Pub Date : 2026-03-01Epub Date: 2025-12-29DOI: 10.1016/j.najef.2025.102574
George K. Zestos , Yixiao Jiang , Robert C. Winder , Charles Matzen
This study investigates the long-run relationship between public debt and economic growth in Canada from 1960 to 2022 using an Autoregressive Distributed Lag (ARDL) model. By incorporating key macroeconomic variables such as world GDP, the current account balance, and long-term interest rates, the analysis captures the macroeconomic dynamics of Canada’s small open economy. The findings reveal a negative relationship between public debt and economic growth in Canada, suggesting that fiscal prudence is crucial for sustained economic performance. Specifically, a 1% annual increase in public debt results in a 0.6–0.7% reduction in real GDP. Moreover, external factors such as global economic conditions and interest rates significantly influence Canada’s economic trajectory. These insights offer valuable policy implications not only for Canada, but also for similar open economies grappling with rising public debt levels.
{"title":"The debt-growth nexus in Canada: evidence from an open-economy ARDL model","authors":"George K. Zestos , Yixiao Jiang , Robert C. Winder , Charles Matzen","doi":"10.1016/j.najef.2025.102574","DOIUrl":"10.1016/j.najef.2025.102574","url":null,"abstract":"<div><div>This study investigates the long-run relationship between public debt and economic growth in Canada from 1960 to 2022 using an Autoregressive Distributed Lag (ARDL) model. By incorporating key macroeconomic variables such as world GDP, the current account balance, and long-term interest rates, the analysis captures the macroeconomic dynamics of Canada’s small open economy. The findings reveal a negative relationship between public debt and economic growth in Canada, suggesting that fiscal prudence is crucial for sustained economic performance. Specifically, a 1% annual increase in public debt results in a 0.6–0.7% reduction in real GDP. Moreover, external factors such as global economic conditions and interest rates significantly influence Canada’s economic trajectory. These insights offer valuable policy implications not only for Canada, but also for similar open economies grappling with rising public debt levels.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"83 ","pages":"Article 102574"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-11DOI: 10.1016/j.najef.2026.102598
Hatem Brik
{"title":"Corrigendum to “Dynamic distortions of the security market line: Evidence from asymmetric volatility and regime-switching models” [N. Am. J. Econ. Financ. 82 (2026) 102566]","authors":"Hatem Brik","doi":"10.1016/j.najef.2026.102598","DOIUrl":"10.1016/j.najef.2026.102598","url":null,"abstract":"","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"83 ","pages":"Article 102598"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-27DOI: 10.1016/j.najef.2026.102610
Monica Singhania , Surabhi Seth , Chanchal Saini
We investigate volatility spillovers between green and dirty cryptocurrencies and North American clean technology and ESG equity using a quantile time–frequency connectedness approach. Leveraging an energy-efficiency-based classification of cryptocurrencies, we examine their dynamic interactions with sustainability-focused equity markets. The results reveal that connectedness varies across quantiles and time horizons, with heightened short-run spillovers during market stress. Bitcoin, Ethereum, and Cardano alternate between transmitter and receiver roles across regimes, whereas Ripple more consistently acts as a net receiver. Clean-technology and ESG equities exhibit state-dependent behaviour, shifting between shock absorption and propagation during systemic disruptions. Determinants analysis indicates that macro-financial uncertainty measures display horizon-specific associations with spillovers, while connectedness itself exhibits strong persistence, underscoring the path-dependent nature of systemic risk. Translating these findings into portfolio strategies, we show that minimum connectedness portfolios provide improved downside protection relative to traditional minimum variance and minimum correlation approaches during high-spillover states. By focusing on North American clean technology markets and situating the analysis within major systemic episodes, including the COVID-19 pandemic, the Russia-Ukraine conflict, the 2022–2023 monetary tightening cycle, the Terra-Luna and FTX-led crypto crises, and Ethereum’s energy transition from dirty to clean cryptocurrency post-merge in September 2022, the study offers a regionally grounded assessment of how North American climate-aligned equities and digital assets co-evolve under stress. The results offer valuable insights for investors and policymakers navigating increasingly climate-sensitive and digitally integrated financial systems.
