North American Journal of Economics and Finance最新文献

{"title":"Examining climate risk attention in stock markets: insights from quantile-on-quantile regression","authors":"Lili Zhao ,&nbsp;Yutong Lin ,&nbsp;Zhenhao Liu ,&nbsp;Guozheng Yang","doi":"10.1016/j.najef.2025.102544","DOIUrl":"10.1016/j.najef.2025.102544","url":null,"abstract":"<div><div>Climate change has profound effects on society and the global economy. This study investigates the impact of climate risk attention (CRA) on China’s overall and sectoral stock markets by constructing a CRA index and applying the Quantile-on-Quantile regression approach. We find asymmetric and heterogeneous effects of CRA on the overall stock market, with the strongest positive effects concentrated in the upper quantiles. The results also reveal a saturation point beyond which further increases in CRA exert diminishing influence. At the sectoral level, high CRA is positively associated with non-distressed market states in Public Utilities, Information Technology, Optional Consumption, Materials, and Industrials. By contrast, its significant effects appear only during extremely prosperous conditions in Real Estate and Source Energy. Both low and high CRA are positively linked to upside volatility in the Medical Care and Daily Consumption sectors. The Financials sector responds mainly on the downside, with reduced CRA showing a positive association. Our findings underscore the importance of integrating climate risk considerations into financial strategies to support sustainable market development.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102544"},"PeriodicalIF":3.9,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219715","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}

{"title":"Cybersecurity risk and firm growth: Empirical evidence based on text analysis","authors":"Gengxi Xu,&nbsp;Yugang Li,&nbsp;Shanshan Liu,&nbsp;Zhuhong Ye","doi":"10.1016/j.najef.2025.102542","DOIUrl":"10.1016/j.najef.2025.102542","url":null,"abstract":"<div><div>Despite cybersecurity risk emerging as a critical firm threat, research on effective prevention and response strategies remains limited. Using a sample of A-share listed companies in Shanghai and Shenzhen from 2010 to 2022, this study adopts text analysis to construct indicators that portray the cybersecurity risk of Chinese listed companies and systematically examines the impact of cybersecurity risk on firm growth. The findings reveal that cybersecurity risk significantly inhibits firm growth. Mechanism analysis indicates that cybersecurity risk adversely impacts growth by increasing firms’ excessive cash holdings, amplifying operational risks, and exacerbating financing constraints. Further analysis shows that the growth-inhibiting effect is more pronounced among firms in technology-intensive industries, larger scale, higher media attention, and higher analyst attention. This study provides empirical evidence to guide firms in developing preemptive cybersecurity strategies, supports regulators in implementing differentiated governance, and helps governments refine cybersecurity incentives. These measures help firms strike a balance between growth and risk while supporting effective cybersecurity governance.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102542"},"PeriodicalIF":3.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219630","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}

{"title":"Early warning systems for cryptocurrency markets: Predicting ‘zombie’ assets using machine learning","authors":"Barbara Będowska-Sójka ,&nbsp;Piotr Wójcik ,&nbsp;Daniel Traian Pele","doi":"10.1016/j.najef.2025.102543","DOIUrl":"10.1016/j.najef.2025.102543","url":null,"abstract":"<div><div>The cryptocurrency market harbours a hidden risk: assets that silently disappear from trading, leaving investors stranded. These ‘zombie’ cryptocurrencies technically exist but become temporarily untradable on exchanges, ranging from weeks to permanent delisting. This study predicts which cryptocurrencies are at risk of becoming zombies using predictors derived from return statistics, trading volume, market capitalisation, and asset-specific features. Our sample includes cryptocurrencies listed for at least 210 days between January 2015 and December 2022. We employ various machine learning algorithms and novel explainable AI (XAI) tools, including permutation-based feature importance and partial dependence plots (PDPs), to identify and analyse key factors influencing zombification risk. Our machine learning models achieve 84% out-of-time balanced accuracy in predicting whether a cryptocurrency will become a zombie within the next 28 days. Tree-based approaches, particularly random forests, significantly outperform traditional econometric methods. Trading volumes and past returns emerge as the most influential predictors.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102543"},"PeriodicalIF":3.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266567","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}

{"title":"International main precious metals futures price forecasting based on decomposition-combinatorial time series model","authors":"Zihan Zhang ,&nbsp;Xiaojuan Dong ,&nbsp;Haigang An ,&nbsp;Hai Qi ,&nbsp;Sufang An ,&nbsp;Zhiliang Dong","doi":"10.1016/j.najef.2025.102541","DOIUrl":"10.1016/j.najef.2025.102541","url":null,"abstract":"<div><div>In the complex and volatile macroeconomic environment, precious metals play an important role in investment risk management because of their value preservation, value-added, and hedging functions. If investors can effectively predict price fluctuations in the precious metals market and thus optimize their investment portfolio strategies in time, they may be able to avoid market risks. In this paper, the futures prices of three international precious metals on the New York Mercantile Exchange of the Wind Database from 2014 to 2024 are taken as examples. First of all, the time-varying characteristics of non-pervasive, non-Gaussian, aging and delay are obtained for precious metals. Then the trend term, seasonal term, and residual term of the price series are modeled with the Autoregressive Integrated Moving Average (ARIMA) model, the Exponen Tial Smoothing (ETS) model, and the Long-Short Term Memory (LSTM) model, respectively, and the results are summarized to form a forecast of the futures prices of precious metals for the next 100 days. The results show that the error of the combination model for the three precious metal price predictions is less than 0.03, and the model fit is more than 0.98, indicating that the decomposition-combination model is suitable for predicting the precious metal futures prices. According to the results of the study, gold and silver have investment value in a short period, while the investment value of platinum is not obvious. Corresponding investment advice for investors is also given.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102541"},"PeriodicalIF":3.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158223","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}

