Journal of International Financial Markets Institutions & Money最新文献_第4页

Energy market deregulation: A new perspective on dividend smoothing 能源市场放松管制：股利平滑的新视角

IF 6.1 2区经济学

Journal of International Financial Markets Institutions & Money Pub Date : 2026-01-01 Epub Date: 2025-11-18 DOI: 10.1016/j.intfin.2025.102260

Faruk Balli , Hatice Ozer Balli , Indrit Hoxha , Hannah Nguyen , Tam Hoang Nhat Dang

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引用次数: 0

Controlling shareholders Abroad: How overseas residency rights influence the costs of corporate Leverage? 海外控股股东：海外居留权如何影响企业杠杆成本？

IF 6.1 2区经济学

Journal of International Financial Markets Institutions & Money Pub Date : 2026-01-01 Epub Date: 2025-11-23 DOI: 10.1016/j.intfin.2025.102262

Qian Li , Shihao Wang , Victor Song

{"title":"Controlling shareholders Abroad: How overseas residency rights influence the costs of corporate Leverage?","authors":"Qian Li , Shihao Wang , Victor Song","doi":"10.1016/j.intfin.2025.102262","DOIUrl":"10.1016/j.intfin.2025.102262","url":null,"abstract":"<div><div>This study examines how controlling shareholders’ overseas residency rights influence the costs of high leverage in China’s A-share market from 2007 to 2021. We find that privately owned firms with controllers holding overseas residency rights incur significantly higher costs of high leverage. The results are robust to alternative specifications, including two-stage least squares, propensity score matching, and alternative measures of both high leverage and residency rights. Mechanism analyses show that overseas residency rights exacerbate stakeholder-driven pressures—particularly from customers, competitors, employees, and suppliers—facing highly leveraged firms. At the same time, this adverse effect is mitigated by formal institutional mechanisms, such as extradition treaties and strong investor protections in the controller’s country of residence, as well as by informal institutions, including corporate integrity culture and political connections. Finally, we show that overseas residency rights amplify the negative impact of high leverage on firms’ investment and profitability.</div></div>","PeriodicalId":48119,"journal":{"name":"Journal of International Financial Markets Institutions & Money","volume":"106 ","pages":"Article 102262"},"PeriodicalIF":6.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145615320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bank competition and corporate tax avoidance: the Chinese experience 银行竞争与企业避税：中国的经验

IF 6.1 2区经济学

Journal of International Financial Markets Institutions & Money Pub Date : 2026-01-01 Epub Date: 2025-11-12 DOI: 10.1016/j.intfin.2025.102248

Yekun Xu , Jiayu Zhao , Ruohan Zhong , Qiang Zhang

引用次数: 0

How are retail investors informed? A perspective from institutional trading intention exposure 如何通知散户投资者？机构交易意向敞口的视角

IF 6.1 2区经济学

Journal of International Financial Markets Institutions & Money Pub Date : 2026-01-01 Epub Date: 2025-11-20 DOI: 10.1016/j.intfin.2025.102259

Xuchu Sun , Qing Zhang , Tangrong Li

引用次数: 0

Bitcoin bans & regulatory segmentation in digitally native asset markets 数字原生资产市场中的比特币禁令和监管细分

IF 6.1 2区经济学

Journal of International Financial Markets Institutions & Money Pub Date : 2026-01-01 Epub Date: 2025-11-19 DOI: 10.1016/j.intfin.2025.102261

Henrik Seikku , Imtiaz Sifat

{"title":"Bitcoin bans & regulatory segmentation in digitally native asset markets","authors":"Henrik Seikku , Imtiaz Sifat","doi":"10.1016/j.intfin.2025.102261","DOIUrl":"10.1016/j.intfin.2025.102261","url":null,"abstract":"<div><div>We examine whether regulatory barriers segment technologically integrated financial markets using Bitcoin bans across 19 countries (2013–2024). Unlike traditional markets where infrastructure creates natural frictions, Bitcoin operates on a global, permissionless network. Using proper maximum likelihood BEKK-GARCH and CCC-GARCH estimation, we find no significant differences in market integration between liberal and conservative regulatory regimes across four complementary tests. However, country-specific analysis reveals substantial heterogeneity: major markets (China, Russia) achieve partial segmentation while smaller markets show counter-intuitive increases in integration post-ban. Market size and development explain less than 10% of regulatory effectiveness variation. These findings challenge traditional segmentation theory, demonstrating that decentralized technology creates persistent cross-market linkages that transcend regulatory boundaries. The heterogeneous and often counterproductive regulatory outcomes suggest policymakers should prioritize international coordination over unilateral restrictions when addressing digitally native assets.</div></div>","PeriodicalId":48119,"journal":{"name":"Journal of International Financial Markets Institutions & Money","volume":"106 ","pages":"Article 102261"},"PeriodicalIF":6.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning, memory and efficiency in cryptocurrency markets 加密货币市场中的机器学习、内存和效率

