相关性(法律)
线程(计算)
联想(心理学)
骨料(复合)
心理学
优势(遗传学)
金融经济学
经济
计量经济学
认知心理学
语言学
业务
交易策略
计算机科学
库存(枪支)
轮流
对话
作者
Swaminathan Balasubramaniam,Jorge Sabat
摘要
Abstract We study investor disagreement within conversations rather than across independent messages. Using Reddit’s r/wallstreetbets (WSB), we classify the disagreement of each comment toward its parent using a large language model and aggregate the signed disagreement scores at the ticker-day level by conversational depth. Disagreement does not diminish as discussions deepen, and later conversational stages can remain highly polarized. Contrary to the predictions of common prior models, successive rounds of discussion do not drive investors toward consensus. Relevance to market outcomes, however, concentrates in first-round replies. Depth-1 conversational disagreement predicts retail trading volume most robustly in meme stocks from the 2021 retail-trading episode, with effects persisting from the day of the post through subsequent trading days. In the broader cross-section of stocks discussed on WSB, the same association appears contemporaneously but weakens at longer horizons. Discussions that are deeper in the thread are uninformative about trading outcomes. (JEL G10, G12, G41, C55)
科研通智能强力驱动
Strongly Powered by AbleSci AI