Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/27154
Title: | Investor sentiment and the dispersion of stock returns: Evidence based on the social network of investors |
Authors: | Al-Nasseri, A Menla Ali, F Tucker, A |
Keywords: | investor sentiment;StockTwits;stock returns;quantile regression |
Issue Date: | 25-Sep-2021 |
Publisher: | Elsevier |
Citation: | Al-Nasseri, A., Menla Ali, F. and Tucker, A. (2021) 'Investor sentiment and the dispersion of stock returns: Evidence based on the social network of investors', International Review of Financial Analysis, 78, 101910, pp. 1 - 20. doi: 10.1016/j.irfa.2021.101910. |
Abstract: | This paper extracts an investor sentiment indicator for the 30 DJIA stocks based on the textual classification of 289,024 online tweets posted on the so-called StockTwits, and examines its contemporaneous and predictability effects on the dispersion of stock returns using the quantile regression technique. We find that both contemporaneous and predictability effects of sentiment are heterogeneous throughout the return distribution. Specifically, sentiment is positively contemporaneously associated with stock returns at higher quantiles. However, it is a strong negative predictor of future returns at lower quantiles. Overall, our findings are broadly consistent with most behavioural theories and show that sentiment mainly affects the valuation of assets in extreme market conditions. |
Description: | Supplementary data are available online at https://www.sciencedirect.com/science/article/pii/S1057521921002362?via%3Dihub#s0110 . |
URI: | https://bura.brunel.ac.uk/handle/2438/27154 |
DOI: | https://doi.org/10.1016/j.irfa.2021.101910 |
ISSN: | 1057-5219 |
Other Identifiers: | ORCID iD: Faek Menla Ali https://orcid.org/0000-0001-7791-4642; Allan Tucker https://orcid.org/0000-0001-5105-3506 101910 |
Appears in Collections: | Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | Copyright © 2021 Elsevier. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (https://creativecommons.org/licenses/by-nc-nd/4.0/). The version of record is available at https://doi.org/10.1016/ | 1.35 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License