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Renewable energy stocks forecast using Twitter investor sentiment and deep learning

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

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.

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  • Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022. "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, vol. 114(C).
    • Handle: RePEc:eee:eneeco:v:114:y:2022:i:c:s0140988322004170
    DOI: 10.1016/j.eneco.2022.106285
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    More about this item

    Keywords

    Twitter; LSTM; Stock volatility; Stock return; Clean energy;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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