Pub Date : 2025-12-13DOI: 10.1016/j.pacfin.2025.103040
Chuanhai Zhang , Zhongjie Zheng , Tao Bing
Due to global warming, extreme climate events have become increasingly frequent, posing new challenges to both the real economy and the financial system. In this paper, we construct both extreme temperature and precipitation risk indicators as two proxies for climate risk to examine their impact on the pricing of Chinese Chengtou bonds. The main findings are summarized as follows. First, both extreme temperature and precipitation risks significantly increase the issuance spreads of Chinese Chengtou bonds, and the main findings are supported by a series of robustness checks. Second, mechanism analyses reveal that extreme temperatures weaken the solvency of LGFVs and the implicit guarantees of local governments, while extreme precipitation has no such effect. Third, heterogeneity analysis reveals that climate risk has a greater impact on Chengtou bonds that receive more public attention and are issued by platforms at higher administrative levels, while no significant differences are observed across bond maturities.
{"title":"The impact of climate risk on municipal bonds pricing: Evidence from Chinese Chengtou bonds","authors":"Chuanhai Zhang , Zhongjie Zheng , Tao Bing","doi":"10.1016/j.pacfin.2025.103040","DOIUrl":"10.1016/j.pacfin.2025.103040","url":null,"abstract":"<div><div>Due to global warming, extreme climate events have become increasingly frequent, posing new challenges to both the real economy and the financial system. In this paper, we construct both extreme temperature and precipitation risk indicators as two proxies for climate risk to examine their impact on the pricing of Chinese Chengtou bonds. The main findings are summarized as follows. <em>First</em>, both extreme temperature and precipitation risks significantly increase the issuance spreads of Chinese Chengtou bonds, and the main findings are supported by a series of robustness checks. <em>Second</em>, mechanism analyses reveal that extreme temperatures weaken the solvency of LGFVs and the implicit guarantees of local governments, while extreme precipitation has no such effect. <em>Third</em>, heterogeneity analysis reveals that climate risk has a greater impact on Chengtou bonds that receive more public attention and are issued by platforms at higher administrative levels, while no significant differences are observed across bond maturities.</div></div>","PeriodicalId":48074,"journal":{"name":"Pacific-Basin Finance Journal","volume":"96 ","pages":"Article 103040"},"PeriodicalIF":5.3,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790907","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}
Pub Date : 2025-12-12DOI: 10.1016/j.pacfin.2025.103036
Ya Bu , Ningxian Jin , Hui Li
This study examines how artificial intelligence (AI) affects the financing efficiency of SMEs in China from 2007 to 2023. Using a novel AI adoption index derived from annual report text analysis and a comprehensive financing efficiency measure, we find that AI significantly improves SME financing efficiency by reducing costs and risks and enhancing returns. The effects operate through alleviating information asymmetry, easing financing constraints, and promoting innovation. The impact is more pronounced for non-state-owned firms, technology-intensive industries, and those in eastern China. These findings offer insights for digital transformation in emerging market SMEs.
