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Estimating common impact in class action litigation: A two-step method 评估集体诉讼中的共同影响:两步法
IF 0.5 4区 经济学 Q4 ECONOMICS Pub Date : 2025-08-13 DOI: 10.1002/ise3.70017
Yonghong An

A central question in class action litigation is all or nearly all proposed class members were injured by the alleged conduct, that is, does “common impact” exist. In this paper, we demonstrate that a two-step econometric methodology can be used to estimate customer-level overcharges, providing a basis for evaluating whether common impact exists. The estimates obtained from this methodology possess desirable properties. We also address several common misunderstandings and misinterpretations of the two-step approach in practice.

集体诉讼的一个核心问题是所有或几乎所有拟议的集体成员都受到指控行为的伤害,即“共同影响”是否存在。在本文中,我们证明了两步计量经济学方法可用于估计客户层面的超额收费,为评估是否存在共同影响提供了基础。用这种方法得到的估计具有理想的性质。我们还解决了实践中对两步法的几个常见误解和误解。
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引用次数: 0
Dynamic Henry George Theorem and Optimal City Sizes 动态亨利·乔治定理与最优城市规模
IF 0.5 4区 经济学 Q4 ECONOMICS Pub Date : 2025-07-21 DOI: 10.1002/ise3.70013
Shihe Fu

The Henry George Theorem (HGT) in static models states that when a city has an optimal population size, aggregate urban differential land rents exactly cover costs of pure public goods. This paper extends the static HGT to dynamic settings. Through a series of dynamic models, the paper tentatively concludes that the HGT holds in dynamic settings in terms of present value—the present value of urban differential land rents equals the present value of public goods expenditure. In urban economies with congestion externalities or production externalities, the dynamic HGT still holds if externalities are priced correctly.