{"title":"Quantile connectedness among green and dirty cryptocurrencies and North American clean technology and ESG","authors":"Monica Singhania , Surabhi Seth , Chanchal Saini","doi":"10.1016/j.najef.2026.102610","DOIUrl":"10.1016/j.najef.2026.102610","url":null,"abstract":"<div><div>We investigate volatility spillovers between green and dirty cryptocurrencies and North American clean technology and ESG equity using a quantile time–frequency connectedness approach. Leveraging an energy-efficiency-based classification of cryptocurrencies, we examine their dynamic interactions with sustainability-focused equity markets. The results reveal that connectedness varies across quantiles and time horizons, with heightened short-run spillovers during market stress. Bitcoin, Ethereum, and Cardano alternate between transmitter and receiver roles across regimes, whereas Ripple more consistently acts as a net receiver. Clean-technology and ESG equities exhibit state-dependent behaviour, shifting between shock absorption and propagation during systemic disruptions. Determinants analysis indicates that macro-financial uncertainty measures display horizon-specific associations with spillovers, while connectedness itself exhibits strong persistence, underscoring the path-dependent nature of systemic risk. Translating these findings into portfolio strategies, we show that minimum connectedness portfolios provide improved downside protection relative to traditional minimum variance and minimum correlation approaches during high-spillover states. By focusing on North American clean technology markets and situating the analysis within major systemic episodes, including the COVID-19 pandemic, the Russia-Ukraine conflict, the 2022–2023 monetary tightening cycle, the Terra-Luna and FTX-led crypto crises, and Ethereum’s energy transition from dirty to clean cryptocurrency post-merge in September 2022, the study offers a regionally grounded assessment of how North American climate-aligned equities and digital assets co-evolve under stress. The results offer valuable insights for investors and policymakers navigating increasingly climate-sensitive and digitally integrated financial systems.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"83 ","pages":"Article 102610"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-08DOI: 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.
{"title":"MRN-based connectedness: A nonlinear approach for capturing systemic risk dynamics in financial systems","authors":"Shijia Song , Handong Li","doi":"10.1016/j.najef.2025.102572","DOIUrl":"10.1016/j.najef.2025.102572","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"83 ","pages":"Article 102572"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The onset of a “Trump 2.0” era is expected to usher a new wave of global economic uncertainty, exerting profound effects on shadow banking activities and impacting systemic risk. Motivated by these concerns, we examine the dynamic evolution of systemic risk across various interbank network structures to investigate how economic uncertainty and shadow banking interact to propagate and accumulate risk. Our findings reveal that heightened economic uncertainty significantly amplifies the systemic risk posed by shadow banking, while shadow banking activities, in turn, increase the banking system’s sensitivity to economic fluctuations. Under minimal to intermediate economic uncertainty, uniformly connected networks exhibit greater risk resilience. However, in a substantial economic uncertainty environment, network structures featuring core hubs demonstrates superior stability. Furthermore, the rise in economic uncertainty diminishes the positive influence of shadow banking on bank liquidity, profitability, and investment opportunities, markedly lowering the bank survival rate and increasing the need for central bank interventions. Asset loss stress tests further indicate that elevated economic uncertainty severely weakens the resilience of interbank networks, with intense shocks risking systemic dysfunction and collapse.
{"title":"Economic uncertainty, shadow banking, and systemic risk: A perspective of interbank network structure analysis","authors":"Hongjie Pan, Zhaojie Wang, Hejie Zhang, Shusheng Ding","doi":"10.1016/j.najef.2026.102600","DOIUrl":"10.1016/j.najef.2026.102600","url":null,"abstract":"<div><div>The onset of a “Trump 2.0” era is expected to usher a new wave of global economic uncertainty, exerting profound effects on shadow banking activities and impacting systemic risk. Motivated by these concerns, we examine the dynamic evolution of systemic risk across various interbank network structures to investigate how economic uncertainty and shadow banking interact to propagate and accumulate risk. Our findings reveal that heightened economic uncertainty significantly amplifies the systemic risk posed by shadow banking, while shadow banking activities, in turn, increase the banking system’s sensitivity to economic fluctuations. Under minimal to intermediate economic uncertainty, uniformly connected networks exhibit greater risk resilience. However, in a substantial economic uncertainty environment, network structures featuring core hubs demonstrates superior stability. Furthermore, the rise in economic uncertainty diminishes the positive influence of shadow banking on bank liquidity, profitability, and investment opportunities, markedly lowering the bank survival rate and increasing the need for central bank interventions. Asset loss stress tests further indicate that elevated economic uncertainty severely weakens the resilience of interbank networks, with intense shocks risking systemic dysfunction and collapse.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"83 ","pages":"Article 102600"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-05DOI: 10.1016/j.najef.2026.102599
Li Cai , Jiachen Liu
This article examines the causal structure of international stock markets using causal discovery algorithms across a six-market system. Unlike methods that infer connections without an assumption of cause and effect, causal discovery methods strive to uncover genuine causal relationships directly from observational data. Our findings reveal significantly fewer causal links compared to previous studies. Notably, during recessions, information circulates so rapidly that its impact rarely extends beyond a single day. However, in other periods, information from the previous day continues to affect returns, positioning the U.S. stock market as a leading market. Leveraging the identified causal relationships, we backtest simple cross-border timing strategies that achieve significant improvements in both risk and return relative to buy-and-hold benchmarks. These findings point to a previously unexplored class of trading signals for cross-border market timing.
