Using data from 11 major international markets that celebrate six cultural New Year holidays that do not occur on January 1, we find that stock markets tend to outperform in days surrounding a cultural New Year. After controlling for firm characteristics, an average stock earns 2.5% higher abnormal returns across all markets in the month of a cultural New Year relative to other months of the year. Further evidence suggests that positive holiday moods, in conjunction with cash infusions prior to a cultural New Year, produce elevated stock prices, particularly among those stocks most preferred and traded by individual investors.

We study the effects of a direct high-speed rail (HSR) service between two cities on investors and firms in China’s A-share markets. After an HSR introduction, retail investors make more cross-city web searches and block stock purchases of firms in connected cities. An HSR introduction also leads to less comovement among local stocks and more comovement between stocks in connected cities. Firms located in more central cities in the HSR network enjoy higher firm valuation, lower cost of equity, higher turnover, and better liquidity, in part through the channel of increased investor recognition. The HSR effects on capital market outcomes are more pronounced among small firms and when the connected city-pair distance is below 1,500 km, for which HSR is faster than flying. The findings highlight the importance of in-person interactions in financial markets.

We find that institutional investors' local culture of religiosity influences their stock lending decisions and induces short-sale constraints on the underlying stocks. Firms with higher ownerships by blockholders located in more religious counties are associated with higher utilization of lendable shares. This effect is driven by a lower supply of, rather than a higher demand for, lendable shares. Stock lending fees of such firms are higher, and higher short interests of such firms more strongly predict lower future stock returns. Our findings suggest that the cultural norms of institutional investors can create market friction through the stock lending channel.

Corporate misconduct poses significant risks to financial markets, undermining investor confidence and economic stability. This study investigates the influence of World Heritage culture, with its social, historical, and symbolic values, on reducing corporate misconduct. Using firm-level data from China, with its rich cultural heritage and ancient civilization, we find a significant negative association between the number of World Heritage sites near a company and corporate misconduct. This suggests that a richer World Heritage culture fosters an informal institutional environment that mitigates corporate misconduct. This effect is robust across 100 km, 200 km, and 300 km thresholds and remains significant when using a binary misconduct indicator. The results also show that World Heritage culture enhances corporate social responsibility (CSR) and social capital, which in turn reduces corporate misconduct. Additionally, the impact of World Heritage culture is more pronounced in firms located in high social trust areas, those with high institutional investor supervision, and those farther from regulatory authorities. These findings advance academic knowledge and offer practical implications for policymakers and investors.

如何通过宏观经济政策有效支持气候适应性投资，从而提升气候韧性，是现阶段推进气候适应性社会建设的重要议题。本文首先在动态测度气候韧性的基础上，为气候适应性投资提升气候韧性的效果提供初步证据；然后构建嵌入气候适应型财政货币政策的气候灾难风险模型，考察气候适应性投资缺口的产生机理以及宏观经济政策的支持效果。最后进一步讨论最优的宏观经济政策支持力度与政策组合。研究发现：第一，气候适应性投资能够显著增强经济的抗气候风险冲击能力以及冲击后的反弹能力，有效提升气候韧性。但由于银行、企业道德风险以及气候适应性技术约束的存在，气候适应性投资低于社会最优水平，气候适应性投资缺口由此产生。第二，气候再贷款政策通过缓解银行道德风险引起的贷款利率溢价；气候适应补贴通过补偿适应性技术门槛产生的投资调整成本，能够有效缩小气候适应性投资缺口。但企业道德风险会削弱两类政策的实际效果。第三，宏观经济政策通过支持气候适应性投资，能够有效缓解气候灾难风险对经济金融系统的负面冲击，其中气候再贷款政策的效果主要体现在金融层面，气候适应补贴政策对实体经济的作用更加显著。第四，为完全弥补气候适应性投资的缺口，最优的宏观经济政策支持力度随着气候灾难风险升高需要不断增强。但气候再贷款政策会受到零利率下限的约束，过低的气候再贷款利率不利于社会福利改进，气候再贷款政策搭配使用气候适应补贴政策能够更好地增进社会福利。

Analyzing data from 3,616 Chinese listed firms, we find a strong positive relationship between policy uncertainty and firms’ exchange rate exposure. This result remains robust after controlling for macroeconomic conditions and addressing endogeneity issues. Notably, policy uncertainty’s impact is significantly stronger for firms with a higher degree of international involvement and for poorly-governed firms. Interestingly, firms use financial hedging more intensively and reduce their operational hedging in high-uncertainty periods. Our results suggest that policy uncertainty exacerbates the impact of currency movements on firms’ financial performance, as firms become increasingly involved in international operations. Consequently, firms should strengthen their corporate governance and make effective use of hedging tools.

