Social media, especially microblogs, have potentials to develop significant unavoidable factors in investment decision-making, because of its use for capturing human sentiment. In this paper, by applying Signalling theory and Natural Language Processing (NLP) technique, we concern social media sentiment as a signal to stock return which is based on human the sentiment, which may lead to price fluctuation in the market. We take the strength of signal into consideration, introducing the sentiment of traditional media to compare with social media sentiment in different industry. The empirical result of this paper will prove the relationship between social media sentiment and stock return. It will also reflect on analyzing the changes of stock price given different strength of signals in both positive and negative way. The entire study will be viewed as a guideline for investors to filter and smartly use the huge numbers of information when making investment decision.
Qin, Chuan; Miah, Shah J; and Shao, David, "Social Media Sentiment and Stock Return: A Signalling Theory Explanation for Application of the Natural Langrage Processing Approaches" (2022). ACIS 2022 Proceedings. 31.