This study investigates the pricing of convertible bonds by focusing on the underlying stock price limits, a market structural feature overlooked in conventional models, and provides a comprehensive framework integrating price limits with embedded options (including conversion, call, put and reset clauses) based on Monte Carlo simulation. Numerical analysis reveals that convertible bond prices across varying moneyness states are insensitive to price limits at low underlying stock volatility, while higher volatility amplifies the pronounced impact of price limits on in-the-money bonds. Empirical tests based on data on the Chinese convertible bond market spanning the period from 2018 to 2024 indicate an average overvaluation in the market. The result identifies that there are negative pricing errors for deep-out-of-the-money bonds and positive errors for in-the-money bonds. A trading strategy based on pricing errors yields positive returns, demonstrating the model's practical applicability.

This study explores the determinants of Chatbots adoption for investment purposes using the Unified Theory of Acceptance and Use of Technology (UTAUT). A conceptual framework is proposed, including perceived ease of use (PE), perceived usefulness (PU), social influence (SI), perceived risk (PR), and algorithm aversion (AA). Data from 506 valid responses in China are analyzed using partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANN). Results indicate that: (1) Different groups of people have different perception on five factors. (2) PU and SI exhibit significant positive effects on behavioral intention; PE, PU, and SI exhibit significant positive effects on use behaviors. (3) Neither PR nor AA has significant impacts on intention or behavior. (4) The importance of PU increases 71.48% from intention to behaviors stage. With the findings, we contribute to the technical adoption literature by extending UTAUT, and provide insights for investment Chatbots designers.

We examine the impact of government social media on capital markets using China's staggered adoption of local government microblogs. A difference-indifferences design reveals economically significant increases in firm value, with stronger effects for more content-rich, original, and interactive microblogs. We find improvements in the corporate information environment and reductions in perceived uncertainty, with larger effects for firms with limited external information coverage and in stronger business environments. During the COVID-19 Wuhan lockdown, firms in regions with more active microblogs achieved better stock performance. Overall, government social media is a powerful tool for enhancing transparency, firm valuation, and market resilience.

We study how expanding access to intellectual property (IP)-backed credit affects firm innovation and aggregate total factor productivity (TFP). Exploiting China's IP-backed financing pilot as a quasi-natural experiment, we use difference-indifferences estimates to show that the policy increases R&D participation, raises firm-level productivity, and reduces capital misallocation. To interpret these effects and quantify their aggregate implications, we develop a dynamic model of heterogeneous firms that borrow subject to collateral constraints backed by tangible assets and IP, and choose R&D investment and the accumulation of wealth. R&D raises future productivity and creates collateralizable IP. We estimate the model by the method of simulated moments, using the policy-induced shift in R&D participation to identify the change in IP pledgeability. The estimated model implies that a nationwide adoption of IP-backed financing would raise long-run TFP by 14%, with roughly two-thirds coming from static gains due to improved capital allocation and the remainder from dynamic gains driven by higher innovation that accumulate over time. Relative to a comparable expansion of lending backed by tangible collateral, IP-backed financing delivers larger productivity gains by easing constraints for high-productivity but low-wealth firms and by strengthening incentives to accumulate IP as collateral for future borrowing.

This study evaluates whether electric vehicles (EVs) in China have delivered on their “green” promise by improving air quality and reducing private ownership costs. Combining city–month data on EV adoption and air pollution with detailed vehicle-level transaction records, we find little evidence that rising EV adoption reduces concentrations of major pollutants or greenhouse gases. Both the traditional instrumental variable (IV) and the Bartik IV estimations show that the null effects persist after addressing potential endogeneity. The limited environmental gains are largely attributable to the continued dominance of coal in electricity generation and to a sizable rebound effect whereby lower operating costs increase vehicle usage. On the private side, although EVs reduce energy expenditures by roughly CNY 16,747, their faster depreciation leads to an average net loss of about CNY 18,175 over the ownership period relative to fuel vehicles (FVs). Counterfactual simulations show that EVs match the emissions of fuel vehicles when the coal share falls to about 55%, deliver measurable benefits below 49%, and require a reduction to around 31% once higher utilization is considered. The results show that the environmental benefits from EV adoption depend on a transition toward cleaner electricity generation.

This paper examines how traditional informal institutions shape preferences for old-age support and the emergence of initiative-based long-term financial planning. Using data from the China Health and Retirement Longitudinal Study combined with clan genealogy records, we estimate the causal impact of exposure to clan culture on old-age support preferences. We find that stronger exposure to clan culture in early life is associated with a higher likelihood of adopting modern preferences for old-age support, characterized by greater reliance on self-initiated savings and pension participation rather than dependence on children. The results are robust to instrumental variable estimation and placebo test. Importantly, the effect operates exclusively through exposure to clan culture during early life, while clan culture encountered in adulthood has no significant influence, particularly among individuals who relocate later in life. Mechanism analysis suggests that peer effects, generalized trust, income growth, and financial literacy jointly mediate the relationship between clan culture and old-age support preferences. We further show that the expansion of the formal pension system complements, rather than crowds out, the influence of clan culture, consistent with a process of co-evolution between informal and formal institutions. The effects are stronger among individuals with favorable attitudes toward clans, rural residents, and men. Finally, modern preferences induced by clan culture translate into higher pension participation and larger financial asset holdings. Overall, the findings highlight how informal institutions can shape economic preferences and facilitate the adoption of formal financial arrangements.

