Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We study the impact of Twitter sentiment and sentiment divergence on returns, volatility and trading volumes for renewable energy stocks. Based on daily time series for Twitter sentiment and Twitter sentiment divergence, we estimate VAR models and evaluate spillovers between sentiment and renewable energy stock pricing and trading. We find that whereas Twitter sentiment has no sizeable impact on returns, volatility or trading volumes, Twitter sentiment divergence generates feedback effects on volatility and trading volumes. Our evidence would indicate that the wisdom of the Twitter crowd is not substantial in shaping prices and trading for renewable energy companies.