Whether risk aversion is time varying and countercyclical is central to modern asset pricing, yet evidence remains limited and is based mainly on experimental, survey, or aggregate stock market data. We provide individual-level evidence from 10 million Chinese auto insurance contracts from 2011 to 2017, estimating policyholders’ risk aversion from deductible choices. We find that risk aversion is time varying and countercyclical. The estimates are negatively related to lottery and stock trading, positively related to insurance sales and bond trading, and vary with psychological factors, including seasonal mood, “zodiac year,” and calendar events.

This paper identifies the marginal investors in China’s stock market and examines their impact on stock pricing. To clearly distinguish between the equity constraint channel and the debt constraint channel, we construct the capital ratio factor and the debt constraint factor for banks and securities companies, the two most critical financial intermediaries in China’s stock market. Our results demonstrate that banks indeed serve as marginal investors and influence stock market efficiency primarily through the equity capital constraint channel. Furthermore, we find that the bank capital ratio factor significantly explains stock mispricing in China, with the single-factor model based on bank equity capital producing substantially smaller pricing errors compared to traditional multi-factor models.

Investors’ prior experiences with a stock substantially affect their willingness to repurchase it. This paper explores the repurchase effect, a psychological bias in which investors are reluctant to repurchase stocks that have appreciated after a prior sale. To quantify this bias, we develop a novel stock-level measure, termed Repur, and investigate its implications for cross-sectional asset pricing. Our findings show that stocks with higher Repur tend to experience reduced future buying pressure from investors, which in turn results in lower subsequent returns. Economically, long-short portfolios based on Repur yield annualized abnormal returns exceeding 23% for equal-weighted and 11% for value-weighted risk-adjusted returns. Further analyses show that the pricing effect of Repur is more pronounced following periods of high investor sentiment, for stocks with greater arbitrage constraints, and for firms with smaller investor bases. Out-of-sample evidence from China confirms the significant pricing impact of the repurchase effect.

This study integrates microstructure theory with asset pricing to investigates how the idiosyncratic volatility (IVOL) puzzle operates through specialized liquidity risk channels in China’s A-shares market. We employ intraday transactions data to perform a novel decomposition of liquidity into its variable (informational) and fixed (transitory) components. We show that the anomalous negative relationship between IVOL and future returns emerges from the intricate interaction of liquidity risk exposure, information and arbitrage constraints, and measurement biases. Specifically, the variable component tied to informed trading and adverse selection exposes high-IVOL stocks to greater arbitrage risk during liquidity shocks, while the fixed component exacerbates their vulnerability to short-term market-making cost fluctuations. Our results reveal that the IVOL puzzle is not a statistical artifact but a rational pricing phenomenon driven by omitted liquidity risk, mediated by the country’s unique institutional environment and monetary conditions.

We interpret industry discount rates as proxies for value investment, grounded in Q theory, while capturing gambling preferences through salience theory. Integrating these perspectives, we propose a novel asset pricing model (ST-ICAPM) that unifies value investment and salience factors, evaluating its pricing efficacy across Chinese industries from 2004 to 2023. Empirical results show that investment factors tend to negatively predict future returns, while growth factors command risk premia. However, profitability factors exhibit limited explanatory power. Investor expectations are primarily driven by profit growth, emphasizing the need to enhance profit stability for a value-oriented market. Salience intensity, especially when measured via eigenvector centrality within industry networks, serves as a strong negative predictor of returns, emphasizing the importance of conceptual connections over purely economic linkages in shaping investor behavior. Robust tests confirm that the ST-ICAPM outperforms benchmark models (FF3, CARHART4, FF5, ICAPM, and STCAPM) in terms of pricing power. Our findings emphasize the need to promote value investing and restrain gambling behavior as essential strategies to cultivate a resilient capital market in China.

