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].

Do investors respond to what fund managers say, or how they look saying it? Using 2,000 live-streamed sessions by Chinese ETF managers and multimodal machine learning, we show that managers’ facial expressions, not their words, drive fund flows. A one-standard-deviation increase in positive facial affect raises next-day flows by 0.17pp (260% of mean). Vocal tone shows weak effects; textual sentiment shows none. Critically, facial expressions predict flows but not returns, indicating pure persuasion rather than information transmission. Effects strengthen when investors are emotionally vulnerable (down markets, retail-heavy funds) and persist 2-3 weeks before dissipating. Our findings challenge the emphasis on textual disclosure in finance and raise questions about investor protection as video communication proliferates.

With the digital transformation of the capitalist economy, some contemporary scholars have put forward the Techno-Feudalism narrative of the digital economy. This narrative emphasizes that digital platform enterprises, as emerging market entities in the digital economy, have many practices that are highly similar to those of feudal lords. For example, digital platform enterprises plundering user data is similar to feudal lords plundering land; digital platform enterprises collecting digital rent is similar to feudal lords collecting land rent; digital platform enterprises controlling users and workers is similar to feudal lords controlling slaves. However, this narrative has many theoretical fallacies. Marx's theory of surplus value shows that the above phenomena are essentially still the contemporary form of capital seizing surplus value through technological innovation. The techno-feudalism narrative ignores the internal logic of capital using technological iteration to reconstruct the exploitation mechanism and falls into a superficial misjudgment. In contrast, the Chinese governance practice of digital economy breaks the monopoly of platforms on data elements through the innovation of the separation of three rights of data property rights; promotes fair competition and optimal allocation of resources in the digital economy by strengthening anti-monopoly supervision and promoting the construction of digital infrastructure; proves that the socialist system can break the capital proliferation cycle and achieve "people-centered" development by building a labor rights protection system to promote the creation and sharing of value and transcending the techno-feudalism phenomenon of the digital economy.

Green bonds, pivotal to green finance, draw growing attention from scholars and investors. Social media’s proliferation has amplified the influence of investor sentiment, necessitating robust analysis of its market impact. However, general sentiment lexicons often fail to capture domain-specific slang and nuanced expressions unique to China’s bond market, leading to inaccuracies in sentiment analysis. Thus, this study constructs a specialized sentiment lexicon for the green bond market, namely the COBC (Chinese online bond comments sentiment lexicon), to dissect bond market slang and investor remarks. Compared to three general lexicons (Textbook, SnowNLP, and VADER), it improves the average prediction accuracy by approximately 87.2% in sentiment analysis of Chinese online language within the green bond domain. Sentiment scores derived from COBC-based dictionary analysis are systematically integrated as predictive features into a two-stage hybrid predictive model is proposed integrating Support Vector Machine (SVM), Auto-Regressive Moving Average (ARMA), Bidirectional Long Short-Term Memory Networks (BiLSTM), and Attention Mechanisms to forecast China's green bond market, represented by the China Bond 45 Green Bond Index. First, ARMA-SVR is employed to extract residuals and statistical features from the green bond index. Then, the BiLSTM-Attention model is applied to assess the impact of investor sentiment on the index. Empirical results show that incorporating investor sentiment significantly enhances the predictive accuracy of the green bond index, achieving an average of 67.5% reduction in Mean Squared Error (MSE), and providing valuable insights for market participants and policymakers.

Recent studies highlight the resurgence of state capitalism, with the state increasingly acting as equity investors in private firms. Why do state--business equity ties, including partial and indirect state ownership in private firms, proliferate in weakly institutionalized contexts like China? While conventional wisdom emphasizes state-driven explanations based on static evidence, I argue that regulatory risk reshapes business preferences, prompting firms to seek state investors and expanding state--business equity ties. These ties facilitate information exchange and signal political endorsement under regulatory scrutiny. Focusing on China's crackdown on the Internet and IT sectors, difference-in-differences analyses of all investments from 2016 to 2022 reveal a rise in state--business equity ties post-crackdown. In-depth interviews with investors along with quantitative analysis, demonstrate that shifts in business preferences drive this change. This study shows the resurgence of state capitalism is driven not only by the state but also by businesses in response to regulatory risks.