{"title":"Causal structure of international stock markets","authors":"Li Cai , Jiachen Liu","doi":"10.1016/j.najef.2026.102599","DOIUrl":"10.1016/j.najef.2026.102599","url":null,"abstract":"<div><div>This article examines the causal structure of international stock markets using causal discovery algorithms across a six-market system. Unlike methods that infer connections without an assumption of cause and effect, causal discovery methods strive to uncover genuine causal relationships directly from observational data. Our findings reveal significantly fewer causal links compared to previous studies. Notably, during recessions, information circulates so rapidly that its impact rarely extends beyond a single day. However, in other periods, information from the previous day continues to affect returns, positioning the U.S. stock market as a leading market. Leveraging the identified causal relationships, we backtest simple cross-border timing strategies that achieve significant improvements in both risk and return relative to buy-and-hold benchmarks. These findings point to a previously unexplored class of trading signals for cross-border market timing.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"83 ","pages":"Article 102599"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-03DOI: 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 -deformed logarithmic function derived from Tsallis entropy, we introduce the concept of -growth rate for stock market portfolios and extend it to the Varma–Tsallis entropic framework. Within this setting, we define the optimal -growth rate and derive the growth-optimal portfolio that maximizes terminal -wealth over -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.
{"title":"Entropy-Based portfolio optimization under Varma–Tsallis Statistics: Evidence from stock markets","authors":"Muhammad Sheraz , Mihăiță Drăgan , Vasile Preda","doi":"10.1016/j.najef.2026.102581","DOIUrl":"10.1016/j.najef.2026.102581","url":null,"abstract":"<div><div>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 <span><math><mrow><mo>(</mo><mi>a</mi><mo>,</mo><mi>b</mi><mo>)</mo></mrow></math></span>-deformed logarithmic function derived from Tsallis entropy, we introduce the concept of <span><math><mrow><mo>(</mo><mi>a</mi><mo>,</mo><mi>b</mi><mo>)</mo></mrow></math></span>-growth rate for stock market portfolios and extend it to the Varma–Tsallis entropic framework. Within this setting, we define the optimal <span><math><mrow><mo>(</mo><mi>a</mi><mo>,</mo><mi>b</mi><mo>)</mo></mrow></math></span>-growth rate and derive the growth-optimal portfolio that maximizes terminal <span><math><mrow><mo>(</mo><mi>a</mi><mo>,</mo><mi>b</mi><mo>)</mo></mrow></math></span>-wealth over <span><math><mi>n</mi></math></span>-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.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"83 ","pages":"Article 102581"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145898045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-20DOI: 10.1016/j.najef.2026.102604
Yawen Li , Yufei Xia , Huiyi Shi , Lingyun He , Yinguo Li
Financial mismatch (FM) remains a major challenge for firms, especially amid information asymmetry. The emergence of bank regulatory technology (RegTech) is reshaping regulation and risk management in banking. Utilizing a panel dataset of bank-firm matched loan-level data from 2014 to 2023, we employ double-debiased machine learning to provide empirical evidence that bank RegTech significantly reduces firms’ FM: one-standard-deviation increase in bank RegTech corresponds to at least a 2.29% reduction in the FM. This effect operates through three main channels: improved information transparency, eased financing constraints, and reduced managerial performance pressure. Investor attention amplifies the mitigating impact of bank RegTech on FM. The effects are heterogeneous, with more pronounced impacts observed among non-state-owned enterprises, high-tech firms, firms in less competitive industries, and firms with established bank-firm relationships. Results hold after rigorous robustness validation. Finally, we further demonstrate that reduced FM leads to lower operational risk and a decline in corporate financialization.