静态模型中的亨利·乔治定理(HGT)指出,当一个城市的人口规模达到最优时,城市差异土地总租金正好覆盖纯公共产品的成本。本文将静态HGT扩展到动态设置。通过一系列的动态模型,本文初步得出了HGT在现值方面是成立的——城市地租差异现值等于公共物品支出的现值。在具有拥堵外部性或生产外部性的城市经济中,如果外部性定价正确,动态HGT仍然成立。
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引用次数: 0
Introduction to the Special Issue: Cities and Economic Development 特刊导言:城市与经济发展
IF 0.5 4区 经济学 Q4 ECONOMICS Pub Date : 2025-07-17 DOI: 10.1002/ise3.70014
Shihe Fu, Junfu Zhang
<p>To a great extent, economic development is urban development. As economies grow, people and firms cluster in cities to take advantage of agglomeration economies, better infrastructure, access to markets and labor, and consumption amenities. Urbanization not only drives productivity gains and enhances consumer welfare but also introduces challenges such as housing affordability, traffic congestion, pollution, and inequality. Understanding these dynamics is essential for promoting efficiency and sustainable development. The evolution of cities is therefore not only a reflection of economic progress but also a key driver of it.</p><p>The field that examines these dynamics—urban economics—focuses on cities and the spatial organization of economic activities. It covers a wide range of topics, including but not limited to the determinants of city size and growth, the functioning of urban housing and labor markets, transportation systems, land use, public goods provision, urban poverty, segregation, and environmental challenges. Researchers in this field investigate how individuals and firms make location decisions and how their decisions shape the economic and social structure of urban areas.</p><p>A defining feature of research in urban economics is its methodological diversity. Scholars employ both partial and general equilibrium models, conduct reduced-form and structural estimations, and rely on mathematical modeling as well as computer simulations. On the empirical side, quasi-experimental strategies—such as difference-in-differences, spatial regression discontinuity, and instrumental variables—are widely used to identify causality. With the growing availability of geocoded data, remote sensing imagery, granular administrative records, and online big data, researchers increasingly adopt spatial econometrics, machine learning tools, and GIS techniques to uncover complex spatial patterns and effects.</p><p>The field has seen remarkable growth in recent years, propelled by greater data availability, advances in computing power, and methodological innovations inspired by neighboring disciplines such as microeconometrics, labor economics, industrial organization, and international trade. These developments have enabled scholars to revisit classic theories, uncover new stylized facts, and produce sharper, policy-relevant insights.</p><p>What makes urban economic research especially valuable is its direct relevance to real-world challenges. The insights it generates often inform how cities are planned, governed, and improved. For example, research on housing markets informs zoning and land-use regulation; studies of transportation systems guide infrastructure investment and congestion pricing; and work on crime and inequality has implications for public safety and social policies. Several papers in this special issue—including those on fiscal transparency, pandemic-era rental markets, and crime—offer concrete evidence that can help policymakers design
经济的发展在很大程度上就是城市的发展。随着经济增长,人们和企业聚集在城市,以利用集聚经济、更好的基础设施、市场和劳动力准入以及消费便利设施。城市化不仅推动了生产率的提高,提高了消费者的福利,但也带来了诸如住房负担能力、交通拥堵、污染和不平等等挑战。了解这些动态对于促进效率和可持续发展至关重要。因此,城市的演变不仅是经济进步的反映,也是经济进步的关键驱动力。研究这些动态的领域——城市经济学——侧重于城市和经济活动的空间组织。它涵盖了广泛的主题,包括但不限于城市规模和增长的决定因素,城市住房和劳动力市场的运作,交通系统,土地使用,公共产品提供,城市贫困,隔离和环境挑战。这一领域的研究人员研究个人和企业如何做出区位决策,以及他们的决策如何塑造城市地区的经济和社会结构。城市经济学研究的一个显著特征是其方法的多样性。学者们采用部分和一般平衡模型,进行简化形式和结构估计,并依靠数学建模和计算机模拟。在实证方面,准实验策略——如差异中的差异、空间回归不连续和工具变量——被广泛用于确定因果关系。随着地理编码数据、遥感图像、细粒度管理记录和在线大数据的日益可用性,研究人员越来越多地采用空间计量经济学、机器学习工具和GIS技术来揭示复杂的空间模式和影响。近年来,由于数据可用性的提高、计算能力的进步,以及受微观计量经济学、劳动经济学、产业组织和国际贸易等邻近学科启发的方法创新,该领域取得了显著的增长。这些发展使学者们能够重新审视经典理论,发现新的风格化事实,并产生更尖锐的、与政策相关的见解。城市经济研究之所以特别有价值,是因为它与现实世界的挑战直接相关。它产生的见解通常会告诉我们如何规划、管理和改善城市。例如,对住房市场的研究为分区和土地使用法规提供了信息;交通系统研究指导基础设施投资和拥堵收费;研究犯罪和不平等对公共安全和社会政策也有影响。本期特刊中的几篇论文——包括有关财政透明度、大流行时期的租赁市场和犯罪的论文——提供了具体证据,可以帮助决策者设计出更有效的干预措施。随着城市继续努力应对与可负担性、不平等和可持续性相关的挑战,城市经济学在基于证据的政策制定中的作用变得越来越重要。这种相关性在中国等快速发展的国家尤为明显,在这些国家,城市的迅速扩张深刻地改变了经济和社会。中国城市发展的速度和规模带来了独特的挑战——从管理移民和基础设施需求,创造就业机会,到促进公平发展和环境可持续性。与此同时,由于大量的社会实验和制度变革、管理和大数据获取的改善以及城市经验的显著区域差异,中国为实证研究提供了肥沃的环境。本期的几篇文章引用了中国的数据,说明了针对特定地点的分析如何产生既立足于当地又与全球相关的见解。这里收录的论文体现了当代城市研究的主题和方法的广度。他们讨论了不同的主题,如外国直接投资的模式(Qian, Zhang, and Chen),审计在提高财政透明度中的作用(Chen和Hu),对大规模枪击事件的需求反应(Chen等人),流行病引起的租赁市场变化(Jiang和Shen),收入两极化对犯罪的影响(Liu, Wang, and Wang),以及动态条件下最优规模城市土地租金的理论分析(Fu)。它们共同展示了现代城市经济学的经验深度和理论丰富性。我们希望这期特刊能引发新的对话,启发未来的研究,加深我们对城市与经济发展之间不断发展的关系的理解。 我们尤其欢迎更多关于非洲和其他欠发达国家城市化的研究,这些国家在数据可得性、城市发展和城市治理方面面临的挑战尤为突出。
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引用次数: 0
The Effect of Income Polarization on Crime: Evidence From Court Judicial Documents in China 收入两极分化对犯罪的影响:来自中国法院司法文书的证据
IF 0.5 4区 经济学 Q4 ECONOMICS Pub Date : 2025-07-17 DOI: 10.1002/ise3.70012
Jingqi Liu, Chen Wang, Yuzhou Wang

