ÐÂÏã¸ÛÁùºÏ²Ê¿ª½±½á¹û

In this paper, we investigate whether disagreement on StockTwits provides firm-specific information. Using supervised machine learning approaches, we predict investors' recommendations and measure disagreement among investors on StockTwits. Our findings suggest that an increase in investors' disagreement results in a drop in return synchronicity. The negative impact of investors' disagreement on return synchronicity suggests a higher inflow of firm-specific information. Further empirical evidence suggests that the negative impact of disagreement on return synchronicity is more pronounced for firms with a less transparent information environment and higher salience on StockTwits. These results are robust to using a variety of different specifications and estimation methods.

Ìý