{"title":"Can bank regulatory technology alleviate financial mismatch? Causal evidence from double-debiased machine learning on bank-firm matched data","authors":"Yawen Li , Yufei Xia , Huiyi Shi , Lingyun He , Yinguo Li","doi":"10.1016/j.najef.2026.102604","DOIUrl":"10.1016/j.najef.2026.102604","url":null,"abstract":"<div><div>Financial mismatch (FM) remains a major challenge for firms, especially amid information asymmetry. The emergence of bank regulatory technology (RegTech) is reshaping regulation and risk management in banking. Utilizing a panel dataset of bank-firm matched loan-level data from 2014 to 2023, we employ double-debiased machine learning to provide empirical evidence that bank RegTech significantly reduces firms’ FM: one-standard-deviation increase in bank RegTech corresponds to at least a 2.29% reduction in the FM. This effect operates through three main channels: improved information transparency, eased financing constraints, and reduced managerial performance pressure. Investor attention amplifies the mitigating impact of bank RegTech on FM. The effects are heterogeneous, with more pronounced impacts observed among non-state-owned enterprises, high-tech firms, firms in less competitive industries, and firms with established bank-firm relationships. Results hold after rigorous robustness validation. Finally, we further demonstrate that reduced FM leads to lower operational risk and a decline in corporate financialization.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"83 ","pages":"Article 102604"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-03DOI: 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.
{"title":"Sustainability disclosure and bank liquidity risk: evidence from global banking sector","authors":"Jianjin Huang , Song-Lin(Sony) Hsieh , Jia Wang","doi":"10.1016/j.najef.2026.102582","DOIUrl":"10.1016/j.najef.2026.102582","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"83 ","pages":"Article 102582"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-20DOI: 10.1016/j.najef.2026.102609
Sanghoon Lim , Mijin Ha , Jongkyu Park , Ji-Hun Yoon , Hyojung Lee
This study systematically investigates the impact of the COVID-19 pandemic on the stock price structure of the KOSPI200, the core index of the South Korean stock market, and 10 key industrial sectors. As COVID-19 was not a single event but a gradual complex crisis, conventional event studies relying on ‘exogenous’ dates, such as the WHO declaration date, struggle to capture ‘endogenous’ structural changes in the market. To overcome this limitation, this study focuses on the South Korean market, which employed unique policy responses characterized by the ‘K-quarantine’ strategy. Utilizing daily data from 140 KOSPI200 firms from 2019 to 2024, the study proposes an analytical framework that combines Change-Point Detection (CPD) with the event study methodology. Specifically, endogenous change points, revealed directly by the data, were identified through the dual verification of the non-linear method Binary Segmentation (BS) and the linear method Pruned Exact Linear Time (PELT) algorithms. Setting these change points as events, the analysis of Cumulative Abnormal Return (CAR) and Abnormal Return (AR) confirmed the defensive nature of the Healthcare sector and the strong reaction of cyclical sectors, such as Industrials. By demonstrating that the CPD detection times precede actual policy announcements and official news (e.g., WHO declaration), this study empirically validates the usefulness of CPD as an early warning indicator during crises, offering practical implications for financial stability monitoring and policy formulation.
本研究系统地考察了新冠肺炎疫情对韩国股市核心指数KOSPI200指数和10个主要行业股价结构的影响。由于COVID-19不是一个单一事件，而是一个渐进的复杂危机，依赖于“外生”日期（如世卫组织宣布日期）的传统事件研究难以捕捉市场的“内生”结构变化。为了克服这一限制，本研究将重点放在韩国市场上，韩国采用了以“k -检疫”战略为特征的独特政策反应。利用2019年至2024年140家KOSPI200公司的日常数据，该研究提出了一个将变化点检测（CPD）与事件研究方法相结合的分析框架。具体而言，通过对非线性方法二值分割（BS）和线性方法Pruned Exact linear Time （PELT）算法的双重验证，识别出直接由数据揭示的内生变化点。将这些变化点设置为事件，对累积异常回报（CAR）和异常回报（AR）的分析证实了医疗保健行业的防御性质以及工业等周期性行业的强烈反应。通过证明CPD的检测时间早于实际的政策公告和官方新闻（如世卫组织声明），本研究从经验上验证了CPD作为危机期间预警指标的有效性，为金融稳定监测和政策制定提供了实际意义。
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