{"title":"How does artificial intelligence affect the financing efficiency of small and medium-sized enterprises (SMEs)?","authors":"Ya Bu , Ningxian Jin , Hui Li","doi":"10.1016/j.pacfin.2025.103036","DOIUrl":"10.1016/j.pacfin.2025.103036","url":null,"abstract":"<div><div>This study examines how artificial intelligence (AI) affects the financing efficiency of SMEs in China from 2007 to 2023. Using a novel AI adoption index derived from annual report text analysis and a comprehensive financing efficiency measure, we find that AI significantly improves SME financing efficiency by reducing costs and risks and enhancing returns. The effects operate through alleviating information asymmetry, easing financing constraints, and promoting innovation. The impact is more pronounced for non-state-owned firms, technology-intensive industries, and those in eastern China. These findings offer insights for digital transformation in emerging market SMEs.</div></div>","PeriodicalId":48074,"journal":{"name":"Pacific-Basin Finance Journal","volume":"96 ","pages":"Article 103036"},"PeriodicalIF":5.3,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790905","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}
Pub Date : 2025-12-12DOI: 10.1016/j.pacfin.2025.103038
Pengshi Li , Jinbo Huang , Yan Lin
We propose an interpretable hybrid machine learning framework for forecasting and explaining implied volatility surface dynamics of CSI 300 index options. Our methodology leverages machine learning to correct a theory-based baseline model. Initial predictions are derived from an analytical model, while the second stage involves a machine learning model trained on the residuals of the first stage. We construct three variants of hybrid models using XGBoost: a baseline three-feature model, a VIX-augmented four-feature model, and a five-feature model incorporating a newly developed options-implied ambiguity index. Empirical results using 2019–2025 CSI 300 options data show that the five-feature model significantly outperforms both the analytical benchmark and VIX-only model. Performance improvements are especially pronounced in market rallies and high-ambiguity regimes, where ambiguity attenuates implied volatility compression and amplifies perceptions of downside risk. We further use SHAP value analysis to demonstrate that feature effects are economically coherent and state-dependent. Our findings confirm that ambiguity is a distinct and quantitatively meaningful risk factor for explaining implied volatility dynamics in emerging market.
{"title":"Learn to explain the smile: An interpretable hybrid machine learning model to understand the implied volatility of CSI 300 options","authors":"Pengshi Li , Jinbo Huang , Yan Lin","doi":"10.1016/j.pacfin.2025.103038","DOIUrl":"10.1016/j.pacfin.2025.103038","url":null,"abstract":"<div><div>We propose an interpretable hybrid machine learning framework for forecasting and explaining implied volatility surface dynamics of CSI 300 index options. Our methodology leverages machine learning to correct a theory-based baseline model. Initial predictions are derived from an analytical model, while the second stage involves a machine learning model trained on the residuals of the first stage. We construct three variants of hybrid models using XGBoost: a baseline three-feature model, a VIX-augmented four-feature model, and a five-feature model incorporating a newly developed options-implied ambiguity index. Empirical results using 2019–2025 CSI 300 options data show that the five-feature model significantly outperforms both the analytical benchmark and VIX-only model. Performance improvements are especially pronounced in market rallies and high-ambiguity regimes, where ambiguity attenuates implied volatility compression and amplifies perceptions of downside risk. We further use SHAP value analysis to demonstrate that feature effects are economically coherent and state-dependent. Our findings confirm that ambiguity is a distinct and quantitatively meaningful risk factor for explaining implied volatility dynamics in emerging market.</div></div>","PeriodicalId":48074,"journal":{"name":"Pacific-Basin Finance Journal","volume":"96 ","pages":"Article 103038"},"PeriodicalIF":5.3,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790906","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}
Pub Date : 2025-12-12DOI: 10.1016/j.pacfin.2025.103039
Xiao Bai , Wenyao Zhao , Meng Liu
This pre-registered study executes the empirical design approved in the associated pre-registered report (Bai and Zhao, 2025) to investigate the impact of artificial intelligence (AI) investment on stock price crash risk. Using China's “New-generation Artificial Intelligence Polit Zone Policy” as a quasi-natural experiment, we find robust evidence that AI investment significantly increases firms' stock price crash risk, mainly due to reduced information transparency and heightened managerial optimism. The effect is more pronounced for firms with lower levels of information transparency and tighter resource constraints. Furthermore, we also find the policy boosts firm value, suggesting that market optimism may drive short-term valuation gains at the cost of long-term stability. Overall, our findings highlight the unintended downside risks associated with AI investment, emphasizing the importance of transparency and governance in mitigating potential adverse outcomes.