As digital technologies proliferate, firms increasingly leverage digital transformation strategically, necessitating new orientations attuned to digital technological change. This study investigates how digital orientation (DORI)- the philosophy of harnessing digital technology scope, digital capabilities, digital ecosystem coordination, and digital architecture configuration for competitive advantage – influences firms’ environmental, social, and governance performance (ESG_per). Analysis of Chinese A-share firms from 2010-2019 reveals DORI is associated with superior ESG_per, operating through the mediating mechanism of enhanced digital finance (DIFIN) as a fund-providing facilitator for sustainability initiatives. Additional analysis uncovers important heterogeneities – private firms, centrally owned state-owned enterprises, politically connected, and emerging companies exhibit the strongest DORI - ESG_per linkages. Prominently, the study findings are validated through a battery of robustness tests, including instrumental variable methods, and propensity score matching. Overall, the results underscore the need for firms to purposefully develop multifaceted digital orientation and furnishes novel theoretical insights and practical implications regarding DORI’s role in improving ESG_per.

Software and accounting advances have led to a rapid expansion in and proliferation of loyalty tokens, typically bundled as part of product price. Some tokens, such as in the airline industry, already account for tens of billions of dollars and are a major contributor to revenues. An open question is whether, as technology evolves, firms will have a strong incentive to make loyalty tokens tradable, raising regulation issues, including with monetary and banking authorities. This paper argues that for the vast majority of tokens, issuing firms have a strong incentive to make them non-tradable. The core incentive for token issuance here is that an issuer can earn a higher rate of return on the ``float'' (tokens issued but not yet used) than its retail customers can, much like a bank. Our main finding is that an issuer earns higher revenue by making tokens non-tradable even though the consumer would be willing to pay a higher price for tradable tokens. We further show that an issuer with stronger market power tends to allow more frequent token redemption, and its revenue is more token-dependent. We test the model's predictions with data on airline mileage and hotel reward programs and document consistent empirical results that align with our theory.

By using a proprietary real–time daily online sales data collected in China from 10–billion consumer accounts, this paper ffnds that the ffrm–level daily online sales growth (DOSG) can positively predict future one–day to more than three–month cumulative stock returns in the cross section, implying a growth premium in contrast to Lakonishok, Shleifer, and Vishny (1994). A spread portfolio that is long on stocks with high DOSG and short on stocks with low DOSG delivers an abnormal return of around 30 basis points per week. DOSG derives its short–run (e.g., weekly) predictability from investor sentiments, tilting to a behavioral explanation. However, it derives its medium to long–run (e.g., three–month) predictability from fundamentals, voting for a rational explanation. Our further evidence indicates that stocks with high DOSG experience more intensive information acquisition from retail investors and less severe crash risk, implying online sales as a channel for retail investors to get access to daily real–time ffrm fundamentals.

尽管可转换公司债券在改善无效投资方面具有理论上的优良性质，但对其治理功能 的实证考察却十分缺乏。以Shleifer（1989）的分析框架为基础，本文所构建的研究模型着 重于分析可转债对无效投资的治理作用及可转债各具体条款设计与投资效率改善之间的关 系。结果表明，可转债能够实现对公司无效投资的双向治理。以2000至2008年间我国可转债 发行公司为样本所进行的实证检验也部分支持了上述理论分析的结论。具体来说，可转债的 发行能够显著改善公司的投资不足问题，但对于过度投资的制约却表现为短期效应。各具体 条款对投资效率改善的作用并不明显，只有赎回条款能有效缓解公司的投资不足行为，而向 下修正条款的作用则依赖于其表决机制。

We investigate the choice problem in the massive Chinese restructuring campaign that has been described as “grasping the large and letting go of the small,” in which a third of the million or so Chinese state-owned enterprises were either corporatized or privatized. Corporatization differs from privatization in the Chinese context, as in the former case the state remains a large shareholder, whereas in the latter case it has little or no ownership. Using a panel of provincial level statistics, we show that greater local employment pressure, less local fiscal pressure, and a more corrupt local business environment all lead to a lesser likelihood that privatization will be chosen over corporatization. Privatization is found to yield consistent efficiency gains over corporatization in terms of employment and firm profitability. Our evidence is supportive of the theoretical framework of Boycko, Shleifer, and Vishny (1996), who model privatization as an endogenous decision in which politicians trade off employment pressure against public fiscal interest.