This study examines how institutional entrepreneurs in China’s aviation sector navigate the dual pressures of domestic carbon emission trading schemes (ETS) and the global Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA). Using qualitative case study methods, including 24 elite interviews with aviation executives, regulators, and carbon market experts, we analyse the role of China Southern Airlines (CSA) as a key actor in sustainability transition. Findings reveal that field-level contradictions, social legitimacy, and collective institutional entrepreneurship shape the adoption of carbon reduction strategies. The study contributes to aviation environmental and sustainability policies and institutional theory by highlighting the interplay between regulatory compliance and global sustainability norms in emerging economies.

The global financial system is currently navigating a profound transformation driven by digitalization and the rise of decentralized financial innovations. In response, central banks are increasingly exploring or implementing Central Bank Digital Currencies (CBDCs) as a sovereign digital evolution of fiat money. This study investigates the dual nature of CBDCs, evaluating whether they represent a strategic opportunity to modernize the economy or a systemic threat to existing financial stability. Employing a qualitative methodology, the research analyzes official policy frameworks from the IMF and BIS alongside diverse real-world case studies, including China’s e-CNY, the Bahamas’ Sand Dollar, Nigeria’s eNaira, and the European Central Bank’s Digital Euro. The findings suggest that while CBDCs offer significant benefits—such as enhanced payment efficiency, reduced transaction costs, and improved financial inclusion—they also introduce critical risks. These include the potential disintermediation of commercial banks, heightened cybersecurity vulnerabilities, and concerns regarding individual data privacy and government surveillance. The study concludes that the successful integration of CBDCs is not merely a technical challenge but a social and strategic one. Adoption is heavily dependent on infrastructure, digital literacy, and public trust. Ultimately, the research highlights that there is no "one-size-fits-all" model; the future of money will be shaped by how effectively individual nations balance technological innovation with the preservation of financial architecture.

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.

The global AI inference market—reaching approximately $90–100 billion annually and growing at 18% CAGR—operates without organized exchange infrastructure. We document three market failures: resource misallocation (80% of China’s newly built compute capacity sits idle), price opacity (100-fold price dispersion across providers of equivalent quality), and unhedged risk exposure (85% of enterprises miss AI cost forecasts by more than 10%). Following the market design tradition of Roth [2002] and Budish et al. [2015], we propose the AI Service Right (ASR) as a transferable property right on AI compute and the AI Service Unit (ASU) as a quality-adjusted, cross-platform unit of account grounded in hedonic price theory [Rosen, 1974]. The ASU is modality-neutral: billing prices across text, image, video, and speech modalities are unified via eq-token conversion factors (κimg ≈ 2,667 eq-tokens per image; κvid ≈ 2,667 per second of video; κspc ≈ 7 per second of audio), and modality-appropriate benchmark sets (MMLU/HumanEval for language; FID/CLIP Score for image; FVD/CLIPSIM for video; MMBench for multimodal) supply the quality in dex via PCA. We design a hybrid secondary market architecture synthesizing mechanisms from four orthogonal market traditions: foreign exchange markets (cross-platform exchange rates and PPP-analog arbitrage via the ASU); equity markets (Central Limit Order Book, market making, clearing); electricity markets (Compute Locational Marginal Pricing for spatial scarcity signals); and decentralized finance (Automated Market Maker for long-tail liquidity). We establish nine formal propositions: bilateral trading is generically inefficient; Compute Locational Marginal Pricing decomposes nodal prices into system marginal cost, capacity congestion, and bandwidth premia; no-arbitrage equi librium holds with capital constraints (extending Shleifer and Vishny 1997); the ASR market Pareto-improves over bilateral trading; market prices are more in formative under ASR; the hybrid CLOB-AMM architecture weakly dominates either mechanism alone; platform adoption admits multiple equilibria with a coordination trap; financialization may improve or reduce price informativeness depending on speculator-hedger composition; and a hedonic micro-foundation justifies the ASU definition. Calibrated agent-based simulation (500 steps, 30 Monte Carlo runs) provides computational validation: the hybrid architecture reduces price dispersion by 90% relative to bilateral trading, and order-of-magnitude welfare estimates suggest enterprise procurement cost savings of 0.2–20% (net of ASR transaction costs; see Table 7) and potential TFP gains from compute reallocation of up to $29.9 billion annually. We propose a phased implementation roadmap from shadow ledger to full financialization, and we engage critically with the concern that financialization may not reduce intermediation costs [Philippon, 2015].