Crime affects social stability and people's safety, and directly increases the cost of urban development. Existing literature shows that income distribution affects crime, but it mainly focuses on the effect of income inequality and poverty, with scant evidence addressing the impact of income polarization on crime. Applying data from the China Household Finance Survey (CHFS) and judicial documents from 2014 to 2018, this paper demonstrates that rising income polarization significantly increases crime in cities. The result still holds after a series of robustness checks. Heterogeneity analyses show that income polarization has a more pronounced effect on criminal activities related to violence, robbery and stealing, drug and financial fraud. Moreover, cities with a higher proportion of young and migrant populations would be more adversely affected by income polarization. Mechanism analyses indicate that income polarization exacerbates crime by increasing alienation, reducing job-seeking willingness and happiness of residents.

犯罪影响社会稳定和人民安全,直接增加城市发展成本。现有文献表明,收入分配影响犯罪,但主要集中在收入不平等和贫困的影响,很少有证据解决收入两极分化对犯罪的影响。本文运用2014 - 2018年中国家庭金融调查(CHFS)数据和司法文件,证明收入两极分化加剧显著增加了城市犯罪。经过一系列稳健性检验,结果仍然成立。异质性分析表明,收入两极分化对暴力、抢劫、盗窃、毒品和金融欺诈等犯罪活动的影响更为明显。此外,年轻人和流动人口比例较高的城市受收入两极分化的不利影响更大。机制分析表明,收入两极化通过增加疏离感、降低居民求职意愿和幸福感来加剧犯罪。
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引用次数: 0
Does National Auditing Improve Local Fiscal Transparency? Evidence From China 国家审计提高了地方财政透明度吗?来自中国的证据
IF 0.5 4区 经济学 Q4 ECONOMICS Pub Date : 2025-04-14 DOI: 10.1002/ise3.70007
Zhuo Chen, Mingzhi Hu

This paper examines whether and how national audits affect local fiscal transparency in China. Using panel data from 30 provinces between 2009 and 2018, we find that national audits significantly improve local fiscal transparency after controlling for various economic and institutional factors. The effect of national audits on fiscal transparency varies significantly by region, which is stronger and statistically significant in eastern regions and in regions with high land finance, while not significant in central-western regions or in those with low land finance. Furthermore, the positive impact is primarily driven by the disclosure and defense functions. These findings suggest that national audits are valuable for improving fiscal transparency, but their effectiveness varies depending on regional economic development and local government financing mechanisms.

本文考察了国家审计是否以及如何影响中国地方财政透明度。利用2009 - 2018年30个省份的面板数据,我们发现在控制了各种经济和制度因素后,国家审计显著提高了地方财政透明度。国家审计对财政透明度的影响因地区而异,在东部地区和土地财政高的地区更强且具有统计学意义,而在中西部地区或土地财政低的地区则不显著。此外,正面影响主要是由披露和防御功能驱动的。这些发现表明,国家审计对提高财政透明度很有价值,但其有效性因区域经济发展和地方政府融资机制而异。
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引用次数: 0
Localized and Short-Term Effects of Lockdowns on Urban Rental Markets: Evidence From Shanghai 封锁对城市租赁市场的局部和短期影响:来自上海的证据
IF 0.5 4区 经济学 Q4 ECONOMICS Pub Date : 2025-04-08 DOI: 10.1002/ise3.70005
Yanpeng Jiang, Xiaochi Shen

This paper investigates the localized and short-term effects of COVID-19-induced lockdowns on Shanghai's rental market in the second half of 2022. Using Difference-in-Differences methodologies, we find that rental prices declined by 1.5% following the lockdowns on average, with areas characterized by a high density of companies falling by 2.0% and no significant change in suburban areas. Event-study analysis further reveals that this decline peaked at 3.0% 2 months post-lockdown. This decline was temporary with rental prices returning to pre-lockdown levels within 12 months. Robustness checks and comparisons with housing sale prices confirm the absence of significant spillover effects or structural shifts. These findings underscore the localized and temporary nature of lockdown-induced disruptions in the rental market and may be valuable to housing policymakers considering responses to short-term crises.

