We construct a text-based measure of war-related diplomatic sentiment from 154,185 foreignministry communications across the 15 largest world economies. The daily index tracks military escalations and ceasefires, varies across countries, and predicts newspaper-based geopolitical risk more than the reverse. Adverse Chinese rhetoric foreshadows stronger southbound reallocation into Hong Kong equities and weaker Stock Connect flows; a one-unit decline shifts daily flows by $42.4 million towards outflows, operating through a relative-price channel widening the AH premium rather than onshore declines. In monthly cross-country analyses, only the U.S. shows safe-haven behavior; adverse rhetoric raises Chinese and U.S. trading volume and U.S. volatility.

Partially convertible economies face a market-design problem: trade integration, cross-border investment, and domestic balance-sheet exposure increase the demand for currency hedging before full financial integration is complete. China adopted a distinctive architecture for this problem by fostering a deliverable offshore Renminbi market (CNH) alongside the segmented onshore market (CNY), rather than relying only on non-deliverable forwards. This creates two venues for closely related claims on the same currency. Spot prices are tightly linked, yet CNY and CNH forwards display a persistent and economically large discrepancy. We study that discrepancy in a joint equilibrium model for spot and forward trading with transaction costs and segmented supply. In the benchmark case with common constant supply and deterministic costs, spot parity implies a forward differential with the wrong sign relative to the data. Random offshore stress, modeled as a jump in trading costs, overturns this benchmark while preserving tight spot parity. The model yields a semi-explicit representation in the CNY/CNH application and a calibration of the observed forward discrepancy in terms of the market-implied likelihood and severity of offshore liquidity stress.

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.

Supply chain standardization significantly promotes cross-regional investment by increasing subsidiaries outside headquarters cities, mainly by reducing transaction and information costs and alleviating “outsider disadvantage.” This effect is stronger for non-state-owned firms, firms with lower financing constraints, and those in highly marketized regions. Our findings show that standardization helps overcomeinstitutional and information barriers, optimizing resource allocation. This study expands supply chain governance literature and offers insights for building a unified national market.

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.

A central premise of mandatory disclosure regulation is that greater transparency reduces information asymmetry and lowers borrowing costs. We challenge this premise by examining industry-level operational disclosure - a regulatory form that reveals horizontally comparable information across peer firms rather than refining individual firm fundamentals. Exploiting the staggered introduction of mandatory industry-specific disclosure guidelines by Chinese stock exchanges between 2013 and 2019, we find that enhanced industry disclosure significantly widens bond credit spreads by approximately 54 basis points - the opposite of what standard disclosure theory predicts. This counterintuitive effect is more pronounced in non-homogeneous industries, among smaller firms, and for bonds restricted to institutional investors. Mechanism tests confirm two opposing channels: disclosure reduces information asymmetry while simultaneously intensifying product market competition by exposing strategically sensitive operational metrics. Our evidence challenges the one-size-fits-all approach to disclosure regulation and highlights that the competitive implications of disclosed information - not merely its quantity - shape credit risk pricing.

We propose a clustering-based dimensionality reduction approach to minimum variance portfolio optimization. Rather than constructing the global minimum variance (GMV) portfolio over the full stock universe, which is subject to severe estimation error due to high-dimensionality, we apply Ward’s hierarchical clustering to partition stocks into groups of similarly behaving assets, select one representative per cluster, and optimize on the resulting low-dimensional sub-universe. We show theoretically that clustering preserves the factor structure and yields a better-conditioned covariance matrix than random selection. Empirically, on the Chinese A-share market, the proposed strategies substantially outperform the full-universe benchmark, with gains robust to transaction costs.