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.

Under China’s transition toward high-quality financial development, this study investigates the tail dependence among media sentiment, investor attention, and stock market returns using the M-Clayton Copula. Comparative model fitting tests demonstrate that the M-Clayton Copula outperforms both single Copula models and M-Copula models in characterizing asymmetric negative dependence structures. It is particularly effective in capturing both upper-lower and lower-upper tail dependencies, thereby providing a more comprehensive analysis of their interdependencies. The empirical results reveal three key findings. First, a significant positive tail dependence exists between media sentiment and stock market returns, suggesting that they tend to move together under extreme conditions, with notable asymmetry in the strength of co-movements. Second, media sentiment and investor attention exhibit negative tail dependence, with the lower-upper tail dependence coefficient exceeding its upper-lower counterpart, suggesting higher probability of rising investor attention following media sentiment decline than vice versa. Third, a pronounced negative tail dependence emerges between stock market returns and investor attention, particularly showing strong lower-upper tail correlation, implying substantial likelihood of increased investor attention subsequent to market downturns.

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.

Traditional information asymmetry theories suggest that greater corporate transparency should reduce investor disagreement. Using Chinese mutual fund holdings, we document the opposite pattern: transparency amplifies disagreement among institutional investors. Mechanism tests show that transparency discourages herding while intensifying private information acquisition among fund managers. The effect is stronger for growth-oriented and high-skill funds, and during periods of elevated market sentiment, and among firms with lower credibility, excessive disclosure frequency, and greater investor attention. Further analysis indicates that this transparency-induced disagreement stems from informed trading rather than noise, thereby enhancing price informativeness and market efficiency. Overall, the evidence reveals the dual nature of transparency as both an informational input and a behavioral catalyst that increases disagreement in financial markets.

Cross-firm momentum effects via shared analyst coverage are well-documented in de-veloped markets, but their robustness remains unclear in emerging markets, where information diffusion is asymmetric and analyst coverage is highly concentrated. Our work revisits this effect in an environment of extreme informational frictions — the Chinese market. We reconstruct the information transmission channel within the an-alyst coverage network by introducing a novel weighting scheme based on strength centrality (SC). This measure identiffes inffuential leader firms that command dis-proportionate attention from both analysts and the market. Our results demonstrate that SC-weighted connected-firm returns robustly predict cross-sectional stock returns, yielding significant and persistent profits even under a rigorous stock filter. This per-formance cannot be subsumed by strategies based on alternative weighting schemes or by explanations such as intra-industry cross-firm momentum and information discreteness. Further analysis reveals that the superiority of the SC-based approach stems from its ability to effectively identify firms with stronger cross-period fundamental linkages. In addition, high-SC stocks are characterized by higher investor attention, more efficient information processing, lower arbitrage costs, and greater internationa exposures. With this evidence, we further confirm a directional spillover: cross-firm momentum effects flow exclusively from these high-SC leaders to low-SC laggards, and there is no reverse spillover. Our findings suggest that cross-firm momentum may be systematically underestimated in many international markets due to methodological limitations rather than economic irrelevance. The SC-based framework therefore of-fers a portable tool for global investors and researchers operating in environments with asymmetric information.

We introduce a real-time dispersion measure based on cross-sectional stock returns explicitly designed to capture retail-driven speculative episodes. Elevated return dispersion effectively identifies periods characterized by intensified retail investor trading behaviors, driven by salience, diagnostic expectations, and extrapolative beliefs. During these high-dispersion states, momentum strategies collapse, and short-term reversals become dominant. Conditioning momentum strategies on our dispersion measure resolves the longstanding puzzle of missing momentum in retail-intensive markets such as China, substantially enhancing profitability. A dynamic rotation strategy between momentum and short-term reversal portfolios guided by dispersion states achieves annualized Sharpe ratios nearly double those of static approaches. Extending our analysis internationally, we employ Google search trends as proxies for retail investor attention, confirming that dispersion robustly predicts momentum and reversal returns globally. Our findings underscore the behavioral channel through which retail-driven speculation conditions momentum dynamics, providing clear implications for dynamic portfolio management strategies.

We develop a deep-visualization framework for timing the factor zoo. Historical factor return trajectories are converted to two complementary image representations, which are then learned by convolutional neural networks (CNNs) to generate factor-specific timing signals. Using 206 equity factors, our CNN-based forecasts deliver significant economic gains: timed factors earn an average annualized alpha of about 6\%, and a high-minus-low strategy yields an annualized Sharpe ratio of 1.22. The outperformance is robust to transaction costs, post-publication decay, and factor category-level analysis. Interpretability analyses reveal that CNNs extract predictive signals from path boundaries and regime shifts, capturing patterns orthogonal to investor attention.

This study aims to examine the causal effect of carbon regulatory risk on corporate bond yield spreads in emerging markets through empirical analysis. Exploiting China's commitment to peak CO2 emissions before 2030 and achieve carbon neutrality before 2060 as an exogenous shock to an unexpected increase in carbon regulatory risk, we perform a difference-in-difference-in-differences (DDD) strategy. We find that exposure to carbon regulatory risk leads to an increase in bond yield spreads for carbon-intensive firms located in regions with stricter regulatory enforcement. This positive relationship is more pronounced for firms with financing constraints, belonging to more competitive industries, and located in regions with a high marketization process. We further identify that higher earnings uncertainty and increased investor attention serve as two mechanisms by which carbon regulatory risk influences the yield spreads of corporate bonds. Moreover, the spread decomposition reveals that the rise in bond yield spreads after an increase in carbon regulatory risk is primarily driven by the rise in default risk rather than the rise in liquidity risk. Overall, our findings highlight the importance of considering carbon regulatory risk exposure in financial markets, especially in developing economies like China.

We examine how social networks shaped by high-speed rail connections influence investor attention and market reactions to earnings announcements in China. Firms in high-centrality cities exhibit stronger immediate and subsequent responses in investor attention, stock price, and trading volume to earnings news. Further analysis shows that earnings-induced local attention predicts future attention spillovers to intercity investors, amplifying both price and volume reactions after announcements. Overall, these findings indicate that high-speed rail networks foster investor social networks that facilitate the dissemination of firm news and help explain predictable patterns in investor behavior and market pricing.

We introduce a hypergraph-based approach to analyze information flow and investor attention transfers through news outlets in financial markets. Extending traditional graph models that focus on pairwise interactions, our hypergraph framework captures higher order relationships between firms that are simultaneously mentioned in the same news article. We develop a random walk based centrality framework that considers both the properties of the hyperedges (news articles) and the nodes (firms). This framework allows us to more accurately simulate investor attention flows and to incorporate different theories of investor behavior, such as category learning and investor attention theory. To demonstrate the effectiveness of our attention centrality, we apply it to the Chinese CSI500 market index from 2016 to 2021, where our centrality measures improve the prediction of future returns, with improvements ranging from 6.3% to 14.0% compared to traditional graph-based models. This improvement implies that our centrality measure can better capture investor attention transfers on the news hypergraph. In particular, we find that investors pay more attention to news that covers both a greater number of firms and firms on which the sentiments are more negative. Although we focus on financial markets in this research, our hypergraph framework holds potential for broader applications in information systems — for example, in understanding social or collaboration networks.