{"title":"Investigating the impact of the Covid-19 pandemic on stock markets volatility in USA and Europe","authors":"Mohamed Chikhi ,&nbsp;François Benhmad","doi":"10.1016/j.najef.2025.102540","DOIUrl":"10.1016/j.najef.2025.102540","url":null,"abstract":"<div><div>Financial data exhibit distinctive characteristics known as stylized facts including volatility clustering, long memory, the leverage effect, and risk premium.</div><div>In this paper, we introduce a innovative volatility model (ARFIMA-HYAPGARCH-M) designed to effectively capture these features in both the S&amp;P 500 and the European STOXX600 indices, before and during the Covid-19 pandemic.</div><div>Empirical findings reveal a significant surge in return volatility across both U.S. and European stock markets during the pandemic. Moreover, the data exhibit dual long memory properties in both the mean and variance of returns, along with an evidence of asymmetry and the leverage effect. Furthermore, the results show that risk premiums increased during the Covid period, confirming that investors demand higher compensation during periods of “bad” volatility compared to periods of “good” volatility.</div><div>As such, the ARFIMA-HYAPGARCH-M volatility model provides a valuable tool for improved risk assessment, enabling investors and portfolio managers to make more informed decisions. Additionally, the model can enhance the performance of hedging strategies by accurately capturing volatility dynamics.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102540"},"PeriodicalIF":3.9,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109387","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}

{"title":"Enhancing financial stability through prospective resilience: Insights from the EN-VAR-DY-PR framework in international stock market networks","authors":"Jiang-Cheng Li ,&nbsp;Yi-Zhen Xu ,&nbsp;Chen Tao ,&nbsp;Guang-Yan Zhong","doi":"10.1016/j.najef.2025.102539","DOIUrl":"10.1016/j.najef.2025.102539","url":null,"abstract":"<div><div>The increasing interconnectedness and systemic vulnerabilities of financial networks underscore the necessity of enhancing their resilience to shocks and ensuring the stability of the global financial system. This paper proposes the EN-VAR-DY-PR framework, which integrates Elastic Net (EN) regularization, Vector Autoregression (VAR), and the Diebold–Yilmaz (DY) index. This novel approach enables the dynamic assessment of prospective resilience (PR) in complex financial networks, capturing both temporal and structural dimensions of risk. Focusing on three scenarios – economic blockade, financial liberalization, and random behavior – this research examines the dynamic evolution of network prospective resilience across three distinct periods marked by major market crises. Empirical analysis of 40 countries reveals that while economic blockade temporarily enhances network resilience, it undermines long-term shock absorption. Conversely, financial liberalization consistently improves network stability, and an optimal level of randomness significantly improve the resilience of financial networks and strengthen overall financial stability. Additionally, over the three periods, the clustering of the network decreases and the network becomes more homogeneous, suggesting heightened risk concentration and intensified interconnectedness. The significant growth in both the prospective resilience and volatility of network modularity underscores an escalating systemic vulnerability and a weakening of overall network stability. This study provides a novel perspective on financial stability, demonstrating how network science can effectively identify systemic vulnerabilities and inform strategies to mitigate systemic risks.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102539"},"PeriodicalIF":3.9,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109386","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}

{"title":"Spillover and return connectedness between uncertainties, digital assets, green bond, green and traditional energy markets: Evidence from quantile VAR","authors":"Rana Muhammad Nasir ,&nbsp;Feng He ,&nbsp;Mehrad Asadi ,&nbsp;David Roubaud","doi":"10.1016/j.najef.2025.102538","DOIUrl":"10.1016/j.najef.2025.102538","url":null,"abstract":"<div><div>This study investigates the extreme connectedness and spillover transmission between cryptocurrencies, digital assets, green bonds, traditional and green energy markets against different uncertainties from July 2, 2018, to February 3, 2023. First, we employ Quantile VAR to unveil extreme connectedness among markets. Further, Baruník and Křehlík (BK) framework is used to understand time frequency spillover transmission across our chosen markets. Our results indicate that spillover magnitude under bullish market conditions is higher than normal and bearish market conditions. In addition, the equity market volatility, geopolitical risk, Twitter-based economic risk, and oil markets are the major receiver of spillover across all market conditions. In contrast, NFTs and Defis are the significant transmitters of spillover across all quantiles. Similarly, natural gas and green bonds act as spillover transmitters under extreme quantiles. While, green energy and cryptocurrencies are net transmitters in only bearish market conditions. Based on these findings, this study proposed several important implications for investors, financial markets participants, portfolio managers and market regulators in terms of diversifying their risk and design effective market regulation policies.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102538"},"PeriodicalIF":3.9,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095580","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}

