经济意义和稳健性。将ESG分解为其组成部分，我们发现环境和社会披露推动了流动性风险的降低，而治理披露则没有明显的影响。对于大型银行和实行自愿而非强制性披露制度的司法管辖区，这种影响更大，这与自愿报告的信号和可信度理论是一致的。我们的研究结果突出了ESG披露影响银行稳定性的一个新的风险渠道，为寻求通过披露政策增强流动性弹性的银行经理和监管机构提供了可操作的见解。

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引用次数: 0

Bank systemic risk prediction based on text mining and explainable machine learning 基于文本挖掘和可解释机器学习的银行系统风险预测

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-01 DOI: 10.1016/j.najef.2025.102577

Pucong Wang, Sumuya Borjigin

This study utilizes textual data from The Wall Street Journal, employing 12 machine learning models to forecast systemic risk in the US banking sector. Then, this paper applies the SHAP method to interpret the prediction results. The empirical conclusions are as follows: Firstly, in terms of time series forecasting, deep learning models exhibit the best performance, tree models demonstrate moderate predictive efficacy, while linear models perform poorly in predictions. Secondly, there is a positive correlation between SHAP values and banking systemic risk, this conclusion fills the previous research gap. Further research reveals that Topic_29 consistently ranks at the top in feature importance across various time windows. Its keywords (interest rate, bank, stock, company, inflation, rate cut, China) suggest that interest rate policies, corporate operations, inflation control, and geoeconomic factors play pivotal roles in systemic risk. Additionally, the study observes a negative correlation between news sentiment and SHAP values; negative sentiment has a stronger impact and a longer duration. Finally, this study links the topic keywords back to the original news texts to elucidate the impact of news on systemic risk across different sliding window periods.

本研究利用《华尔街日报》的文本数据，采用12个机器学习模型来预测美国银行业的系统性风险。然后，应用SHAP方法对预测结果进行解释。实证结论如下：首先，在时间序列预测中，深度学习模型的预测效果最好，树模型的预测效果中等，线性模型的预测效果较差。其次，SHAP值与银行系统性风险之间存在正相关关系，这一结论填补了以往研究的空白。进一步的研究表明，Topic_29在不同时间窗口的特征重要性上始终名列前茅。它的关键词（利率、银行、股票、公司、通货膨胀、降息、中国）表明，利率政策、企业运营、通货膨胀控制和地缘经济因素在系统性风险中起着关键作用。此外，研究发现新闻情绪与SHAP值呈负相关；负面情绪的影响更强，持续时间更长。最后，本研究将主题关键词与原始新闻文本联系起来，以阐明新闻对不同滑动窗口期系统性风险的影响。

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引用次数: 0

Systemic spillovers in high-growth private market sectors: determinants and portfolio implications 高增长私人市场部门的系统性溢出效应：决定因素和投资组合影响

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-01 DOI: 10.1016/j.najef.2025.102579

Adnan Aslam , Rayenda Khresna Brahmana

This study investigates the systematic spillover dynamics across high-growth private market sectors and their key drivers, with particular emphasis on portfolio diversification implications. Using a time-varying parameter vector autoregression framework, we document substantial and persistent return spillovers, with AI, HealthTech, FinTech, and Mobility Tech acting as dominant transmitters, and AgTech, BioPharma, ClimateTech, and Cybersecurity serving primarily as receivers. Spillover intensity peaks during post-pandemic capital inflows and green policy expansions, and declines during monetary tightening and geopolitical shocks. Employing robust regression and eXplainable AI approaches, we identify short-term interest rates, trade policy uncertainty, and geopolitical risk as the most influential determinants of connectedness. Portfolio tests show that minimum correlation and connectedness strategies outperform minimum variance portfolios, achieving higher risk-adjusted returns and better tail-risk protection. Our results provide new insights into the structural dynamics of high-growth private markets and offer a practical framework for spillover-aware asset allocation.

本研究探讨了高增长私人市场部门的系统性溢出动态及其关键驱动因素，特别强调了投资组合多元化的影响。使用时变参数向量自回归框架，我们记录了大量和持续的回报溢出效应，其中人工智能、医疗科技、金融科技和移动科技是主要的发射器，农业科技、生物制药、气候科技和网络安全主要是接收器。溢出强度在大流行后资本流入和绿色政策扩张期间达到峰值，在货币紧缩和地缘政治冲击期间下降。采用稳健回归和可解释的人工智能方法，我们确定短期利率、贸易政策不确定性和地缘政治风险是连通性最具影响力的决定因素。投资组合测试表明，最小相关性和连通性策略优于最小方差投资组合，获得更高的风险调整收益和更好的尾部风险保护。我们的研究结果为高增长私人市场的结构动态提供了新的见解，并为考虑溢出效应的资产配置提供了一个实用的框架。

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引用次数: 0

Expected versus unexpected Inflation:The role of Trade Policy 预期通货膨胀与意外通货膨胀：贸易政策的作用

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-01 DOI: 10.1016/j.najef.2025.102578

Hakan Yilmazkuday

This paper investigates how trade policy shocks, specifically tariffs, distinctly affect the 1-year expected versus unexpected components of U.S. inflation. Using a Bayesian structural vector autoregression model and monthly data from 2007 to 2025, we decompose headline inflation to uncover a powerful and nuanced role for trade policy. We find that tariffs have a statistically significant and positive long-run impact on unexpected inflation. In contrast, while a tariff shock does not produce a statistically significant response in the level of expected inflation, our variance decomposition reveals it is the single largest long-run contributor to its volatility. These effects are distinct from short-run dynamics, where oil prices primarily drive inflation surprises. We also identify a formal structural break in May 2018, after which the effects of monetary policy grew to overshadow those of trade policy. A counterfactual analysis confirms that in the absence of tariff shocks, the inflation path would have been different. A key implication is that trade policy, through tariffs, creates significant long-term uncertainty for inflation expectations, complicating the task of maintaining price stability.

本文研究了贸易政策冲击，特别是关税，如何显著影响美国1年预期通胀率和非预期通胀率。利用贝叶斯结构向量自回归模型和2007年至2025年的月度数据，我们对总体通胀进行了分解，揭示了贸易政策的强大而微妙的作用。我们发现，关税对非预期通胀具有显著的长期积极影响。相比之下，虽然关税冲击在预期通胀水平上不会产生统计上显著的反应，但我们的方差分解显示，它是其波动性的单一最大长期贡献者。这些影响与短期动态不同，在短期动态中，油价主要推动通胀意外。我们还发现，2018年5月出现了正式的结构性断裂，此后货币政策的影响逐渐超过了贸易政策的影响。一项反事实分析证实，如果没有关税冲击，通胀路径将会不同。一个关键的含义是，通过关税，贸易政策给通胀预期带来了重大的长期不确定性，使维持价格稳定的任务复杂化。

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引用次数: 0