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Purpose The purpose of this paper is to examine the difference between the daytime (open-to-close) and overnight (close-to-open) returns of CSI 300 index and its derivative futures. Design/methodology/approach The paper explores the difference between the daytime and overnight time returns by using nonparametric techniques. Moreover, investigation on some factors such as short selling, trading rules, risks are made to seek the sources of the day and night effects based on a large number of empirical analysis. In the end, further analyses on daytime and overnight returns are given by the use of high-frequency data and linear regression technique. Findings The authors show that the daytime returns of CSI 300 index are no less than its overnight returns, while the daytime returns of CSI 300 index futures are no more than its overnight returns, even after removing the heteroscedasticity of the researched time series. Specifically, the PM returns (13:05 to close) play a quite important role in the intra-day time. The findings also suggest that the unique “T+1 trading rule” in China may be a reason that incurs the lower opening price in the morning and the higher closing price in the afternoon, resulting in the statistically significant differences between the daytime and overnight returns. Practical implications The findings are of great importance for investors to decide when to buy and sell stock and futures portfolios in Chinese financial markets. Originality/value This study empirically analyzes why there the higher daytime returns and the lower overnight returns exist in the Chinese stock markets from different aspects and contributes the existing literature on day and night effects because of periodic market closures.

SUMMARY Selfish behavior of nodes in opportunistic networks impact greatly on the network performance. Therefore, an effective mechanism should be proposed to resist such misbehavior and to stimulate the nodes willing to relay packets for other nodes toward their destinations. On the basis of the evolutionary reputation model proposed, this paper investigates the network performance, including message delivery ratio, average delivery delay, and average intermittent hops, under the influence of reputation degree and selfish degree. In addition, the reputation evolution process is described, which helps to understand the interaction among network nodes. Simulation results show that the opportunistic network adopting the model proposed can resist partially the node selfishness and maintain acceptable network performance. Copyright © 2011 John Wiley & Sons, Ltd.