The impact of Twitter-based sentiment on US sectoral returns
This paper scrutinizes the effect of Twitter-based sentiment on US sectoral returns using data from between 21 June 2010 and 13 April 2020. We apply causality in quantiles as a non-parametric measure, followed by a rolling window wavelet correlation. The former measures the manifestation of causalit...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| التنسيق: | article |
| منشور في: |
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://dx.doi.org/10.1016/j.najef.2022.101847 https://www.sciencedirect.com/science/article/pii/S1062940822001826 http://hdl.handle.net/10576/47355 |
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| الملخص: | This paper scrutinizes the effect of Twitter-based sentiment on US sectoral returns using data from between 21 June 2010 and 13 April 2020. We apply causality in quantiles as a non-parametric measure, followed by a rolling window wavelet correlation. The former measures the manifestation of causality directed from Twitter-based sentiment towards US sectoral returns, whereas the latter measures the correlation of returns across decomposed series that correspond to different time horizons. Our results highlight symmetric changes in US sectoral returns that vary across different sectors. The healthcare, communications, materials, consumer discretionary, energy, staples, and information technology sectors are more sensitive to changes in Twitter-based sentiment across all quantiles. Our findings from the rolling window wavelet correlation point to low correlation values for all decomposed series (i.e., long-, medium-, and short-run). Our findings have value for investors in the US sectoral market because they may be helpful for constructing and rebalancing portfolios based on varying levels of correlation across different quantile distributions and investment periods. |
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