Based on high-frequency data of China's commodity market from 2017 to 2022, this article examines the intraday momentum effect. The results indicate that China's commodity futures and options have significant intraday reversal effects, and the overnight opening factor and opening to last half hour factor are more significant. These effects are driven, in part, by liquidity factors. This trend aligns with market makers' behavior, passively accepting orders during low liquidity and actively closing positions amid high liquidity. Furthermore, our examination of cross-predictive ability shows strong futures-to-options predictability, while the reverse is weaker. We posit options traders' Vega hedging as a key factor in this phenomenon, our study finds futures volatility changes can predict options’ return.

This paper studies the Level, Slope, and Curve factor model under different tests in the Chinese stock market. Empirical asset pricing tests reveal that the slope factor in the model represents either reversal or momentum effect for the Chinese stocks. Further tests on individual stocks demonstrate that the Level, Slope, and Curve model using effective predictor variables outperforms other common factor models, thus a filter in virtue of multiple hypothesis testing is designed to identify the effective predictor variables. In the filter models, the cross-section anomaly factors perform better than the time-series anomaly factors under different tests, and trading frictions, momentum, and growth categories are potential drivers of Chinese stock returns.

We track the fundamental informed traders' (FITs) behavior and show the fundamental momentum effect in the Chinese stock market. We train the deep learning model with a set of fundamental characteristics to extract fundamental implied component from realized returns. The fundamental part characterizes the price movement driven by FITs. Fundamental momentum differentiates from the fundamental trend and is not quality minus junk (QMJ) factor. Underreaction bias helps explain the strategy, as it generates stronger profit during periods of low investor sentiment and aggregate idiosyncratic volatility. Fundamental momentum is not sensitive to changing beta and robust in subsamples and machine learning models.

We provide the first systematic analysis of the stock return lead-lag effect among firms connected through shared analyst coverage in China’s A-share markets. We measure the shared analysts-weighted average returns of connected firms (CF) and show that CF return is a significant positive predictor of future returns of the focal firms in the following one to 12 months. The CF-based long-short portfolio earns an abnormal return of 10% to 12% per year. The effect is robust to controls for the industry and geographic momentum effects. Further evidence shows that the CF momentum spillover effect is stronger when the focal firm shares more analysts with connected firms, is covered by more non-star analysts or analysts with lower levels of education, or is held by more stress-resistant institutional investors. Our findings contribute to the cross-asset momentum literature by documenting a new, strong, and long-lasting momentum spillover effect in the Chinese stock markets.

Different from developed markets, Chinese government imposes strict control over the IPO market. Using a sample of 156 monthly returns over the period of 1996 to 2008, we find a positive relationship between the monthly issuing size and prior market return, suggesting that government decides the timing and size of issuance based on prior market condition. Different from previous findings, we find no evidence of decline in subsequent market return after IPO. However, IPO issuance has a significantly negative impact on the return momentum effect, while the degree of impact is indifferent to issuing size. We conjecture that the overall mild impact on subsequent market results from the government control over the IPO market.

Recent theoretical studies (Aggarwal and Wu,2006; Mei,Wu and Zhou,2004) show that trade-based stock price manipulation is a possible source of the momentum effect. This paper proposes three sets of testable hypotheses and provides empirical evidence for a manipulation-based explanation of momentum.Using weekly data on 14 CITIC industries in the Shanghai A-share market from 1997 to 2006, our analysis of industry momentum shows that cumulative returns first increase then decrease across holding periods, and the returns monotonically decrease across formation periods. This return pattern is consistent with a so-called”pump and dump”scheme,where momentum is created by manipulators and chased by speculators. We attribute the source of momentum to the positive own-autocorrelation, which dominates the cross-autocorrelation effect of industry returns. We also find that momentum profits are higher in the bull than in bear market, and most of the profits come from the gains of winning industries rather than the losses of losing industries. These empirical results,when related to some well-documented behavioral biases of Chinese speculators,tell us a possible stock-market manipulation story of momentum.

This study examines the role of information uncertainty (IU) in predicting cross-sectional stock returns. We define IU in terms of "value ambiguity", or the precision with which firm value can be estimated by knowledgeable investors at reasonable cost. Using several different proxies for IU, we show that: (1) On average, High IU firms earn lower future resturns (the "mean" effect), and (2) Price and earnings momentum effects are much stronger among high IU firms (the "interaction" effect). These findings are consistent with theoretical models that feature investor overconfidence (Daniel et al. (1998)) and information cascades (Bikhchandani et al. (1992)). Specifically, our evidence indicates that high IU exacerbates investor overconfidence and limits rational arbitrage.