This paper examines whether reserve prices impact the discount on private equity placements (PEPs). Using a sample of auction-based PEPs in China, we find that reserve price discounts are positively associated with bid (offer) price discounts. This inference holds after executing several robustness checks. As extra analyses reveal, the documented impact is ascribed to bidders anchoring on reserve prices. The positive association also depends on bidder identity and anchor-target compatibility. Our evidence ultimately shows that investor wealth benefits from such anchoring biases. Altogether, these findings demonstrate that reserve prices induce bidder undervaluation, thereby resulting in lower offer prices.

This paper develops a delegation-based framework to explain how institutional design shapes pricing incentives under risk. Using China’s 2016 IPO reform—which abolished prefunding requirements and transferred payment obligations from investors to underwriters—as a natural experiment, we show that introducing subscription-payment risk (SPR) renders underwriter’s partial residual claimants with respect to unpaid allocations. Building on Baron’s (1982) delegation model, we argue that the reform amplifies information asymmetry and induces underwriters to adopt more conservative pricing strategies to manage perceived payment risk. Empirically, IPOs exposed to SPR exhibit greater underpricing and lower offer prices, particularly when investor bids reflect stronger valuation pessimism. The effect tends to be less pronounced for reputable underwriters and when foreign institutional investors participate. Overall, the evidence demonstrates how risk redistribution and institutional frictions jointly shape underwriter behavior and pricing efficiency in primary equity markets.

With the development of China's economy, more and more Chinese enterprises are active on the world stage, and cross-border M&A is the most effective and fastest way for enterprises to go abroad and make overseas investments, and it is also an important path for globalization after the enterprises have reached a certain stage of growth. Compared to domestic M&A, cross-border M&A is a more complex economic activity, requiring more factors to be considered and greater risks to be taken, with the slightest misstep often leading to operational difficulties for the acquiring company. It is important to consider whether cross-border M&A can improve business performance, the factors that influence the performance of cross-border M&A, and how to improve the performance of enterprises in cross-border M&A. This study takes 100 cross-border M&A events of Chinese listed companies in Shanghai and Shenzhen during the period of 2017-2020 as a sample, and on the basis of reviewing the research results of cross-border M&A at home and abroad, combined with the characteristics of cross-border M&A of Chinese enterprises, from different perspectives, a number of financial indicators are selected to construct comprehensive performance evaluation indicators using factor analysis, and the preliminary analysis shows that after cross-border M&A, the companies with increased performance The preliminary analysis showed that the number of companies whose performance increased after cross-border M&A increased year by year. The impact of industry relevance and transaction equity on M&A performance is not significant; the ratio ofM&A amount to current assets negatively affects firm performance in the year of M&A. Finally, based on the empirical results, relevant policy recommendations are made to encourage better development of private enterprises and improving cross-border M&A performance.

This study examines the role of artificial intelligence (AI) adoption in the market for corporate control using a sample of Chinese listed firms from 2011 to 2021. We construct a novel firm-level AI Index through textual analysis of annual reports and find that AI adoption significantly enhances both the likelihood of becoming an acquisition target and the valuation premiums commanded in M&A transactions. Specifically, a one-standard-deviation increase in the AI Index is associated with a significant increase in the probability of being acquired and higher deal premiums measured by price-to-earnings multiples. We identify two channels through which AI adoption creates value recognized by the M&A market: an efficiency channel, whereby AI reduces agency costs and improves profitability, and an innovation channel, evidenced by increased high-quality patent output. The persistence of these effects over time further suggests that AI adoption generates substantive improvements in firm fundamentals rather than serving as a transitory informational signal. Importantly, we document significant heterogeneity across ownership structures: the positive effects of AI adoption are substantially weaker for State-Owned Enterprises (SOEs) than for non-SOEs. Our findings contribute to the literature on digital transformation and corporate finance by demonstrating that AI adoption serves as a value-relevant firm attribute that shapes outcomes in the market for corporate control.

This study investigates how the interaction of political accountability and local officials’ career incentives shapes the market for Municipal Corporate Bonds (MCBs) in China, taking the 2017 local government debt personal responsibility rule as a quasinatural experiment. We develop a stylized incomplete-information bargaining model to analyze how the rule reshapes the bargaining equilibrium by rendering officials’ observable characteristics credible signals of bailout incentives. Using a dataset of prefecture-level MCBs from 2008 to 2020, we empirically test the model’s predictions and focus on separating officials’ incentive effects from their inherent ability. Our core findings show that post-announcement of the rule, each additional year of a local party secretary’s remaining time to retirement, a proxy for bailout incentives, reduces MCB spreads by approximately 2.5 basis points and increases issuance volume by about 2.0%. These effects are significantly amplified in fiscally stressed cities. Notably, under the 2017 rule, cities led by party secretaries with stronger bailout incentives can expand MCB issuance, which is contrary to the rule’s original intent to rein in local borrowing.