IF 6.1 2区经济学

Journal of International Financial Markets Institutions & Money Pub Date : 2025-12-01 Epub Date: 2025-10-06 DOI: 10.1016/j.intfin.2025.102210

Shuyue Li, Larisa Yarovaya, Tapas Mishra

{"title":"Machine learning, memory and efficiency in cryptocurrency markets","authors":"Shuyue Li, Larisa Yarovaya, Tapas Mishra","doi":"10.1016/j.intfin.2025.102210","DOIUrl":"10.1016/j.intfin.2025.102210","url":null,"abstract":"<div><div>This paper empirically examines whether machine learning (ML) methods can capture long memory in the cryptocurrency markets. We design two tests to evaluate seven widely used ML regression algorithms and sequence-to-sequence (Seq2Seq) models to determine their ability to capture long-memory characteristics of financial data. Specifically, we assess their accuracy in estimating the fractional integration parameter <span><math><mi>d</mi></math></span> for both univariate and systemic memory. Additionally, we examine whether the predicted time series preserve the long-memory properties of the original cryptocurrency market data. Our findings reveal that most ML algorithms fail to handle long-memory series effectively, while models incorporating Long Short-Term Memory (LSTM) and Attention-LSTM components exhibit superior performance. Whilst comparing models using Mean Squared Errors (MSE), we find that our tests identify models better for directional predictions. These results highlight the limitations of conventional ML mechanism for long-range dependence and position Seq2Seq models as a promising alternative for addressing the complex movements of cryptocurrency time series. Our approach can be readily extended, offering both academics and practitioners a systematic procedure for evaluating arbitrary ML models, thereby yielding insights not only into their generalization of performance but also into the interpretability of their capacity for long-term dependence.</div></div>","PeriodicalId":48119,"journal":{"name":"Journal of International Financial Markets Institutions & Money","volume":"105 ","pages":"Article 102210"},"PeriodicalIF":6.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Biodiversity risk and firms’ access to trade credit 生物多样性风险与企业获得贸易信贷的途径

IF 6.1 2区经济学

Journal of International Financial Markets Institutions & Money Pub Date : 2025-12-01 Epub Date: 2025-10-04 DOI: 10.1016/j.intfin.2025.102226

Khadija S. Almaghrabi , Walid Ben-Amar , Ziyu Kong

引用次数: 0

Terrorism, institutional environment, and corporate cash holdings 恐怖主义，制度环境，企业现金持有量

IF 6.1 2区经济学

Journal of International Financial Markets Institutions & Money Pub Date : 2025-12-01 Epub Date: 2025-10-04 DOI: 10.1016/j.intfin.2025.102227

Solomon Wise Dodzidenu Adza , Adelaide Dak-Adzaklo , Patrick Bimpong , James Edudzi Kudoh , Cephas Simon Peter Dak-Adzaklo

引用次数: 0

Cross-market overnight time-series momentum 跨市场隔夜时间序列动量

IF 6.1 2区经济学

Journal of International Financial Markets Institutions & Money Pub Date : 2025-12-01 Epub Date: 2025-10-21 DOI: 10.1016/j.intfin.2025.102239

Dezhong Xu , Bin Li , Tarlok Singh , Xiaoyue Chen , Jinze Li

引用次数: 0

Bank relationships and corporate exchange rate risk 银行关系和企业汇率风险

IF 6.1 2区经济学

Journal of International Financial Markets Institutions & Money Pub Date : 2025-12-01 Epub Date: 2025-10-02 DOI: 10.1016/j.intfin.2025.102228

Qianyi Hao, Jiajia Liu, Zhe Yang

引用次数: 0