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.

It is diffffcult to understand the risk-return trade-off in option market with observable factormodels. In this paper, we employ a latent factor model for delta-hedge option returns over a varietyof important exchange traded options in China, based on the instrumented principal componentanalysis (IPCA). This model incorporates conditional betas instrumented by option characteristics,to tackle the diffffculty caused by short lifespans and rapidly migrating characteristics of options. Ourresults show that a three-factor IPCA model can explain 19.30% variance in returns of individualoptions and 99.23% for managed portfolios. An asset pricing test with bootstrap shows that there isno unexplained alpha term with such a model. Comparison with observable factor model indicatesthe necessity of including characteristics. We also provide subsample analysis and characteristicimportance.

Using comprehensive account-level data and 192 asset pricing anomaly signals, we investigate whether retail investors and institutions trade on anomalies in China. We find that retail investors tend to trade contrary to anomaly prescriptions, suggesting that they have a strong tendency to buy (sell) overvalued (undervalued) stocks. In contrast, institutions trade consistent with anomalies, indicating that they buy (sell) undervalued (overvalued) stocks. Regarding the information content of anomalies, we find that small retail investors trade contrary to trading-based anomalies, whereas institutions trade consistent with both trading- and accounting-based anomalies. Additionally, lottery stock preference and return extrapolation help explain investors’ trading behavior on anomalies.

This paper employs a proprietary data set on commodity producers’ profit margins (PPMG) and establishes a robust positive relationship between commodity producers’ profitability growth and future returns of commodity futures. The spread portfolio that longs top-PPMG futures contracts and shorts bottom-PPMG futures contracts delivers a statistically significant average weekly return of 36 basis points. We further demonstrate that profitability is a strong SDF factor in commodity futures market. We theoretically justify our empirical findings by developing an investment-based pricing model, in which producers optimally adjust their production process by maximizing profits subject to aggregate profitability shocks. The model reproduces key empirical results through calibration and simulation.

Beyond a bias toward local stocks, investors prefer companies in certain cities over others. This study uses the geographic network of investor-followed stocks from stock watchlists to identify intercity investment preferences in China. We measure the city-pair connectivity by its likelihood of sharing an investor in common whose stock watchlist is highly concentrated in the firms of that city pair. We find that a higher connectivity-weighted aggregate stock demand-to-supply ratio across connected cities is associated with higher stock valuations, higher turnover, better liquidity, and lower cost of equity for firms in the focal city. The effects are robust to controls for geographic proximity and the broad investor base, are stronger among small firms, extend to stock return predictability, and imply excess intercity return comovement. Our results suggest that city connectivity revealed on the stock watchlist helps identify network factors in asset pricing.

According to prospect theory [Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk, Eco- nometrica, 47, 263–292], gains and losses are measured from a reference point. We attempted to ascertain to what extent the refer- ence point shifts following gains or losses. In questionnaire studies, we asked subjects what stock price today will generate the same utility as a previous change in a stock price. From participants’ responses, we calculated the magnitude of reference point adapta- tion, which was significantly greater following a gain than following a loss of equivalent size. We also found the asymmetric adap- tation of gains and losses persisted when a stock was included within a portfolio rather than being considered individually. In studies using financial incentives within the BDM procedure [Becker, G. M., DeGroot, M. H., & Marschak, J. (1964). Measuring utility by a single-response sequential method. Behavioral Science, 9(3), 226–232], we again noted faster adaptation of the reference point to gains than losses. We related our findings to several aspects of asset pricing and investor behavior.

Beyond a bias toward local stocks, investors prefer companies in certain cities over others. This study uses the geographic network of investor-followed stocks from stock watchlists to identify intercity investment preferences in China. We measure the city-pair connectivity by its likelihood of sharing an investor in common whose stock watchlist is highly concentrated in the firms of that city pair. We find that a higher connectivity-weighted aggregate stock demand-to-supply ratio across connected cities is associated with higher stock valuations, higher turnover, better liquidity, and lower cost of equity for firms in the focal city. The effects are robust to controls for geographic proximity and the broad investor base, are stronger among small firms, extend to stock return predictability, and imply excess intercity return comovement. Our results suggest that city connectivity revealed on the stock watchlist helps identify network factors in asset pricing.