本预注册研究执行了相关预注册报告（Bai and Zhao, 2025）中批准的实证设计，以调查人工智能（AI）投资对股价崩盘风险的影响。利用中国的“新一代人工智能专区政策”作为准自然实验，我们发现强有力的证据表明，人工智能投资显著增加了公司股价崩溃的风险，主要原因是信息透明度降低和管理层乐观情绪增强。对于信息透明度较低、资源约束较紧的企业，这种影响更为明显。此外，我们还发现政策提升了公司价值，这表明市场乐观情绪可能以长期稳定为代价推动短期估值收益。总体而言，我们的研究结果突出了与人工智能投资相关的意外下行风险，强调了透明度和治理在减轻潜在不利后果方面的重要性。
{"title":"Artificial intelligence and stock price crash risk: Evidence from China: A pre-registered study","authors":"Xiao Bai , Wenyao Zhao , Meng Liu","doi":"10.1016/j.pacfin.2025.103039","DOIUrl":"10.1016/j.pacfin.2025.103039","url":null,"abstract":"<div><div>This pre-registered study executes the empirical design approved in the associated pre-registered report (<span><span>Bai and Zhao, 2025</span></span>) to investigate the impact of artificial intelligence (AI) investment on stock price crash risk. Using China's “New-generation Artificial Intelligence Polit Zone Policy” as a quasi-natural experiment, we find robust evidence that AI investment significantly increases firms' stock price crash risk, mainly due to reduced information transparency and heightened managerial optimism. The effect is more pronounced for firms with lower levels of information transparency and tighter resource constraints. Furthermore, we also find the policy boosts firm value, suggesting that market optimism may drive short-term valuation gains at the cost of long-term stability. Overall, our findings highlight the unintended downside risks associated with AI investment, emphasizing the importance of transparency and governance in mitigating potential adverse outcomes.</div></div>","PeriodicalId":48074,"journal":{"name":"Pacific-Basin Finance Journal","volume":"96 ","pages":"Article 103039"},"PeriodicalIF":5.3,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790894","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}
Pub Date : 2025-12-12DOI: 10.1016/j.pacfin.2025.103037
Xiao Li, Xirou Yang
We construct deviation salience (DS) to capture the relative return deviation of a stock from its industry peers. We find strong return reversals among high-salience stocks, with no evidence of momentum. These results are robust to alternative specifications and firm-level controls. Cross-sectional tests suggest that salience amplifies the market's reaction to bad news more than to good news. Mechanism analyses indicate that heightened salience induces greater investor attention and overreaction, leading to short-term price reversals.
{"title":"Salience and return reversals: Evidence from China","authors":"Xiao Li, Xirou Yang","doi":"10.1016/j.pacfin.2025.103037","DOIUrl":"10.1016/j.pacfin.2025.103037","url":null,"abstract":"<div><div>We construct deviation salience (DS) to capture the relative return deviation of a stock from its industry peers. We find strong return reversals among high-salience stocks, with no evidence of momentum. These results are robust to alternative specifications and firm-level controls. Cross-sectional tests suggest that salience amplifies the market's reaction to bad news more than to good news. Mechanism analyses indicate that heightened salience induces greater investor attention and overreaction, leading to short-term price reversals.</div></div>","PeriodicalId":48074,"journal":{"name":"Pacific-Basin Finance Journal","volume":"96 ","pages":"Article 103037"},"PeriodicalIF":5.3,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791021","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}
Pub Date : 2025-12-11DOI: 10.1016/j.pacfin.2025.103032
Withz Aimable
This study examines cryptocurrency valuation using a cross-sectional portfolio approach that integrates equity, commodity, and foreign-exchange market interactions with financial, macroeconomic, and policy uncertainty factors. Portfolios constructed from daily and monthly data are evaluated using returns and Sharpe ratios, while factor sensitivities are estimated through a two-pass regression framework combined with Fama–MacBeth procedures that incorporate both traditional market factors and macro-financial uncertainty. Using a sample of 250 cryptocurrencies from 2014 to 2024, the results show that financial, macroeconomic, and policy uncertainty are consistently priced sources of risk that generate strong and robust premia. These effects remain significant after controlling for equity, commodity, and foreign-exchange exposures, indicating that cryptocurrencies behave as speculative but highly risk-sensitive assets whose valuations depend more on broad uncertainty conditions than on equity-market dynamics. Methodological enhancements, including Shanken corrections, frequency alignment, and crypto-specific controls such as liquidity and mining difficulty, reinforce the robustness of the findings. Overall, the study extends asset-pricing research by demonstrating that uncertainty factors widely used in traditional markets also explain the cross-section of cryptocurrency returns, underscoring the need for investors to incorporate uncertainty risk in allocation decisions and highlighting the sensitivity of crypto markets to macro-financial conditions.