This paper studies missing firm characteristics in the Chinese stock market and their implications for empirical asset pricing. Relative to the U.S. market, missing firm characteristics in China remain underexplored despite substantial differences in data availability and disclosure environments. Using a dataset of 106 firm characteristics from 1992 to 2021, we document a pronounced cliff-shaped pattern in missingness, with missing rates falling sharply after 2000. We then compare expectation-maximization (EM) and mean imputation (MN) in both univariate characteristic-sorted portfolios and machine-learning applications that combine many predictors. Results indicate that, in univariate analysis, the two methods produce very similar return spreads because they assign largely the same stocks to the extreme deciles. In machine-learning applications, however, EM-imputed data generally produce better-performing prediction-sorted portfolios than mean-imputed data. These findings provide new evidence on missing firm characteristics in a major emerging market and highlight the importance of imputation choices in machine-learning asset-pricing applications.

This study documents a novel, culturally embedded calendar anomaly in the Shanghai Composite Index (SSE Composite) derived from the Nayin (纳音) Five Elements system—a traditional Chinese sexagenary calendrical framework. Utilizing daily data from 1990 to 2025, the analysis reveals a significant correlation between elemental two-year periods and market performance. Key findings include: Earth-Element Dominance: Earth periods exhibit a 100% positive return rate (4/4) with a mean return of +123.4%. The effect size is substantial (Cohen’s d=1.50) compared to non-Earth periods. Metal-Element Declines: Metal periods universally display a structural peak-and-decline morphology, with an average −30.4% late-cycle decline. Water-Element Momentum: Water periods systematically mirror the directional momentum of their predecessors with 100% accuracy (3/3). These patterns fail to replicate in the S&P 500, suggesting a unique cultural-behavioral channel where traditional metaphysical cycles modulate investor sentiment in the Chinese market. This research provides the first empirical validation of Nayin-based cyclicality in financial asset pricing, offering a predictive framework for institutional and individual investors focused on the China-specific market. Keywords: Calendar anomaly, Chinese traditional calendar, Nayin Five Elements, Shanghai Composite Index, Cultural behavioral finance, Sexagenary Cycle, Market Sentiment Declaration of Interest The author declares no conflict of interest. To ensure the objectivity of this research, the author further declares that he holds no active personal trading positions in the securities discussed. The author's personal trading account has been inactive with zero transactions over the past five years.

This paper documents that the physical appearance of CEOs, specifically excess body weight, is priced in the capital market. In the absence of explicit health disclosures,market participants interpret obesity as a proxy for latent health risks and potential managerial disrupts, thereby demanding a compensation premium. Our analysis reveals that (1) IPOs of firms with obese CEOs have lower first-day performance, (2) these firms achieve a lower valuation, (3) the stocks of these firms have lower liquidity and (4) they provide higher stock returns thereafter. A quasi-natural experiment based on the invention of anti-obesity medications provides supporting causal evidence.

This study investigates the link between market arbitrage risk and liquidity risk pricing in a conditional asset pricing framework. We estimate comparative models both at the portfolio and firm level in the Chinese A- and B-shares to test behavioral hypotheses with respect to foreign ownership restrictions and market segmentation. Results show that conditional liquidity premium and risk betas exhibit pronounced asymmetry across share classes which could be attributed to differentiated levels of market mispricing. Specifically, stocks with a greater degree of information asymmetry and retail ownership are more sensitive to liquidity risks when the market arbitrage risk increase. Further policy impact analysis shows that China’s market liberalization efforts, contingent upon its recent stock connect programs, conditionally reduce the price of liquidity risk for connected stocks.

We introduce a deep visualization (DV) framework that turns conventional commodity data into images and extracts predictive signals via convolutional feature learning. Specifically, we encode futures price trajectories and the futures surface as images, then derive four deep‑visualization (DV) predictors, carry ($bs_{DV}$), basis momentum ($bm_{DV}$), momentum ($mom_{DV}$), and skewness ($sk_{DV}$), each of which consistently outperforms its traditional formula‑based counterpart in return predictability. By forming long–short portfolios in the top (bottom) quartile of each DV predictor, we build an image‑based four‑factor model that delivers significant alpha and better explains the cross‑section of commodity returns than existing benchmarks. Further evidence shows that the explanatory power of these image‑based factors is strongly linked to macroeconomic uncertainty and geopolitical risk. Our findings reveal that transforming conventional financial data into images and relying solely on image-derived features suffices to construct a sophisticated asset pricing model at least in commodity markets, pioneering the paradigm of image‑based asset pricing.