本文研究了新冠肺炎疫情对2022年下半年上海租赁市场的局部和短期影响。采用差异中的差异方法,我们发现,在封城之后,租金价格平均下降了1.5%,其中企业密度高的地区下降了2.0%,郊区没有明显变化。事件研究分析进一步显示,这一降幅在封锁后2个月达到3.0%的峰值。这种下降是暂时的,租金在12个月内恢复到封锁前的水平。鲁棒性检查和与房屋销售价格的比较证实了没有显著的溢出效应或结构转移。这些发现强调了封锁导致的租赁市场中断的局域性和暂时性,可能对考虑应对短期危机的住房政策制定者有价值。
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引用次数: 0
Special issue on machine learning and artificial intelligence in business and economics 商业和经济学中的机器学习和人工智能特刊
IF 0.5 4区 经济学 Q4 ECONOMICS Pub Date : 2024-12-18 DOI: 10.1002/ise3.106
Ye Luo
<p>In academic studies, the integration of artificial intelligence (AI) and machine learning in the field of economics and finance has revolutionized research methodologies and enhanced the understanding of complex economic phenomena. Researchers can now analyze vast amounts of data more efficiently, identify patterns and trends, and develop predictive models with greater accuracy. This enables academics to delve deeper into economic theories, test hypotheses, and make more informed policy recommendations. Furthermore, the use of AI and machine learning algorithms in academic studies can lead to new insights, innovative research approaches, and interdisciplinary collaborations.</p><p>The leading article of this special issue, “Finance research over 40 years: What can we learn from machine learning?”, investigated on the topic distributional features of the research in finance over the past 40 years. This is the most thorough investigation on such a large field about the research topics, authorship distributions, using the method of machine learning. The authors have conducted a study applying machine learning models to analyze a data set comprising 20,185 finance articles published across 17 finance journals from 1976 to 2015. Through this analysis, they have objectively identified 38 research topics within the field. Among these topics, the financial crisis, hedge/mutual fund, social network, and culture emerged as the fastest-growing areas, while market microstructure, initial public offering, and option pricing experienced a decline in interest from 2006 to 2015. The authors also find a very interesting exponential decay rule for the number of topics that authors are covering.</p><p>A similar paper “Topic modeling of financial accounting research over 70 years” investigated topic distributions and time series patterns in financial accounting research in the past 70 years using machine learning methods. The author finds that The topics of mergers and acquisitions, disclosure and internal control, and political connection exhibited the most rapid expansion, whereas management control systems, earnings management, and valuation experienced the greatest contraction from 2014 to 2023. This research on topic classification itself will aid accounting investigators in bypassing superfluous efforts and fostering increased interdisciplinary research.</p><p>Beyond the topic modeling, there are two papers in this special issue regarding using machine learning in asset pricing. The paper “Investigating the profit performance of quantitative timing trading strategies in the Shanghai copper futures market, 2020–2022” investigates the time series signals using machine learning methods in the Shanghai copper futures market. The authors prudently conduct a reality check and advanced assessments to avoid data snooping problem. The basic conclusion of the paper demonstrates that after eliminating the data snooping bias, the time series signal within the class of
在学术研究中,人工智能(AI)和机器学习在经济和金融领域的融合彻底改变了研究方法,增强了对复杂经济现象的理解。研究人员现在可以更有效地分析大量数据,识别模式和趋势,并更准确地开发预测模型。这使学者们能够更深入地研究经济理论,检验假设,并提出更明智的政策建议。此外,在学术研究中使用人工智能和机器学习算法可以带来新的见解、创新的研究方法和跨学科合作。本期特刊的主打文章《40年来的金融研究:我们能从机器学习中学到什么?》,对近40年来金融学研究的课题分布特征进行了考察。这是使用机器学习的方法对如此大的领域进行的关于研究主题、作者分布的最彻底的调查。作者进行了一项研究,应用机器学习模型分析了1976年至2015年在17种金融期刊上发表的20,185篇金融文章的数据集。通过这一分析,他们客观地确定了该领域内的38个研究课题。在这些主题中,金融危机、对冲/共同基金、社交网络和文化成为增长最快的领域,而市场微观结构、首次公开发行和期权定价在2006年至2015年期间出现了兴趣下降。作者还发现了一个非常有趣的指数衰减规律,即作者所涉及的主题数量。另一篇类似的论文《70年来财务会计研究的主题建模》使用机器学习方法研究了过去70年来财务会计研究的主题分布和时间序列模式。笔者发现,2014 - 2023年,并购、披露与内部控制、政治关系等主题扩张最为迅速,而管理控制制度、盈余管理、估值等主题收缩最为明显。这个主题分类本身的研究将有助于会计调查员绕过多余的努力和促进跨学科研究的增加。除了主题建模之外,本期特刊中还有两篇关于在资产定价中使用机器学习的论文。本文《2020-2022年上海铜期货市场定量择时交易策略的盈利表现研究》利用机器学习方法对上海铜期货市场的时间序列信号进行了研究。作者谨慎地进行了现实检查和高级评估,以避免数据窥探问题。本文的基本结论表明,在消除数据窥探偏差后,期货市场调查类内的时间序列信号难以产生一致的利润。本文“中国股票市场的因素择时”对中国股票市场的因素择时可行性进行了探索性研究,涵盖了文献中10大类24个具有代表性且识别良好的风险因素。短期反转的长空组合表现出很强的样本外可预测性,这种可预测性在各种模型和所有类型的预测因子中都是稳健的。与以往文献不同的是,本文证明了中国股票市场因子组合的预测能力在时间序列上具有显著的可预测性。最后,这期特刊包括两篇关于在经济和金融中使用人工智能的论文。第一篇论文《手掌作为去中心化标识符:缓解平台经济的掠夺》,提出了一种基于AI手掌识别KYC技术的新经济模型。该技术有可能解决或减少平台经济中的寻宝问题,从而提高平台推广的效率。第二篇论文《金融服务新时代:人工智能如何提高投资效率》对中国最大的数字金融投资平台东方理财网采用的人工智能技术进行了很好的总结。通过以上简短的介绍,我们可以看到本期特刊的论文有助于我们理解机器学习和人工智能在经济金融领域的应用。还有更多的问题,这期特刊的论文还没有到达。我们希望这些论文的发现将鼓励和启发读者在这一领域进行更多有趣和富有成效的研究。叶洛:构思、形式分析、写作—初稿、写作—评审、编辑。作者没有什么可报道的。作者声明无利益冲突。
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引用次数: 0
Informational differences, adaptive learning, and inflation forecast bias 信息差异、适应性学习和通货膨胀预测偏差
IF 0.5 4区 经济学 Q4 ECONOMICS Pub Date : 2024-12-16 DOI: 10.1002/ise3.105
Qiang Chen, Zechen Yin