{"title":"Cryptocurrencies and economic sanctions","authors":"José Almeida ,&nbsp;Tiago Cruz Gonçalves","doi":"10.1016/j.najef.2025.102537","DOIUrl":"10.1016/j.najef.2025.102537","url":null,"abstract":"<div><div>This study examines the role of cryptocurrencies in modern War, specifically during the Russia-Ukraine conflict. Utilizing a Time-Varying Parameter Vector Autoregression (TVP-VAR) model, the research assesses the dynamic financial behaviors of cryptocurrencies, focusing on changes in liquidity, safe haven status, and their use in circumventing economic sanctions. The analysis distinguishes financial behaviors across three distinct phases: Pre-Conflict, Conflict, and financial sanctions periods, highlighting the interaction between cryptocurrencies and traditional financial markets. The findings indicate shifts in the role of cryptocurrencies from net transmitters to net receivers of spillovers in both returns and volatility, particularly during the financial sanctions phase. This study provides insights into the integration of cryptocurrencies with traditional financial assets and their potential impact on local economies during military conflicts. The results document the increased liquidity and interconnectedness of cryptocurrencies during military conflict periods and explore their potential use in evading sanctions and supporting War efforts.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102537"},"PeriodicalIF":3.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010148","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}

{"title":"Stock market vulnerability to US monetary policy: Evidenced from quantile coherency analysis","authors":"Sangram Keshari Jena ,&nbsp;Amine Lahiani ,&nbsp;Ashutosh Dash ,&nbsp;Sougata Ray","doi":"10.1016/j.najef.2025.102536","DOIUrl":"10.1016/j.najef.2025.102536","url":null,"abstract":"<div><div>Turkey, Brazil, India, South Africa, and Indonesia are referred as the fragile five countries in 2013. Since then, however, the macro-economic environment of those countries has improved a lot. The objective of the study is to investigate whether the stock market of those countries is still vulnerable to US monetary policy using a novel quantile coherency methodology. The vulnerability is based on the general dependency structure at the quantile of joint distribution across frequencies. Besides, the pre and post 2013 dependency is compared to examine the effectiveness of macro-economic factors in controlling the impacts of the US monetary policy. Positive and negative dependencies were observed during conventional and unconventional quantitative easing and tightening respectively. Largely, it persists in the long-to-medium term across the state of the market. Domestic macroeconomic fundamentals seem to be relatively less effective in controlling the impact of US monetary policy. Thus, additional institutional reforms are required to make these markets resilient to global monetary policy shocks.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102536"},"PeriodicalIF":3.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996613","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}

{"title":"Dynamic q-dependent cross-correlation test for investment classification and its application on green finance","authors":"Turker Acikgoz","doi":"10.1016/j.najef.2025.102535","DOIUrl":"10.1016/j.najef.2025.102535","url":null,"abstract":"<div><div>This study develops a novel methodological framework, the dynamic <span><math><mi>q</mi></math></span>-dependent cross-correlation (DQCC) test, to evaluate the diversification, hedging, and safe-haven properties of financial assets under conditions of fractality and nonlinear dependence. Traditional econometric approaches often fail to capture three critical aspects of financial markets: time-varying structures, heterogeneous investment horizons, and fluctuation-dependent dynamics. To address these limitations, the proposed framework integrates fractal theory and econophysics-based cross-correlation measures, enabling a simultaneous analysis of time, scale, and moment dimensions. Empirically, the model is applied to the nexus between green bonds and equity markets. Methodologically, both quantile-based and event-based specifications are employed to assess asset behavior under normal conditions and during systemic crises, including the COVID-19 pandemic, the Russia–Ukraine war, and the Hamas–Israel conflict. The results reveal strong evidence of multifractality and nonlinear dynamics across all return series, rejecting market efficiency. Green bonds are found to provide persistent diversification benefits against both advanced and emerging market equities under ordinary conditions, while their safe-haven properties emerge during extreme downturns, particularly at lower quantiles and longer horizons. Event-based results confirm the safe-haven role of green bonds during COVID-19, with more mixed evidence during geopolitical crises. No robust hedging capacity is observed. The study contributes to the literature by introducing a comprehensive testing framework for investment classification under fractal dynamics and by extending the understanding of the green bond–equity nexus across advanced and emerging markets. The findings carry significant implications for portfolio construction, risk management, and sustainable finance, and the model can be applied to other asset classes to evaluate their potential roles as diversifiers, hedges, or safe-haven.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102535"},"PeriodicalIF":3.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004430","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}