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.

This paper revisits the idiosyncratic volatility (IVOL) puzzle in the Chinese stock market using a comparative analysis of alternative IVOL forecasting models. We show that deep learning approaches, such as the Temporal Fusion Transformer (TFT), and conventional time-series models like ARMA(1,1) substantially outperform the martingale benchmark in predictive accuracy. The IVOL puzzle proves fragile for total returns: while the Martingale forecast produces a significant negative relationship between IVOL and returns, this effect disappears under TFT-based forecasts in Fama-French regressions and is fully absorbed by the CH-4 factor model. Forecast error decomposition reveals that Martingale overestimation induces spurious negative correlations. Further decomposing returns show negative IVOL exposure overnight but positive intraday compensation, which largely offset in aggregate. The TFT model more accurately captures the true intraday risk premium, explaining the lack of robustness in the IVOL puzzle.

Using a unique sample constituted A-share listed companies from 2008 to 2022 in China，this study empirically tested the impact of divestitures on corporate risk-taking. Results indicate that divestitures positively impact corporate risk-taking. Specifically, strategic and related-party divestitures both promote corporate risk-taking, further improving information transparency. Furthermore, the effect of divestitures is more pronounced in firms with significant financing constraints.

This paper examines whether a socially embedded soft trait, systematically omitted from AI-automated credit decisions, can predict financial outcomes in fully automated lending environments, and through which economic channel. We develop theoretically and examine empirically a labor-market competitiveness mechanism in which facial trustworthiness can serve as a soft signal under information asymmetry, shaping income growth and repayment capacity. Leveraging a unique proprietary dataset from a nationwide FinTech platform in China with AI-based underwriting that explicitly excludes facial information, we link loan performance to employment histories and construct trustworthiness scores using computer-vision algorithms. We find that borrowers with more trustworthy facial appearances experience greater upward job mobility and significantly lower default risk, conditional on rich hard information. A one-standard-deviation increase in facial trustworthiness is associated with a 4.6 percent reduction in default relative to the sample mean. The effect is strongest among borrowers with limited formal credentials, consistent with facial trustworthiness substituting for hard-to-observe labor-market signals. Our findings identify an economically grounded channel through which biometric appearance predicts financial resilience and highlight limits of algorithmic models that omit socially embedded information.

This paper investigates the impact of physical climate risk, measured by local temperature shocks, on corporate debt concentration. Using a comprehensive dataset of Chinese A-share listed companies from 2003 to 2022, we find that firms exposed to more frequent extreme heat events significantly decrease their debt concentration. This is evidenced by a lower Herfindahl-Hirschman Index of debt types and a higher number of distinct debt instruments employed. This finding is robust across a battery of tests, including alternative measures for both temperature shocks and debt concentration, as well as various model specifications. Mechanism analysis reveals that this strategic diversification is driven by firms’ operational vulnerability: the effect is more pronounced for firms with a higher proportion of production workers, a less-educated workforce, and greater reliance on automation, indicating that neither human labor nor robotic substitution is immune to heat-related productivity risks. Further heterogeneity analysis shows that the response is concentrated in firms with the motivation or capacity to act: non-state-owned enterprises, firms that are less financially constrained, those with strong accounting conservatism, and larger, geographically diversified firms. Our study contributes to the literature by identifying debt structure as a key channel for corporate climate adaptation and by introducing physical climate risk as a novel determinant of debt concentration.

We study how mutual fund managers update beliefs and whether such updating reflects managerial skill. Using regulator-mandated, forward-looking investment outlook disclosures from Chinese mutual funds, we construct a novel measure of belief updating intensity based on year-over-year changes in stated beliefs, and further decompose these updates into macro and micro themes. We show that belief updating is strongly state contingent. During recessions, funds with more intensive updating shift their outlooks toward macroeconomic conditions, reduce procyclical stock exposures, and scale back on-site company visits, while the opposite pattern emerges in expansions. Belief-updating intensity positively predicts future performance, and this payoff is strongest when managers reweight beliefs in ways that align with prevailing economic conditions, indicating that belief updating captures an ex ante dimension of managerial skill. Consistent with a signaling interpretation, investors allocate more capital to funds with stronger belief updating, particularly when past returns are less informative.

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.

In view of the divergence of existing research results on the relationship between ESG and investment returns, this paper constructs an S-score metric, which comprehensively measures corporate sustainability performance. It further tests the applicability of a sustainability-based investment strategy using this metric in Chinas A-share market. Using Shanghai and Shenzhen A-shares from May 2016 to April 2024 as the research sample, the S-score is constructed across five dimensions: Profitability, Growth Opportunities, Investment Efficiency, Risk Mitigation, and ESG Performance. The S-score is calculated using Z-score standardization and entropy weighted. Strategy effectiveness was tested through univariate grouping, bivariate grouping, and Fama-Macbeth regression, further examining strategy performance under varying market conditions, holding periods, and information environments. The study finds that the Sscore demonstrates significant discriminative power for cross-sectional stock returns. The hedge portfolio based on this metric achieved an annualized excess return of 7.943% after adjusting for the China three-factor (CH-3) model. Its predictive power remains robust after controlling for variables such as market capitalization and bookto-market ratio, delivering significant positive returns across bull and bear markets, extreme pandemic conditions, and holding periods of up to eight years. From a behavioral finance perspective, this paper reveals that explanations such as the gradual diffusion of information and investors limited attention span help elucidate the profitability of the S-score strategy. The findings demonstrate the effectiveness of Sustainable Investing strategies in Chinas A-share market, indicating that ESGintegrated factor investing can optimize resource allocation. This research contributes empirical evidence on Sustainable Investing in emerging markets, providing insights for policy formulation and practical implementation while supporting the virtuous cycle between Sustainable Investing and long-termism.

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.

This study investigates whether policy bank lending can mitigate supply chain disruption risks by applying a novel dual-layer complex network framework to Chinese A-share listed firms from 2009 to 2023. By employing dynamic methods to assess disruption risks at supply chain nodes, we demonstrate that dual-layer networks significantly outperform single-layer networks in identifying critical risk points. Our results show that while commercial banks systematically avoid lending to high-risk nodes, policy banks strategically allocate credit to vulnerable and shock-affected firms. This countercyclical and targeted lending behavior not only enhances overall network stability and strengthens supply chain resilience but also mitigates disruption risks effectively. The findings highlight the stabilizing role of state-backed financial institutions and provide policy implications for industrial upgrading and sustainable development in emerging economies.