This work highlights a previously overlooked factor that contributes to bias in private inflation forecasts—ignorance of confidential monetary rules. Additionally, it examines how this ignorance indirectly affects policy rate settings. The model proposed reconciles biases in two key forecast sources: the inflation expectations from the Survey of Professional Forecasters and the Federal Reserve's Greenbook forecasts for the output gap. Moreover, the model investigates the impact of informational differences on monetary policy transmission and identifies clear conditions under which inflation rises following a contractionary monetary policy shock.

这项工作强调了一个以前被忽视的因素,它导致了私人通胀预测的偏差——对保密货币规则的无知。此外,它还研究了这种无知如何间接影响政策利率设置。该模型调和了两个关键预测来源的偏差:专业预测者调查(Survey of Professional forecasts)的通胀预期和美联储(fed)的绿皮书(Greenbook)对产出缺口的预测。此外,该模型还研究了信息差异对货币政策传导的影响,并确定了紧缩货币政策冲击后通货膨胀上升的明确条件。
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引用次数: 0
ESG rating, corporate dividends policy, and the moderating role of corporate life cycle: Cross country study ESG评级、公司股利政策与公司生命周期的调节作用:跨国研究
IF 0.5 4区 经济学 Q4 ECONOMICS Pub Date : 2024-12-01 DOI: 10.1002/ise3.104
Husam Ananzeh, Amin Khalifeh, Hamza Alqudah, Hassan Ali Al-Ababneh, Hamzeh Al Amosh

