- Title
- Social Media Sentiment and Stock Return: A Signaling Theory Explanation for Application of the Natural Langrage Processing Approaches
- Creator
- Qin, Chuan; Miah, Shah J.; Shao, David Xuefeng
- Relation
- ACIS 2022 - Australasian Conference on Information Systems. Proceedings of the ACIS 2022 - Australasian Conference on Information Systems (Melbourne, Australia 12 July, 2022)
- Relation
- https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1030&context=acis2022
- Publisher
- The Association for Information Systems (AIS)
- Resource Type
- conference paper
- Date
- 2022
- Description
- Social media, especially microblogs, have potentials to develop unavoidable factors in investment decision-making, because of its use for capturing human sentiment. In this paper, by applying signaling 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.
- Subject
- social media sentiment; Natural Language Processing (NLP); signaling theory; stock return analysis
- Identifier
- http://hdl.handle.net/1959.13/1496700
- Identifier
- uon:54210
- Language
- eng
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