This study investigates the relationship between the number of stock concepts and future returns, as well as the economic mechanisms underlying this association. Using novel collected data, we find that stocks with a greater number of concepts earn significantly higher returns in the subsequent month, generating a six-factor adjusted annualized alpha of approximately 9.6% for a long-short portfolio. Although these stocks exhibit higher turnover, return volatility, and investor attention - features commonly associated with speculative concept-driven trading - an analysis of cross-listed AH twin stocks reveals that concept counts do not widen the AH premium. Moreover, the return premium persists for up to 15 months without significant reversal, whereas firms that engage in opportunistic concept-chasing exhibit pronounced long-run reversals, suggesting that the positive concept-return relation is not driven by speculative motives. The higher returns are primarily attributable to strong industrial policy support and sustained above-expectation operating performance. Firms with more concepts are more likely to receive government subsidies and deliver positive earnings surprises, effects that are amplified when their core concepts receive stronger national policy backing. In contrast, firms that pursue concepts lacking substantive business relevance are significantly less likely to obtain government subsidies and fail to achieve above-expectation growth. Regarding investor composition, institutional ownership of high-concept stocks increases modestly, while ownership by government-guided funds rises substantially - particularly for stocks whose core concepts are strongly supported by national industrial policies. Conversely, concept-chasing behavior by listed firms significantly reduces the ownership ratio of government-guided funds. Overall, our findings indicate that concept stocks in China’s capital market are not purely speculative but signal underlying national policies.

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.

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].

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.

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.

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.

Against the backdrop of global green development and China’s sustainable economic transition, many firms exaggerate green-transition disclosures to cater to national strategies and capital market preferences, leading to a severe "Say–Do Gap". Based on signaling theory, this study uses the phased establishment of environmental courts in 208 prefecture-level cities as a quasi-natural experiment, adopting a staggered DID design with 2007–2023 panel data of Chinese A-share listed firms for empirical tests. Results show widespread corporate green pandering, with improved disclosure not translating into actual carbon reduction. Environmental courts effectively curb this behavior, with environmental litigation risk as the core mediating channel. Heterogeneity tests reveal stronger deterrence in regions with weaker regulation/heavier pollution and polluting firms with stronger environmental technology. This study enriches literature from a judicial deterrence perspective and provides implications for substantive corporate green transition.

We investigate how the loss of informative salient signals in financial markets influences stock return volatility, using the 2024 intraday disclosure reform of the mainland China-Hong Kong Stock Connect program as a natural experiment. The reform eliminated the real-time disclosure of northbound capital (NC) flows on trading platforms, rendering NC trading information invisible to Chinese investors during market hours. We find that the removal of NC signals induces increased investor belief dispersion and intensifies informed trading, thereby amplifying intraday volatility in NC-eligible stocks. Moreover, this effect is more pronounced for stocks with higher investor attention, indicating that attentive investors suffer stronger anchor loss when NC signals disappear. In contrast, lottery-type stocks and stocks with alternative NC trading clues exhibit weaker volatility responses, since the presence of strong alternative signals reduces the effect of NC signal loss. These findings highlight the informational role of insightful salient signals in stabilizing stock returns.

How do global and domestic investors process institutional signals in emerging markets? We use China’s refined-oil pricing announcements as institutional communications to construct high-frequencymarketization surprises as deviations between actual prices and formula-implied expectations (2013–2025). Three heterogeneous patterns emerge. First, a 1% deviation toward weaker marketization triggers $30m equity and $10m bond outflows internationally while domestic futures appreciate. Second, Kalman filtering extracts latent institutional information differing across markets, with near-zero correlation. Third, international responses amplify quarterly while domestic dissipate immediately. A+H dual-listed firm analysis reveals implicit guarantees and market segmentation jointly drive this divergence.