Renewable energy stocks forecast using Twitter investor sentiment and deep learning

Gabriel Paes Herrera, Michel Constantino, Jen-Je Su and Athula Naranpanawa

Energy Economics, 2022, vol. 114, issue C

Abstract: This paper examines the impact of investor sentiment on forecasting returns and volatility for renewable energy stocks. We apply a natural language processing technique to extract investor sentiment from Twitter during both trading and non-trading hours. Forecasting analyses are conducted using a state-of-the-art hybrid deep learning technique and benchmark models. Results show that the sentiment variables hold significant add-on information not captured by standard financial market variables. Twitter investor sentiment considerably improves return and volatility forecasts of renewable energy stocks, especially when the deep learning method is employed. Our results are statistically significant and robust under different settings.

Keywords: Twitter; LSTM; Stock volatility; Stock return; Clean energy (search for similar items in EconPapers)
JEL-codes: C32 C45 C53 C58 G11 G41 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:114:y:2022:i:c:s0140988322004170

DOI: 10.1016/j.eneco.2022.106285

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