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].

山江湖工程实施四十余年来，江西在生态制度创新方面积累了深厚土壤，但鄱阳湖湿地生态补偿仍面临"拨付滞后、标准与贡献脱钩、用途监管难"三大痛点。本文基于数字人民币2.0"账户体系+币串+智能合约"技术架构，设计鄱阳湖湿地碳汇"按效付费"智能合约方案。针对鄱阳湖碳汇显著的源汇动态转换与年际波动特征，提出"三年滚动平均+气候调整因子"的核算机制与"智能合约计算、人工确认释放"的半自动触发模式，以永修县40公顷湿地为最小可行试点（MVP），设定50元/吨CO₂当量的补偿单价与500吨/年的增量阈值，构建"碳汇监测—滚动核算—合约计算—人工确认—资金直达—用途管控"的闭环。本文明确央行"平台提供者+融资支持者"的边界，建立"央行建平台、银行做应用、地方配设备"的成本分担机制，确保补偿资金全额用于生态保护，并给出从MVP验证到全湖区推广的渐进路径。

Hyman Minsky (1919–1996), a prominent post-Keynesian economist, argued that capitalist financial systems are inherently unstable. During prolonged prosperity, firms and financial institutions increase leverage and adopt more fragile forms of financing, shifting from hedge to speculative and Ponzi finance. This gradual buildup of financial fragility can eventually trigger a sudden collapse of asset values—later termed a “Minsky moment.” After the 2007–2009 global financial crisis, Minsky’s ideas gained renewed attention, and current financial developments once again bring his insights to the forefront.

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.

This paper examines the use of finance leases in China’s local government debt. Using a unique dataset of government finance lease transactions, we document that local government financing vehicles (LGFVs) rapidly adopted finance leases, with the outstanding amount growing from virtually nothing in 2013 to a cumulative total of 1.02 trillion RMB by 2018. Our difference-in-differences (DID) analysis reveals that the central government’s restrictive financial policies account for a substantial portion of this surge. Because these restrictive policies confined LGFVs’access to conventional borrowing channels, finance leases emerged as a key alternative, particularly through bank-affiliated leasing firms. While LGFVs' use of finance leases offers low-cost financing for local governments, the low quality of the underlying assets poses significant risks to the leasing firms.

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.

Whether risk aversion is time varying and countercyclical is central to modern asset pricing, yet evidence remains limited and is based mainly on experimental, survey, or aggregate stock market data. We provide individual-level evidence from 10 million Chinese auto insurance contracts from 2011 to 2017, estimating policyholders’ risk aversion from deductible choices. We find that risk aversion is time varying and countercyclical. The estimates are negatively related to lottery and stock trading, positively related to insurance sales and bond trading, and vary with psychological factors, including seasonal mood, “zodiac year,” and calendar events.

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.