This study investigates the impact of non-controlling shareholders' network on corporate excess goodwill using Chinese publicly listed companies from 2007 to 2020. We find that a stronger centrality of non-controlling shareholders' network leads to a significant decrease in excess goodwill resulting from mergers and acquisitions. This implies that the non-controlling shareholders’ network has a significant inhibitory effect on the occurrence of goodwill bubbles. Mechanism analysis finds that non-controlling shareholders' network can inhibit excess goodwill thorough information effect, resource effect, and governance effect. Furthermore, this inhibitory effect is attributed to pressure-resistant institutional investors and individual investors, and is more pronounced in firms located in less developed intermediary market and legal system environment, as well as firms with lower audit quality. In summary, the non-controlling shareholders' network plays a positive role in curbing excess goodwill in listed companies.

我国共同基金市场的规模在近十年急速扩张，而对于基金经理和基金管理公司谁是决定基金业绩的重要因素的问题仍存争议。本文基于1998-2021年的共同基金、基金经理和基金管理公司数据，采用Abowd, Kramarz, and Margolis （AKM）提出的方法和Mover Dummy Variable（MDV）方法，利用移动者提供的流动性，分离识别了基金经理（人才）和基金管理公司（平台）的固定效应。研究发现在决定基金业绩方面，人才比平台更重要，且重要至少9倍。在不同市场环境中，熊市中人才变得更加重要。在基金类型方面，相对于股票型基金，债券型和混合型基金受人才的影响更大。根据业绩分层，在业绩较好的基金中，人才较平台更为重要，而在业绩较差的基金中，平台的重要性相对提高。通过分析不同跳槽方向的基金经理，发现研究结论不受人才与平台之间依据业绩和业绩差异匹配的影响。本文的研究结论对基金从业者、基金管理公司和投资者在选择平台、人才、基金等方面均有指导意义。

This study examines how localized carbon reduction policies affect corporate green productivity. Leveraging a quasi-experiment from China’s low-carbon pilot rollout across cities, we find that these interventions significantly increased polluting firms’ green productivity. The gains persisted over time and were greater for firms with higher financial constraints, lower market competition, and lower capital intensity. Textual analysis reveals enhanced executive environmental cognition as a plausible channel. Overall, the results provide robust evidence that well-designed local regulations can achieve a win-win outcome of lower emissions and higher efficiency.

We present a novel methodology for constructing portfolios to hedge economic and financial risks arising from climate change. We utilize ChatGPT-4 to identify climate-related conversations during earnings conference calls and connect these time-stamped transcripts with high-frequency stock price data pinpointed to the conversation level. This approach allows us to assess a company’s dynamic exposure to climate change risks by analyzing real-time stock price responses to discussions about climate issues between managers and analysts. Our proposed portfolio, constructed by taking long (short) positions in stocks with positive (negative) market responses to climate conversations, appreciates in value during future periods with negative aggregate climate news shocks. Compared to portfolios constructed using alternative methods, our real-time market response-based portfolios demonstrate superior out-of-sample hedge performance. A key advantage of our approach is its ability to capture time-series and cross-sectional variations in stocks’ rapidly-evolving exposures to climate risk, relying on the timing of when climate-related issues become salient topics that warrant conference call discussions and real-time market responses to such conversations. Additionally, we showcase the versatility of our approach in hedging other types of dynamic risks: namely political risk and pandemic risk.

This study investigates the relationship between investors’ prior gains or losses and their adoption of extrapolative beliefs. Our findings indicate that investors facing prior losses tend to rely on optimistic extrapolative beliefs, whereas those experiencing prior gains adopt pessimistic extrapolative beliefs. These results support the theory of motivated beliefs. The interaction between the capital gain overhang and extrapolative beliefs results in noteworthy mispricing, yielding monthly returns of approximately 1%. Motivated extrapolative beliefs comove with investors’ survey expectations and trading behavior, and help explain momentum anomalies. Additionally, households are susceptible to this belief distortion. Institutional investors can avoid overpriced stocks associated with motivated (over-)optimistic extrapolative beliefs.