{"title":"Disentangling market and uncertainty effects in crypto valuation: A portfolio-based analysis","authors":"Withz Aimable","doi":"10.1016/j.pacfin.2025.103032","DOIUrl":"10.1016/j.pacfin.2025.103032","url":null,"abstract":"<div><div>This study examines cryptocurrency valuation using a cross-sectional portfolio approach that integrates equity, commodity, and foreign-exchange market interactions with financial, macroeconomic, and policy uncertainty factors. Portfolios constructed from daily and monthly data are evaluated using returns and Sharpe ratios, while factor sensitivities are estimated through a two-pass regression framework combined with Fama–MacBeth procedures that incorporate both traditional market factors and macro-financial uncertainty. Using a sample of 250 cryptocurrencies from 2014 to 2024, the results show that financial, macroeconomic, and policy uncertainty are consistently priced sources of risk that generate strong and robust premia. These effects remain significant after controlling for equity, commodity, and foreign-exchange exposures, indicating that cryptocurrencies behave as speculative but highly risk-sensitive assets whose valuations depend more on broad uncertainty conditions than on equity-market dynamics. Methodological enhancements, including Shanken corrections, frequency alignment, and crypto-specific controls such as liquidity and mining difficulty, reinforce the robustness of the findings. Overall, the study extends asset-pricing research by demonstrating that uncertainty factors widely used in traditional markets also explain the cross-section of cryptocurrency returns, underscoring the need for investors to incorporate uncertainty risk in allocation decisions and highlighting the sensitivity of crypto markets to macro-financial conditions.</div></div>","PeriodicalId":48074,"journal":{"name":"Pacific-Basin Finance Journal","volume":"96 ","pages":"Article 103032"},"PeriodicalIF":5.3,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790896","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}
Pub Date : 2025-12-08DOI: 10.1016/j.pacfin.2025.103034
Xudong He , Shuang Yu , Zunxin Zheng
While the impact of general political connections on corporate behavior is well-documented, the role of formal, ideological training, such as that provided by Party Schools remains unexplored. This study examines the influence of CEOs with Party School education on the ESG performance of Chinese state-owned enterprises (SOEs) from 2010 to 2023. Our findings suggest that Party School education has a significant positive impact on corporate ESG performance. This effect operates through multiple mechanisms: alignment with government objectives, enhanced responsiveness to local economic development pressure, incentives for political promotion, and improved access to government subsidies. The results contribute to a broader understanding of politically connected leadership in transition economies and offers policy implications for optimizing the selection and training mechanisms of SOE leaders in Asia-Pacific areas.
