The irrational feedback trading behavior in the crude oil markets remains puzzling for academic communities and market professionals. This study explores the time-varying features of feedback trading in the four major crude oil futures markets. We find the popularity of positive feedback trading in the WTI, Brent and DME Oman futures markets, and that of negative feedback trading in the China SIEE futures market. However, substantial oscillations of feedback trading strategies occur over time, where positive and negative feedback trading strategies cluster during miscella- neous market crises. Further, risk aversion plays a dominant role in the dynamic mean and volatility transmissions with feedback trading in most of the crude oil markets. The dominance of risk aver- sion is more pronounced during the tumultuous periods such as GFC and COVID-19 pandemic. Our findings render important implications for investors on containing irrational feedback trading.

Using 4724 A-share listed firms in the Chinese stock market from 2009 to 2024, we investigate how CPU, an index constructed from textual analysis of major Chinese newspapers, affects investors' behavior, measured by turnover ratios and abnormal returns. The baseline regression shows that higher CPU significantly reduces stock turnover ratio and abnormal returns, indicating heightened investor caution and negative sentiment. Extended regression with provincial-level CPU further supports these findings. Heterogeneity analysis reveal that low-carbon firms, non-state-owned enterprises (non-SOEs), and firms in Central and Western China are more negatively affected by CPU, while high-carbon firms and SOEs are relatively resilient when facing higher CPU, mainly due to expected policy support and institutional investor dominance. Further mechanism analysis uncovers that ESG performance moderates CPU's effects. Firms with higher ESG ratings experience smaller valuation declines but lower trading volumes. In addition, consumer sentiment mediates CPU's effects. CPU suppresses public confidence, which in turn reduces investor participation and firm valuations. These findings highlight the importance of improving policy transparency, promoting standardized ESG disclosure, and mitigating retail investor overreaction. The study contributes to behavioral and climate finance literature by linking policy-driven uncertainty with micro-level investor responses in a key emerging market.

Existing literature questions whether mutual fund holdings convey information for predicting stock returns in emerging markets. We argue that this lack of evidence stems from a methodological limitation: conventional value-weighted holding measures are dominated by a few large funds, obscuring the collective signal from numerous smaller, but potentially more informed managers. This scale-induced distortion masks the true breadth of institutional attention. To address this, we propose the Abnormal Fund Holding Count (AFHC), defined as the residual from regressing the log number of funds holding a stock on controls for size, value, and liquidity. Using Chinese mutual fund data from 2005 to 2023, we show that AFHC strongly predicts future returns: a long-short portfolio based on AFHC earns an equal-weighted monthly alpha of 1.13\% (t = 4.21), robust to risk adjustments and controlling related return predictors. Moreover, high-AFHC stocks exhibit subsequent improvements in fundamentals and greater price informativeness, consistent with a price discovery channel. Our findings demonstrate that institutional breadth, rather than aggregate capital weight, better reveals informed trading in developing markets.

Purpose-This study empirically examines the relationship between the blockholder exit threats (ETs) and management tone manipulation, based on two competing hypotheses: the "governance hypothesis" and the "market pressure hypothesis." The study aims to explore the impact and mechanisms of ETs on the quality of textual information disclosure. Design/methodology/approach-This study uses ordinary least squares multivariate regressions on panel data from Chinese A-share listed companies spanning 2007 to 2022. Building on Huang et al. (2014), we construct a benchmark model of managerial tone and use its residuals to measure abnormal positive tone. Blockholder exit threat (ET), following Dou et al. (2018), is proxied by the interaction between stock liquidity and blockholder competition. To address endogeneity, we employ propensity score matching, the Heckman two-stage method and lagged models. Robustness checks include alternative variable definitions and different textual dictionaries. Findings-The results show that external blockholder ETs significantly increase the degree of abnormal positive managerial tone, thereby supporting the market pressure hypothesis. Further analyses reveal that the positive association between ETs and abnormal positive tone is stronger in subsamples where managerial market pressure is higher, the level of blockholder expropriation is greater and insider trading is more prevalentindicating a stronger collusion motive between external blockholders and management. Mechanism tests show that ETs manipulate managerial tone by increasing the sensitivity of managerial wealth to share price fluctuations. Research limitations/implications-This study is limited to Chinese A-share listed companies, whose unique institutional and regulatory environment may restrict the generalizability of the findings to other markets. Differences in disclosure rules, legal enforcement and investor protection between China and developed economies suggest that the identified mechanisms may not apply universally. Additionally, our reliance on annual report narratives for measuring managerial tone may overlook other communication channels. Future research could extend the analysis to other market segments or countries and incorporate additional data sources to test the robustness and broader applicability of our conclusions. Practical implications-The results highlight the need for enhanced regulatory oversight of narrative disclosures to prevent managerial “window dressing” and mitigate information asymmetry. Regulators should strengthen cross-validation between financial and non-financial information and promote mechanisms that increase the cost and risk of blockholder–management collusion. Investors are advised to critically assess managerial tone and remain alert to overly optimistic language. Additionally, increased supervision of tunneling and insider trading, along with stronger legal protections for minority shareholders, is essential for fostering transparency and integrity in China’s capital markets and improving overall market quality. Originality/value-This paper contributes to understanding the impact of external blockholder ETs on managerial tone manipulation in narrative disclosures from the perspective of ownership structure in China’s unique institutional environment. The findings of the study provide new insights into the governance role of blockholders and offer policy implications for improving information transparency and the healthy development of emerging capital markets.