This study examines the impact of Economic Policy Uncertainty (EPU) on the stock price crash risk of Chinese commercial banks. In addition, it explores how Governance and Digital Transparency curtail the effect of EPU on stock price crash risk. Using a sample of 50 Chinese A-share-listed banks from 2012 to 2024, the study reveals that EPU significantly increased the banks’ stock price crash risk. The findings are robust to alternative measures of EPU and stock price crash risk. Further, governance and FinTech adoption mitigate the positive effect. The mitigating effect persists across high- and low-risk bank subsamples. In addition, we perform a battery of analyses to support our main findings. These findings have important theoretical and practical implications.

The China Securities Regulatory Commission is allowing firms to use externally financed funds for share repurchases, a recent measure to enable listed companies address valuation pressures and protect investors; however, its implications for corporate marketing decisions remain unclear. Using an event sample of Chinese listed firms that conducted open-market repurchases between 2009 and 2024, this study empirically examines how this market activity financed by different sources influence marketing decisions and explores the underlying mechanisms. The findings show that compared with firms using internal cash for repurchases, those relying on debt financing are inclined to resist myopic marketing decisions, and this negative relationship is pronounced under high analyst coverage and when privately owned listed firms are controlled by family entrepreneurs. These results remain robust after replacing the dependent variables and applying propensity score matching. Overall, this study shows that debt financing to support share repurchases has a long-term beneficial governance impact as it improves earnings quality and protects investor interests, and offers a new perspective on the relationship between financing-based repurchases and marketing myopia, and provides useful policy insights for evaluating the effectiveness of China’s refinancing regulations related to share repurchases, while guiding further refinement.

In dynamic housing markets, a sale can affect not only the transacting parties, but also other buyers who had considered the property. We develop a dynamic sequential search model in which property exit creates imperfect recall and signals tighter market conditions, generating transaction cascades. Using data from a major Chinese housing platform, we exploit quasi-random sales of previously inspected properties as option-loss shocks. Option loss raises affected buyers’ purchase probability by 67%, with stronger effects in tighter markets and among buyers with larger choice sets. A back-of-the-envelope quantification suggests that these cascades accounts for about 30% of observed market-level transaction activity. Option loss also reduces the number of property visits, broadens search criteria, and is associated with higher transaction prices. The results highlight imperfect recall in dynamic search as a microlevel channel through which housing market activity can be amplified.

We investigate the capabilities of Large Language Models (LLMs) in generating novel alpha factors for option returns. Utilizing a structured prompt-engineering approach, LLMs like GPT-5 can directly create factors for two distinct options markets: the mature U.S. market and the emerging Chinese market. Empirical analysis further reveals that the LLM-generated factors exhibit remarkable and robust performance, delivering statistically signifcant returns in both all-sample and extensive out-of-sample tests. Beyond their statistical signifcance, such factors are economically meaningful. They display low self-correlation, indicating genuine innovation, and are grounded in sound economic rationale derived from market microstructure and behavioral fnance principles, showcasing a key advantage over traditional machine learning models.

This paper studies whether financial technology (FinTech) changes loan contract design by reducing banks’ reliance on collateral in corporate lending. Using loan-level data on Chinese listed firms from 2007 to 2023 and exploiting the People’s Bank of China’s 2019 FinTech Development Plan as a quasi-natural experiment, we find that banks with stronger pre-policy FinTech capability significantly reduce secured lending after the policy shock. In the benchmark specification, the probability that a loan is secured falls by 1.64 percentage points, or about 2.7% relative to the baseline secured-loan share. The result is robust to alternative loan classifications, matching procedures, alternative measures of FinTech adoption, aggregated lending outcomes, and alternative inference procedures. The pattern is more pronounced among small and medium-sized enterprises, lower-tier branches, and branches located outside bank headquarters’ cities, where borrower information is likely to be more limited. Supplementary analyses are consistent with FinTech reducing banks’ information-production costs and suggest that technological proximity to FinTech-active peers may amplify the collateral-reducing effect. Overall, the evidence indicates that FinTech can enhance banks’ screening capacity and shift lending decisions away from reliance on asset-based guarantees toward information-based credit assessment.