{"title":"CEO Party School education and ESG performance: Evidence from China","authors":"Xudong He , Shuang Yu , Zunxin Zheng","doi":"10.1016/j.pacfin.2025.103034","DOIUrl":"10.1016/j.pacfin.2025.103034","url":null,"abstract":"<div><div>While the impact of general political connections on corporate behavior is well-documented, the role of formal, ideological training, such as that provided by Party Schools remains unexplored. This study examines the influence of CEOs with Party School education on the ESG performance of Chinese state-owned enterprises (SOEs) from 2010 to 2023. Our findings suggest that Party School education has a significant positive impact on corporate ESG performance. This effect operates through multiple mechanisms: alignment with government objectives, enhanced responsiveness to local economic development pressure, incentives for political promotion, and improved access to government subsidies. The results contribute to a broader understanding of politically connected leadership in transition economies and offers policy implications for optimizing the selection and training mechanisms of SOE leaders in Asia-Pacific areas.</div></div>","PeriodicalId":48074,"journal":{"name":"Pacific-Basin Finance Journal","volume":"96 ","pages":"Article 103034"},"PeriodicalIF":5.3,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737601","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}
Pub Date : 2025-12-07DOI: 10.1016/j.pacfin.2025.103033
Xingyi Li , Zhuang Liu , Yujun Liu , Shushang Zhu , Jingzhou Yan
We investigate the predictability of cryptocurrency returns using a comprehensive set of macroeconomic and cryptocurrency-specific factors and a set of 12 machine learning models. To enhance interpretability, we employ SHAP analysis to quantify the marginal contribution of each factor to model outputs. We further assess the economic value of predictive signals by constructing long-short and long-only portfolios. Empirically, tree-based methods, particularly random forests, deliver the highest predictive accuracy and outperform neural network and linear benchmarks, with predictability substantially stronger than that documented in equity markets. Across models, the market-to-realized-value ratio, new addresses, and active addresses consistently emerge as the most influential predictors, with higher values associated with higher expected returns. Portfolio results show that neural network-based strategies achieve the highest cumulative performance, indicating meaningful investment gains. Overall, our findings demonstrate the value of machine learning for return forecasting in the cryptocurrency market and provide practical insights for investors and financial analysts operating in highly volatile and evolving cryptocurrency environments.
{"title":"Predicting cryptocurrency returns with machine learning: Evidence from high-dimensional factor modeling","authors":"Xingyi Li , Zhuang Liu , Yujun Liu , Shushang Zhu , Jingzhou Yan","doi":"10.1016/j.pacfin.2025.103033","DOIUrl":"10.1016/j.pacfin.2025.103033","url":null,"abstract":"<div><div>We investigate the predictability of cryptocurrency returns using a comprehensive set of macroeconomic and cryptocurrency-specific factors and a set of 12 machine learning models. To enhance interpretability, we employ SHAP analysis to quantify the marginal contribution of each factor to model outputs. We further assess the economic value of predictive signals by constructing long-short and long-only portfolios. Empirically, tree-based methods, particularly random forests, deliver the highest predictive accuracy and outperform neural network and linear benchmarks, with predictability substantially stronger than that documented in equity markets. Across models, the market-to-realized-value ratio, new addresses, and active addresses consistently emerge as the most influential predictors, with higher values associated with higher expected returns. Portfolio results show that neural network-based strategies achieve the highest cumulative performance, indicating meaningful investment gains. Overall, our findings demonstrate the value of machine learning for return forecasting in the cryptocurrency market and provide practical insights for investors and financial analysts operating in highly volatile and evolving cryptocurrency environments.</div></div>","PeriodicalId":48074,"journal":{"name":"Pacific-Basin Finance Journal","volume":"96 ","pages":"Article 103033"},"PeriodicalIF":5.3,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737600","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}
This study investigates the impact of state-owned capital on the innovation of private-owned enterprises (POEs) in China, differentiating between its direct role as a shareholder and its indirect role exerted through common ownership network. Using a sample of A-share listed manufacturing firms from 2014 to 2022, this paper finds that state-owned capital significantly promotes POEs innovation. Specifically, the indirect role enhances both innovation input (R&D investment) and output (total patents and invention patents), whereas the direct role primarily fosters high-quality innovation output (invention patents). Further mechanism analyses reveal that state-owned capital facilitates innovation through resource effect (capital, talent, and knowledge) and governance effect. The indirect role operates mainly through capital and talent resource effects, coupled with the governance effect of alleviating managerial myopia. In contrast, the direct role functions through talent and knowledge resource effects, along with the governance effect of managerial incentives. This paper provides novel insights into the multifaceted influence of state-owned capital on POEs innovation and highlights a critical role of state-owned capital in fostering innovation under common ownership network.