Investors' previous experiences with a stock affect their willingness to repurchase it. Using Chinese investor-level brokerage data, we find that investors are less likely to repurchase stocks that have increased in value since they were sold. We then construct a novel measure of Regret to capture investors' repurchase regret and investigate its asset pricing implications. Stocks with higher Regret experience lower buying pressure from retail investors in the future, leading to lower future returns. In terms of economic magnitude, portfolios with low Regret generate 12% more annualized abnormal returns. Further analyses show that the pricing effect of Regret is more pronounced among lottery-like stocks and those in which investors have previously gained profit. The results are robust to alternative estimations.

本研究开发了一种创新方法来预测中国上市公司的"漂绿"行为。通过将大型语言模型BERT整合到机器学习框架中，我们构建了一个先进的漂绿预测模型。这种方法能够捕捉企业社会责任报告和年度报告的环境披露中微妙的语言线索和语义细节，显著提高了识别漂绿的精确度。研究采用了多种机器学习模型，包括支持向量机（SVM）、随机森林（RandomForest）和随机欠采样算法（RUSBoost），并在三种不同的数据集上进行了测试：基础财务数据集、扩展的Word2Vec环境披露数据集，以及BERT优化的环境披露数据集。结果表明，RUSBoost算法结合BERT调整的环境披露数据在各项评估指标上表现最佳，凸显了先进自然语言处理技术在分析环境披露文本方面的优势。此外，我们的研究发现预测的漂绿指标与ESG评级机构间的评级分歧显著相关，验证了本研究所开发的漂绿变量。本研究为识别和预测企业漂绿行为提供了一个创新的、基于文本的方法。这一工具对投资者、监管者和政策制定者具有重要价值，有助于捕捉公司的欺骗性环境披露。

本文探究了长尾市场上的数字信贷利率形成问题，基于Hotelling模型从信息传输的视角提出该市场存在一种新型的垄断，并且动态地分析了这种垄断的加剧过程。本文主要发现有：第一，银行决策的最优信贷利率与其获得的借款人信息量负相关。第二，如果数字空间存在信息传输损耗，那么均衡利率高于Bertrand利率水平，为一种信息垄断利率，垄断溢价、垄断利润均与损耗系数正相关。第三，银行与借款人之间的交易次数越多，这种溢价也会越大，而且借款人之间的利率不平等问题也会加重；第四，部分借款人可能还会遭受歧视性定价。针对这些问题，本文提出了三项政策设计讨论，包括因信息垄断造成的垄断指标测度、风险指标测度以及最优的数字普惠考核政策，以破除数字鸿沟、缓解信息垄断，从而降低长尾市场均衡利率，保障数字金融的普惠性。本文最后对理论模型进行仿真，模拟主要结论。

房地产风险和中小金融机构风险是当前经济金融工作“三大风险”中的两个风险，而我国房地产市场供求关系的重大变化可能诱发地方性中小银行的风险事件。本文以2010年—2022年中国地级市层面的房价数据和地方性中小银行数据为样本，研究发现城市房价泡沫上升会产生风险“粉饰效应”，表现为降低地方性中小银行风险，但是房价泡沫破灭会导致中小银行风险持续上升。影响机制的结果发现，城市房价泡沫带来的抵押品价值效应和银行风险资产结构调整效应会降低中小银行风险，即提升中小银行客户抵押品价值、促进中小银行信用风险资产替代进而降低中小银行风险。异质性研究表明：房价泡沫上升对于地方性中小银行风险粉饰作用在城商行、省域内经营的地方性银行、银行业竞争度较低地区银行影响更大。进一步研究表明：城市房价泡沫会扭曲地方性中小银行的信贷投放行为，表现为更多的信贷资金流向金融资产和房地产行业，并没能够有效支持中小企业融资。本研究不仅有助于厘清房地产市场风险传溢的影响机理，还能够深化对于房地产风险-地方性中小银行风险-信贷扭曲影响逻辑的理解，从而为防范和化解系统性金融风险提供政策启示。