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.

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.

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.

This paper constructs a call pressure index (CP) from China's SSE 50 ETF option market and finds a robust U-shaped nonlinear predictability for directional option returns as measured by log returns. The effect reflects that extreme call pressures—whether unusually low (reversal) or high (momentum)—contain information, while moderate levels are dominated by noise trading. Robustness checks using delta-hedged returns confirm that predictability stems primarily from directional exposure rather than volatility dynamics. The predictability is stronger in high-volatility and down-market states and survives controlling for implied skewness, variance risk premium, and other common predictors. A simple timing strategy based on rolling-window forecasts achieves a Sharpe ratio of 0.97, which further increases to 2.43 after applying a prediction threshold. A parsimonious volume-based indicator captures unique predictive information beyond complex proxies, offering a feasible path for emerging markets lacking proprietary order flow data.

This paper revisits two widely studied predictors of commodity futures returns, basis and basis momentum, whose conventional measures using first-nearby futures as proxies for spot prices may limit their ability to capture fundamental spot-market risks. Motivated by this limitation, we construct two spot-based signals from observed spot and futures prices, which are theoretically shown to contain incremental information beyond conventional measures. Using 41 Chinese commodity futures, we find that these signals robustly predict first-nearby contract returns and remain significantly priced in time-series and cross-sectional tests, even after controlling for their conventional counterparts. We then develop a spot-enhanced three-factor model, including the market factor and the two spot-based factors, which consistently outperforms three widely used benchmark models in pricing competing factors and explaining return anomalies.

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.

Technology firms are often financed on narratives about products, contracts, customers, and technological progress well before these developments appear in accounting statements. We ask when such narratives become economically informative. Our central idea is that narratives should matter more once they can be linked to later verifiable outcomes rather than treated as stand-alone text.Using listed Chinese technology firms, we develop a validated corporate narrative framework for bank-affiliated investment, a setting in which investors must screen with soft information ex ante and then monitor hard realization and downside risk ex post. We use GPT-5.1 to extract business claims from management discussion, investor-relations records, exchange Q&A, and earnings-roadshow materials, and to label later claim–evidence pairs as support, partial support, conflict, duplicate, or irrelevant. We then connect these labels to official announcements, procurement awards, permits, project updates, and negative-event disclosures to construct a validated firm-month signal. The broad merged panel contains 592 firms and 30,169 firm-month observations; the main return tests use 576 firms and 18,230 firm-month observations over 2022–2024. A simple production rule that combines a low-narrative-premium component with hard-narrative and hard-event anchors, together with a separate downside-risk gate, delivers an implementable annualized long-short return of 8.93% in bank-invested firms after trading costs. The signal is much weaker in non-bank firms, predicts future gross-margin improvement more strongly than future ROE, and improves downside screening.

This article examines the link between foreign institutional holdings and firms’ efficiency in labor investment in the setting of Chinese markets. We find that foreign institutional investors enhance firms’ labor investment outcomes primarily through mitigating asymmetric information and by strengthening internal governance. Specifically, the influence of foreign institutional investors on a firm’s labor investment efficiency is stronger when the firm faces greater labor adjustment frictions. This effect is more evident when foreign institutional investors are originated from countries or areas with stronger cultural connections to China, stronger governance quality, common law traditions, or stronger bargaining power in the firms’ governance. Our paper contributes to the literature in that it documents the monitoring role of foreign institutional investors from the perspective of firms’ labor investment decisions, and adds to the literature on the drivers of firms’ labor investment choices.

This study investigates the impact of employee ownership on firms’ investment responsiveness to stock market feedback. Using data from Chinese listed firms, we find that employee ownership significantly enhances the sensitivity of corporate investment to Tobin’s q. This effect is more pronounced in firms with higher information asymmetry, greater labor intensity, weaker corporate governance, and higher financial distress risk. While employee ownership is linked to increased subsequent profitability volatility, it also mitigates financial mismatch and downside operating risk. Our findings also suggest that employee ownership drives a shift in corporate strategy, leading to more aggressive approaches and improved risk preferences. These results highlight the role of employee ownership in mitigating agency problems and enhancing firms’ ability to incorporate market information into investment decisions.