{"title":"The role of state-owned capital in the innovation of private-owned enterprises: Evidence from China","authors":"Haiyan Xue , Haijuan Zhang , Xindong Zhang , Shusheng Ding","doi":"10.1016/j.pacfin.2025.103031","DOIUrl":"10.1016/j.pacfin.2025.103031","url":null,"abstract":"<div><div>This study investigates the impact of state-owned capital on the innovation of private-owned enterprises (POEs) in China, differentiating between its direct role as a shareholder and its indirect role exerted through common ownership network. Using a sample of A-share listed manufacturing firms from 2014 to 2022, this paper finds that state-owned capital significantly promotes POEs innovation. Specifically, the indirect role enhances both innovation input (R&D investment) and output (total patents and invention patents), whereas the direct role primarily fosters high-quality innovation output (invention patents). Further mechanism analyses reveal that state-owned capital facilitates innovation through resource effect (capital, talent, and knowledge) and governance effect. The indirect role operates mainly through capital and talent resource effects, coupled with the governance effect of alleviating managerial myopia. In contrast, the direct role functions through talent and knowledge resource effects, along with the governance effect of managerial incentives. This paper provides novel insights into the multifaceted influence of state-owned capital on POEs innovation and highlights a critical role of state-owned capital in fostering innovation under common ownership network.</div></div>","PeriodicalId":48074,"journal":{"name":"Pacific-Basin Finance Journal","volume":"96 ","pages":"Article 103031"},"PeriodicalIF":5.3,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737603","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}
Pub Date : 2025-12-04DOI: 10.1016/j.pacfin.2025.103030
Honglin Ren , Anqi Jiao
This pre-registered study explores the impact of intergenerational succession on the international strategy of family businesses. Conceptualizing succession as a critical inflection point in corporate governance, we examine whether leadership transition to a next-generation family member influences a firm's likelihood to initiate or expand international operations. Drawing on agency theory, socioemotional wealth, and dynamic capabilities, we propose that successor characteristics—such as foreign education or work experience—affect global strategic orientation. Using a quasi-natural experimental design, we will employ a difference-in-differences approach to evaluate how succession events affect internationalization outcomes, while also investigating the moderating roles of founder retention and governance professionalization. This study aims to provide theoretical insights into strategic transformation in family firms and generate practical implications for succession planning and globalization strategies, particularly in emerging market contexts.
{"title":"The impact of intergenerational succession in family businesses on international strategy: A pre-registered report","authors":"Honglin Ren , Anqi Jiao","doi":"10.1016/j.pacfin.2025.103030","DOIUrl":"10.1016/j.pacfin.2025.103030","url":null,"abstract":"<div><div>This pre-registered study explores the impact of intergenerational succession on the international strategy of family businesses. Conceptualizing succession as a critical inflection point in corporate governance, we examine whether leadership transition to a next-generation family member influences a firm's likelihood to initiate or expand international operations. Drawing on agency theory, socioemotional wealth, and dynamic capabilities, we propose that successor characteristics—such as foreign education or work experience—affect global strategic orientation. Using a quasi-natural experimental design, we will employ a difference-in-differences approach to evaluate how succession events affect internationalization outcomes, while also investigating the moderating roles of founder retention and governance professionalization. This study aims to provide theoretical insights into strategic transformation in family firms and generate practical implications for succession planning and globalization strategies, particularly in emerging market contexts.</div></div>","PeriodicalId":48074,"journal":{"name":"Pacific-Basin Finance Journal","volume":"96 ","pages":"Article 103030"},"PeriodicalIF":5.3,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737604","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号