Based on market microstructure theory, this study develops a model in which rational informed traders trade against rational uninformed traders and noise traders in a one-shot game within a rational expectations equilibrium (REE) framework. In this model, each informed trader is connected with some other (e.g., k) informed traders, sharing his private information with them while also obtaining their private information. That is, he and his k connected informed traders mutually share their private information before trading. However, the informed traders are assumed to never share or deliver any information to the uninformed traders, who can only learn information from the risky asset’s price. With this information-sharing network, this study aims to analyze the implications of informed traders’ limited information sharing for rational traders’ strategic trading behavior and the consequent asset pricing quality, including price discovery efficiency and market liquidity. The results are as follows. First, with exogenous information acquisition, an informed traders’ demand schedule consists of a speculative part and a market-making part. Because their information sharing does not change the precision of each private signal, the intensity of speculative trading remains unchanged, while the intensity of market-making is negatively affected by the extent of information sharing, k. The rational uninformed traders’ trading strategy includes only market-making part, whose trading intensity equals that of the informed traders. Second, information sharing among informed traders results in more informed trading, leading to more information being incorporated into the clearing price and thus boosting price discovery efficiency. Information sharing aggravates information asymmetry between informed and uninformed traders. However, this effect is alleviated and outweighed by the resulting greater price informativeness, as it enables uninformed traders to learn more information by observing the price. Consequently, the market liquidity gets better. On the other hand, with endogenous information acquisition, information sharing creates a strategic substitution or complementarity effect in rational traders’ information acquisition choices within the information market equilibrium, which impacts trading behaviors and, consequently, asset pricing outcomes. Price discovery efficiency increases with the informed traders’ information sharing, while market liquidity is not linearly affected by the information sharing. Specifically, in an unclear market situation where the ex-ante risk of the traded risky asset is higher and/or the noise trading is greater, market liquidity improves with information sharing. In contrast, in a relatively clearer market situation where the ex-ante risk of the traded risky asset is lower and/or the noise trading is less, market liquidity is U-shaped in its response to information sharing among informed traders. In summary, this study extends the research of information sharing network theory from the perspective of market microstructure theory and helps illustrate the implications of information sharing and dissemination on social media and networks for the security market more comprehensively and objectively. The findings of this study also provide some theoretical insights on how to regulate and guide investors’ information sharing on social media and networks to maintain market liquidity and accelerate price discovery in the securities market.