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.

This study develops a comprehensive methodological framework for identifying and quantifying investor echo chambers in online stock discussion forums. Motivated by a dynamic model of endogenous echo chamber formation, which formalizes how investors optimally allocate attention and update beliefs under cognitive and informational constraints, we construct a two-layer multiplex investor network that integrates common-attention similarity and semantic similarity to jointly capture the informational and cognitive linkages among investors. This framework enables the systematic examination of how shared information sources and convergent opinions emerge within investor communities. We compute both community-level and individual-level (node-level) echo-chamber intensity by integrating measures of social homophily, semantic reinforcement, and community insularity. At the firm level, we further aggregate these micro-level indicators using attention-weighted indices, community concentration (HHI), and semantic polarization metrics to characterize how echo-chamber dynamics manifest in firm-related discussions. In addition, we propose a general empirical panel framework to examine the relationship between investor echo-chamber intensity and firm-level outcomes. Overall, this paper provides a methodological foundation for the broader Investors’ Echo Chamber Project, offering scalable tools for network-based behavioral analysis and laying the groundwork for future research linking online social dynamics, financial market efficiency, and corporate decision-making.

This study investigates how mutual funds impact the stock market by ana-lyzing the relationship between mutual fund investment behaviours (holding and trading) and stock returns and realized volatility in the Chinese market. It is found that stocks widely held or bought by mutual funds can earn higher excess returns, and more importantly, the trading measures out-perform the holding measures, which is evident by the portfolio analysis and Fama-MacBeth regressions. Moreover, the proportional holding, pro-portional trading and shares trading measures positively and significantly predict future realized volatility. Meanwhile, a weak asymmetric effect in the share-trade measure is found.

This study uses Shanghai Crude Oil Futures (SC) as a proxy for the upstream segment of China’s petrochemical industry and investigates how its market efficiency influences five key downstream product markets. Considering that markets differ in how they absorb information and in their structural features, we employ the Feasible Exact Local Whittle (FELW) estimator to construct a continuous market efficiency index. To capture efficiency dynamics across different time horizons, the study applies the Maximal Overlap Discrete Wavelet Transform (MODWT) to decompose the efficiency series into short-, medium-, and long-term components. These are then examined by Quantile-on-Quantile (QQ) regression to trace the varying marginal effects across different efficiency states. The results reveal strong state dependence and structural differences in the efficiency transmission from SC to downstream markets. Among the five markets, Low-Sulfur Fuel Oil and Asphalt exhibit the most stable transmission patterns, with the former showing a “saddle-shaped” structure and the latter following a “dual-path” pattern. In contrast, the links between SC and the markets for Linear Low-Density Polyethylene and Polypropylene are highly nonlinear and less predictable. Purified Terephthalic Acid demonstrates a dual mechanism of efficiency resonance and long-term anchoring. These findings deepen our understanding of information efficiency within industrial value chains. They also offer practical insights for managing market risk, guiding price policies, and designing regulatory frameworks in the energy sector.

We propose the AI Narrative Gap as a novel firm characteristic—the systematic divergence between a firm’s AI strategic narrative intensity and its subsequent AI capital expenditure commitment—and document its capital market consequences. Using Chinese A-share listed firms from 2015 to 2022, we show that firms with a wider AI Narrative Gap attract significantly more optimistic and less accurate analyst earnings forecasts. These distorted expectations, in turn, predict lower subsequent stock returns, lower industry-adjusted abnormal returns, and weaker future accounting performance. A double-sort portfolio placing firms simultaneously in the highest tercile of the AI Narrative Gap and highest tercile of analyst optimism earns a mean return 22.8 percentage points below that of the lowest tercile on both dimensions (t = −5.10). The return reduction in the AI Narrative Gap coefficient is attenuated but not eliminated after controlling for optimism, consistent with a partial expectation-distortion channel. Collectively, these results establish the AI Narrative Gap as a cross-sectionally informative firm characteristic that captures the credibility of a firm’s AI strategic identity, with systematic implications for analyst expectations and asset prices.

We posit and demonstrate that, in China’s retail-dominated market, quantitative trading over-relies on non-fundamental signals, thereby crowding out fundamental information from stock prices and increasing crash risk. Using trading data from quantitative mutual funds and Chinese A-share firms during 2009-2023, we find that greater exposure to quantitative trading is associated with higher future crash risk. Mediation analysis further reveals that reduced information efficiency constitutes a key channel through which quantitative trading elevates crash risk. The effect is stronger for stocks with more retail investors, consistent with our proposed mechanism. Overall, we identify a novel potential risk of quantitative trading in underdeveloped emerging markets.

Prevailing research, assuming developed financial markets, concludes that Chinese firms heavily rely on “short-term credit for long-term investment.”We challenge this view, arguing that China's vibrant informal financial system provides crucial alternative funding. Consequently, the severity of this maturity mismatch is likely overestimated. To investigate this, we examine strategic alliances as a representative informal institution. Our analysis confirms that such alliances significantly mitigate maturity mismatch, revealing that they enhance information sharing and reduce transaction costs. This provides initial evidence of informal institutions' critical role in addressing this issue. Given the prevalence of similar arrangements in China—like private lending and inter-corporate financing—our findings highlight the need to look beyond formal systems. This perspective not only recalibrates the understanding of corporate financing in China but also opens ample avenues for future research on informal finance's role in emerging economies.

We examine how investors who experienced paper gains during a bubble-crash episode, deemed as investors “escaping from death”, adjusted their future risk-taking. Using detailed transaction-level data and a quasi-experiment based on an unanticipated government intervention in the 2007–08 Chinese stock market, we find that investors who “escaped from death” reduce risk-taking behaviors over the next five years. The evidence shows that the change in risk taking is likely at-tributable to reference-dependent preferences. However, the effect diminishes over time and investors “escaping from death” do not exhibit a diminished tendency toward risk-taking when confronted with a stock market bubble crash again.

This paper examines whether mandatory ownership disclosure aligns incentives or distorts in-vestor beliefs. Using a sample of 1,436 Chinese equity-oriented mutual funds from 2012 to 2023,we find that higher managerial and senior ownership are significantly associated with larger in-flows, suggesting that investors treat ownership as a quality signal. However, we find no evidencethat ownership forecasts superior future returns or risk-adjusted alphas. Mechanism tests showthat the ownership-flow effect is much stronger in low-marketing funds and that managers increaseownership after weak flows, a countercyclical pattern inconsistent with overconfidence and consis-tent with strategic remedial signaling. Overall, ownership disclosure appears to operate primarilythrough investor perception rather than information about managerial ability, weakening the linkbetween capital allocation and true skill in the mutual fund industry.

We examine the information content of the call-put implied volatility spread (IVS) of Shanghai Stock Exchange 50 ETF options. Empirically, the IVS significantly and negatively predicts future SSE50 ETF returns at both weekly and monthly horizons. This predictability is robust both in-sample and out-of-sample, which stands in contrast to prior evidence from the U.S. options market. We explore several potential explanations and show that the IVS is closely linked to the option-cash basis. Its predictability is consistent with the model of Hazelkorn, Moskowitz, and Vasudevan (2023), where the option-cash basis reflects liquidity demand common to both options and underlying equity markets.