We study how alternative data influences investors’information acquisition and market efficiency from both short-term and long-term perspectives. Using the exogenous introduction of online sales data on a major Chinese financial information platform, we find that such disclosure enhances investors’information processing capacity. In the short term, firms covered by online sales data exhibit weaker market reactions to unexpected earnings, reflecting more accurate earnings expectations. In the long term, online sales disclosure strengthens the link between earnings and stock returns, indicating higher earnings value relevance. These results are robust to a series of endogeneity tests and robustness checks. Cross-sectional analyses indicate that these effects are more pronounced for firms with lower institutional ownership, higher earnings volatility, and greater reliance on online sales. Mechanism analyses show that short-term effects operate through improved analyst forecast accuracy, while long-term effects are jointly driven by higher firms' earnings quality and enhanced analyst performance. These findings offer new perspectives on market efficiency and fresh evidence on investor information processing.

This paper empirically evaluates the impact of a Government-Initiated Non-Profit Ecommerce Platform (GNEP) on specialty agricultural sales, focusing specifically on Pu’er tea in China. Using a difference-in-differences methodology and a comprehensive panel dataset that covers over 90% of local tea farmers, we uncover a marked substitution effect. The implementation of GNEP leads to an average decline of 11.22% in offline household sales, while online sales see an uptick of 16.88%. Further analysis confirms a universal channel shift from offline to online sales, irrespective of both production levels and tea quality. Contrary to expectations, the overall tea sales volume remains largely stable post-launch. Additionally, premium-quality teas experience a 2.42% price boost online, while regular teas show a 0.40% decrease compared to offline prices. Mechanism analyses further indicate that the increase in online sales is driven primarily by the intensive margin instead of the extensive margin. Although the platform does not significantly expand the number of farmers engaging in online sales, it succeeds in offering a cost-effective avenue for diversifying product offerings and achieving higher prices for premium-quality products. Our study illuminates the transformative role of e-commerce platforms in rural economic development and provides essential insights for policymakers and practitioners.

By using a proprietary real–time daily online sales data collected in China from 10–billion consumer accounts, this paper ffnds that the ffrm–level daily online sales growth (DOSG) can positively predict future one–day to more than three–month cumulative stock returns in the cross section, implying a growth premium in contrast to Lakonishok, Shleifer, and Vishny (1994). A spread portfolio that is long on stocks with high DOSG and short on stocks with low DOSG delivers an abnormal return of around 30 basis points per week. DOSG derives its short–run (e.g., weekly) predictability from investor sentiments, tilting to a behavioral explanation. However, it derives its medium to long–run (e.g., three–month) predictability from fundamentals, voting for a rational explanation. Our further evidence indicates that stocks with high DOSG experience more intensive information acquisition from retail investors and less severe crash risk, implying online sales as a channel for retail investors to get access to daily real–time ffrm fundamentals.