This study examines whether stock price fragility—the risk of price volatility arising from non-fundamental demand shifts, such as investor liquidity shocks—affects investment efficiency in real-economy firms. Our research indicates that firms facing higher price fragility exhibit significantly weaker investment performance. Price fragility amplifies noise trading and worsens information asymmetry, undermining the capacity of prices to convey reliable signals for managerial decisions. Further analysis reveals that financing constraints exacerbate the impact of stock price fragility on under-investment, whereas managerial overconfidence plays an asymmetric moderating role, mitigating under-investment while amplifying over-investment. Evidence from a natural experiment–based identification strategy supports a causal interpretation of our results and alleviates endogeneity concerns.

This study utilizes the broadcast of the popular television series Game of Hunting in mainland China as a quasi-natural experiment to examine how investor attention impacts stock price volatility. The empirical findings reveal that the broadcast of Game of Hunting increased stock price volatility for headhunting companies. Notably, headhunting companies that attracted lower levels of investor attention before the broadcast exhibited greater stock price volatility. Conversely, headhunting companies with more complex business operations, which posed greater challenges for investors in processing information, experienced smaller fluctuations in stock prices. The broadcast of Game of Hunting drew retail investors’ attention to headhunting stocks, prompting them to purchase these stocks. This attention-driven buying then triggered arbitrage behavior by other retail investors, who sold the stocks, ultimately causing fluctuations in the share prices of headhunting companies. In contrast, institutional investors’ trading of these stocks was neither affected by the popularity of Game of Hunting nor influenced by the trading behavior of retail investors.

This study examines the relationship between price discovery and commodity-related media sentiment in the Chinese futures market through a tone-based natural language processing framework. We integrate a customized Chinese BERT model into a BiLSTM network with an Attention mechanism, enhancing semantic representation through contextual embeddings and salient feature emphasis. Analyzing over 850,000 news articles, we find sentiment provides significant incremental predictive power for commodity portfolio returns. We finally design a long-short trading strategy that consistently outperforms several benchmark approaches. The results emphasize that deep semantic sentiment analysis offers substantive predictive value for asset returns.

Investor disagreement is central to theories of trading, yet existing proxies are either low-frequency or depend on platform-specific investor labels. We develop a daily, replicable measure of disagreement for unlabeled social media and examine its relation to trading activity in China's A-share market. Using 52.3 million investor comments on CSI 300 constituents from 2016 to 2025, we implement an LLM-based stance-simulation framework in which neutral, bullish, and bearish agents generate annotated training data that are then scaled to the full corpus using three parallel pre-trained language model classifiers. This design yields two distinct disagreement measures: within-group disagreement, capturing dispersion within a shared stance-conditioned interpretation, and cross-group disagreement, capturing divergence across stance-conditioned interpretations. We find that within-group disagreement is negatively associated with abnormal trading volume, whereas cross-group disagreement is positively associated with it. The negative within-group relation strengthens during periods of high policy uncertainty, consistent with a value-of-waiting channel, while the positive cross-group relation is stronger when disagreement involves bearish interpretations and under favorable market conditions, consistent with gains from trade under competing valuation models. The results are robust to alternative assumptions about latent group weights, before-market-open measurement, alternative LLM annotation, and a post-release annotation sample. These findings show that the trading implications of disagreement depend on whether heterogeneity reflects within-stance uncertainty or cross-stance conflict. More broadly, the approach provides a scalable framework for measuring belief heterogeneity in unlabeled text environments.

This study investigates a novel calendar anomaly in the Shanghai Composite Index (SSE Composite) based on the Nayin (纳音) Five Elements system, a cyclical classification embedded in the traditional Chinese sexagenary calendar (Heavenly Stems and Earthly Branches). Each two-year period within the sixty-year cycle is assigned one of five elemental attributes: Earth(土), Metal (金), Fire (火), Wood (木), or Water (水). Using daily trading data from December 1990 to February 2025 spanning 18 complete Nayin cycles, the study finds that Earth-element periods exhibit a 100% positive return rate (mean: +123.4%), while Metal-element periods universally display late-cycle declines (4/4 periods, mean late-segment return: −30.4%). Wood element periods tend toward negative returns (mean: −11.4%). Fire-element periods contain structural bottoms, and Water-element periods mirror the directional momentum of their predecessors (3/3 accuracy). A binomial test for Earth-period returns yields p = 0.095 (one tailed), with Cohen’s d = 1.50 versus non-Earth periods. These patterns are not replicated in the S&P 500 Index under the same calendar framework, suggesting a China-specific cultural behavioral channel consistent with theories of culturally embedded market sentiment. This paper contributes to the calendar anomalies literature by documenting a previously unexplored two year cyclical pattern linked to traditional Chinese metaphysical calendrics.

We examine intensive traders, those who contribute the most to daily volume, and provide new evidence of informational advantages. Using unique Chinese data on particularly active market participants, we demonstrate that intensive trading predicts returns, especially around value-relevant announcements. We also identify several plausible channels; these effects are pronounced for intensive traders who share a geographic location with the firms in which they trade and for stocks with low analyst coverage or small capitalizations. We also uncover novel advantages for intensive traders located near relevant corporate counterparties. Finally, our results suggest a likely important role for lower information acquisition costs.