Investors and forecasters often extrapolate from past returns, but whether this reffects behavioral bias or efficient information processing remains unclear. We address this questionby inferring Chinese mutual fund managers’ market expectations from textual analysis oftheir commentaries and linking them to portfolio choices and performance. Extrapola-tion is state-dependent: it is stronger when growth is above trend and idiosyncratic riskis relatively more important. It is associated with weaker market timing and strongerstock picking, leaving overall performance unchanged. Our findings support a rational-inattention model of expectation formation, in which managers shift scarce attentionbetween aggregate and stock-speciffc information as the relative importance of differentrisks change.

We investigate the stock market reaction in China to Trump’s tariff policy announcement on April 2, 2025. We find that the tariff policy reduced stock prices of Chinese firms except those in the agricultural sector. Large-cap stocks, value stocks, stocks of high profitability firms, and stocks of state-owned enterprises experienced smaller negative impacts. Stocks with higher institutional holdings by mutual funds and Social Security Funds exhibited higher resilience, possibly due to these investors' superior capability in selecting stocks and forecasting trade war risks. In contrast, stocks held by Qualified Foreign Institutional Investors (QFII) did not exhibit such resilience.

This study examines the mean-reversion behavior of trading volume in China’s A-share market, with a focus on the speed at which abnormal surges dissipate. We compare two competing hypotheses: the stealth-trading hypothesis, where persistent volume reflects order-splitting by informed traders, and the informational-efficiency hypothesis, which interprets faster reversion as a sign of efficient information absorption. Using the Ornstein–Uhlenbeck (OU) model, we estimate the reversion speed for over 3,000 stocks and link it to firm- and industry-level characteristics. We find that trading volume is strongly mean-reverting, with over 98% of stocks classified as stationary. The OU model forecasts reversion speed with less than 7% error. Faster reversion is associated with larger size, higher analyst coverage, lower volatility, and greater liquidity. Notably, reversion speed increased after the 2006 IFRS reform but declined following Stock Connect, suggesting that stock market policies can influence informational efficiency. Our OU-based methodology offers a simple, observable proxy for monitoring how quickly markets process information. These results position trading volume as a core variable in market microstructure research and policy evaluation.

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.

Straddle Option is a financial trading tool that explores volatility premiums in high-volatility markets without predicting price direction. Although deep reinforcement learning has emerged as a powerful approach to trading automation in financial markets, existing work mostly focused on predicting price trends and making trading decisions by combining multidimensional datasets like blogs and videos, which led to high computational costs and unstable performance in high-volatility markets. To tackle this challenge, we develop automated straddle option trading based on reinforcement learning and attention mechanisms to handle unpredictability in high-volatility markets. Firstly, we leverage the attention mechanisms in Transformer DDQN through both self-attention with time series data and channel attention with multi-cycle information. Secondly, a novel reward function considering excess earnings is designed to focus on long-term profits and neglect short-term losses over a stop line. Thirdly, we identify the resistance levels to provide reference information when great uncertainty in price movements occurs with intensified battle between the buyers and sellers. Through extensive experiments on the Chinese stock, Brent crude oil, and Bitcoin markets, our attention-based Transformer-DDQN model exhibits the lowest maximum drawdown across all markets, and outperforms other models by 92.5% in terms of the average return excluding the crude oil market due to relatively low fluctuation.

Overwork intensity (OI) is a salient issue that directly affects employees’ motivation and productivity. By using a novel dataset of overwork intensity constructed from daily high-resolution nightlight satellite images, we examine whether overwork intensity is a priced risk in the cross-section of stock returns. We show that a zero-investment portfolio that buys the highest OI quintile stocks and shorts the lowest OI quintile stocks earns 0.495% returns per month. This result is robust when controlling for various well-known risk factors. We argue and empirically verify that profftability, corporate governance, investor sentiment and lottery preference are the potential channels that drive the result.

This study examines the pricing effect of global economic policy uncertainty (GEPU) in the cross-section of individual stocks and portfolios in the Chinese stock market. Employing the GEPU index as a systematic risk factor, our empirical analysis demonstrates that stocks in the lowest decile of βGEPU generate risk-adjusted annualized returns that are 5.16% higher than those in the highest decile. Our analysis reveals that this βGEPU premium is driven by the outperformance of stocks with negative βGEPU and the underperformance of those with positive βGEPU. These findings suggest that uncertainty-averse investors not only demand compensation for holding stocks with negative βGEPU exposure but are also willing to pay a hedging premium for assets that serve as positive βGEPU hedges. The results prove robust across multiple specifications, persisting in both bivariate portfolio sorts and Fama-MacBeth cross-sectional regressions that control an extensive set of classic pricing factors.

This study aims to elucidate the behaviors of the Shanghai and Shenzhen stock exchanges during extreme volatilities—China’s 2015 Stock Market Crash and the 2020 COVID-19 pandemic. Using topological data analysis (TDA), the study identiffes early warning signals within the Shanghai–Hong Kong (SHHK) and Shenzhen–Hong Kong Stock (SZHK) -Stock Connect markets. This timeliness ensures proactive market stabilization and portfolio adjust-ments. The results also reveal that the interconnected market signals are more stable, supporting multidimensional crisis detection and offering valu-able tools for policymakers and investors to effectively mitigate ffnancial risks.

We examine the influence of foreign equity flows on China's unique retail-dominated stock market, identifying a novel channel through which investors’ herding creates significant market externalities. We find that the daily disclosure of foreign investors' positions induces local investors to imitate these trades, resulting in observable short-term price distortions followed by reversals. Our analyses, which include inflow predictability tied to disclosure timing and path analysis decomposition, confirm that the herding effect, largely driven by retail participants, is more impactful than the direct effect based on the informational content of foreign capital. Furthermore, inflated stock prices resulting from the herding behavior cause public firms to overvalue and overinvest, leading to reduced investment efficiencies. These findings highlight potential adverse consequences stemming from specific stock market liberalization designs.

Traditional threshold-based stock networks suffer from subjective parameter selection and inherent limitations: they constrain relationships to binary representations, failing to capture both correlation strength and negative dependencies. To address this, we introduce statistically validated correlation networks that retain only statistically significant correlations via a rigorous t-test of Pearson coefficients. We then propose a novel structure termed the largest strong-correlation balanced module (LSCBM), defined as the maximum-size group of stocks with structural balance (i.e., positive edge-sign products for all triplets) and strong pairwise correlations. This balance condition ensures stable relationships, thus facilitating potential hedging opportunities through negative edges. Theoretically, within a random signed graph model, we establish LSCBM’s asymptotic existence, size scaling, and multiplicity under various parameter regimes. To detect LSCBM efficiently, we develop MaxBalanceCore, a heuristic algorithm that leverages network sparsity. Simulations validate its efficiency, demonstrating scalability to networks of up to 10,000 nodes within tens of seconds. Empirical analysis demonstrates that LSCBM identifies core market subsystems that dynamically reorganize in response to economic shifts and crises. In the Chinese stock market (2013–2024), LSCBM’s size surges during high-stress periods (e.g., the 2015 crash) and contracts during stable or fragmented regimes, while its composition rotates annually across dominant sectors (e.g., Industrials and Financials).