We examine the information content of the call-put implied volatility spread (IVS) of Shanghai Stock Exchange 50 ETF options. Empirically, the IVS significantly and negatively predicts future SSE50 ETF returns at both weekly and monthly horizons. This predictability is robust both in-sample and out-of-sample, which stands in contrast to prior evidence from the U.S. options market. We explore several potential explanations and show that the IVS is closely linked to the option-cash basis. Its predictability is consistent with the model of Hazelkorn, Moskowitz, and Vasudevan (2023), where the option-cash basis reflects liquidity demand common to both options and underlying equity markets.

This paper studies the magnet effect of market-wide circuit breakers and examines its possible forms using high-frequency data from the Chinese stock index futures market. Unlike previous studies that mainly analyzed the price trend and volatility, this paper is the first to consider the intraday price jump behavior in studying the magnet effect. We find that when a market-wide trading halt is imminent, the probability of a price decrease and the level of market volatility remain stable. However, the conditional probability of observing a price jump increases significantly, leading to a higher possibility of triggering market-wide circuit breakers, which is in support of the magnet effect hypothesis. In addition, we find a significant increase in liquidity demand and insignificant change in liquidity supply ahead of a market-wide trading halt, suggesting that the deterioration of market liquidity may play an important role in explaining the magnet effect.

We examine the process of stock prices adjusting to information conveyed by the trading process. Using the price impact of a trade to measure its information content, our analysis shows that the weekly price impact of market transactions has significant cross-sectional predictive power for returns in the subsequent week. The effect is sensitive to the level of informational asymmetry and is not due to excess liquidity demands or variations in rational risk premia. This finding suggests that prices may slowly incorporate trading information. We then characterize the key channel through which price underreaction occurs. We find that the price impact contains information that is not fully captured by public order flows and that a lead-lag effect exists regarding the arrival of information to different groups of investors. Hong and Stein’s (1999) gradual-information-diffusion theory seems the most likely explanation for price underreaction.