This article investigates the link between environmental, social, and governance performance (ESG) and dividend policy, as well as how likely the corporate life cycle might moderate this association. Using cross-country data from 2010 to 2020, the findings of this study reveal that ESG has a favorable influence on corporate dividend payments as measured by dividend payout ratio, dividend yield, and dichotomous variable. This conclusion holds true when the three ESG pillars are examined independently on dividend policy measurements. Furthermore, this analysis reveals that the firm's life cycle stage moderates the association between ESG and corporate dividend policy, exhibiting a negative moderating impact. This study specifically reveals that the relationship between ESG and dividends is stronger for firms in the early stages of their life cycle than for those in the mature stages. This relationship applies to firms operating in developed economies compared to developing economies. The study findings particularly highlight the dynamic nature of the link between ESG and dividends, underlining that this relationship is dependent on the stage of a company's life cycle. Understanding this relationship may assist stakeholders, such as investors and management, in making educated decisions about dividend expectations and sustainable practices depending on the life cycle of a firm.

本文研究了环境、社会和治理绩效(ESG)与股息政策之间的联系,以及公司生命周期如何调节这种联系。利用2010 - 2020年的跨国数据,本研究发现,从股息支付率、股息收益率和二分类变量来看,ESG对公司股息支付具有良好的影响。当三个ESG支柱在股息政策度量上被独立检验时,这一结论成立。进一步分析发现,公司生命周期阶段调节ESG与公司股息政策之间的关联,呈现负向调节作用。本研究特别揭示了ESG与股息之间的关系在公司生命周期的早期阶段比在成熟期的公司更强。与发展中经济体相比,这种关系适用于在发达经济体经营的公司。研究结果特别强调了ESG与股息之间联系的动态性质,强调这种关系取决于公司生命周期的阶段。了解这种关系可以帮助利益相关者,如投资者和管理层,根据公司的生命周期做出有关股息预期和可持续实践的明智决策。
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引用次数: 0
How much and how long? The transmission of external shocks on stock market in Chinese hospitality and tourism industry 多少钱,多长时间?外部冲击对中国酒店旅游业股票市场的传导
IF 0.5 4区 经济学 Q4 ECONOMICS Pub Date : 2024-11-21 DOI: 10.1002/ise3.103
Yuan Chen, Qikexin Yu, Peiwen Yuan, Yeyang Zhao

In this study, we employ the synthetic control method to evaluate the transmission of external shocks, taking COVID-19 as an example, on the stock market in the Chinese hospitality and tourism industry, specifically in the catering, hotel, cultural landscape, natural landscape, and travel agency sectors. We find an increasingly negative causal effect on stock returns in all five sectors within 1–2 days after the external shock occurred. However, this impact then gradually shrank, disappearing after 7 days. We also show that the short- and long-term causal effects of the external shock on stock volatility in the above sectors are positive and significant, due to the great uncertainty induced by the pandemic. We examine the sensitivity of stock market reactions and find that hotels are the most vulnerable among the five sectors in terms of the duration of the pandemic's negative impact. Finally, we report that larger firms in the catering and hotel sectors (compared to the other three sectors) show a greater increase in volatility. These findings provide valuable insights into financial market reactions to an external shock and offer implications for crisis management in the hospitality and tourism industry during such events.

在本研究中,我们采用综合控制方法来评估外部冲击的传导,以COVID-19为例,在中国酒店和旅游行业的股票市场,特别是在餐饮、酒店、文化景观、自然景观和旅行社行业。我们发现,在外部冲击发生后的1-2天内,所有五个行业的股票收益都呈负向因果关系。但这种影响随后逐渐减弱,7天后消失。我们还表明,由于疫情带来的巨大不确定性,外部冲击对上述行业股票波动的短期和长期因果影响是积极和显著的。我们考察了股市反应的敏感性,发现就疫情负面影响的持续时间而言,酒店是五个行业中最脆弱的。最后,我们报告说,餐饮和酒店行业的大公司(与其他三个行业相比)表现出更大的波动性。这些发现为金融市场对外部冲击的反应提供了有价值的见解,并为酒店和旅游业在此类事件期间的危机管理提供了启示。
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引用次数: 0
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International Studies of Economics
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