This paper examines the impact of collateral shocks on corporate financialization using a sample of Chinese-listed firms from 2008 to 2021. We find a statistically and economically significant positive effect of collateral appreciation on financialization, consistent with profit-chasing motives, even after addressing endogeneity concerns. Additional tests reveal the effects are more pronounced among financially constrained, bank-dependent, and high-agency-cost firms. Financialization also elevates the risktaking and financial risks of firms. Overall, we provide novel evidence that collateral shocks stimulate corporate financialization, with implications for incentives, regulation, and systemic risk monitoring.

Based on the expected utility under uncertain probability distribution, we explore whether the ambiguity of individual stocks is priced in China’s A-share market and the mechanism behind the ambiguity premium phenomenon. Theoretically, when the asset price is in a specific price range, investors with ambiguity aversion do not participate in the transaction of the asset. As the ambiguity of assets increases, investors with high ambiguity aversion withdraw from the market, and investors with low ambiguity aversion remain in the market (the limited market participation phenomenon); investors who remain in the market due to lower ambiguity aversion are also willing to accept a low ambiguity premium. Empirically, we use "the volatility of the distributions of daily stock returns within a month" to measure monthly ambiguity; and find that (1) the equal-weighted average returns of the most ambiguous portfolios (top 20%) are significantly lower 1.38% than those of the least ambiguous portfolios (bottom 20%); (2) ambiguity still significantly negatively affects the cross-sectional stock return after controlling for common firm characteristics; (3) the higher the ambiguity, the lower the future trading activity, the empirical results are consistent to the theoretical predictions. Those findings reveal the mechanism of the negative ambiguity premium in the A-share market, provide new ideas for further building a factor pricing model suitable for the A-share market, and provide a fresh perspective for preventing systemic financial risk.

We develop novel high-frequency indices that measure climate attention, covering a wide range of both developed and emerging economies. This is achieved by analyzing the text of over 23 million tweets published by leading national newspapers on Twitter during the period from 2014 to 2022. Our findings reveal that a country experiencing more severe climate news shocks tends to see both an inflow of capital and an appreciation of its currency. In addition, brown stocks in highly exposed countries experience large and persistent negative returns after a global climate news shock. These outcomes align with the predictions of a risk-sharing model in which investors price climate news shocks and trade consumption and investment goods in global markets

Using a comprehensive factor zoo, we document a notable factor MAX premium in the Chinese market. Factors with high maximum daily returns consistently outperform those with low maximum returns by 0.82% per month in the future, on a risk-adjusted basis. This premium remains robust controlling for various factor characteristics, and is not sensitive to the selection of factors. The factor MAX anomaly stands apart from lottery-type stock anomalies and contributes to elucidate most of these anomalies. The factor MAX premium concentrates in high-eigenvalue principal component factors, shedding light on the prevalent lottery preferences for factor investing in China’s A-share market. We document pronounced existence of factor MAX anomaly in the United States and other G7 countries.

This paper focuses on how Chinese and global economic policy uncertainties (CNEPU and GEPU) and geopolitical risks (CNGPR and GGPR) affect the growth of inbound tourism in China using ARDL and NARDL models as well as monthly series of Chinese inbound tourism revenue and arrivals. Firstly, we find significant effects of CNGPR and GGPR as well as GEPU on the growth of inbound tourism in Hainan Province and even in China nationwide, while the impact of CNEPU is limited. Among them, GEPU always has a significant long-term negative impact on inbound tourism growth (both inbound tourism revenue and inbound tourism arrivals). However, CNGPR has a significant short-term negative impact on inbound tourism growth in China nationwide but it has a significant long-term negative impact on inbound tourism growth in Hainan Province. Besides, estimation results of NARDL model further show the significant short-term effects of GEPU and GGPR on the growth of inbound tourism arrivals in Hainan Province and even in China nationwide, and such short-term effects are always significantly asymmetric. Among them, the negative components of GGPR can always more influence the growth of inbound tourism arrivals. However, the positive components of GEPU can more influence the growth inbound tourism arrivals in Hainan Province, but the negative components of GEPU can more influence the growth of inbound tourism arrivals in China nationwide.