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In this study, we predict next-day movements of stock end-of-day impliedvolatility using random forests. Through an ablation study, we examine theusefulness of different sources of predictors and expose the value of attentionand sentiment features extracted from Twitter. We study the approach on a stockuniverse comprised of the 165 most liquid US stocks diversified across the 11traditional market sectors using a sizeable out-of-sample period spanning oversix years. In doing so, we uncover that stocks in certain sectors, such asConsumer Discretionary, Technology, Real Estate, and Utilities are easier topredict than others. Further analysis shows that possible reasons for thesediscrepancies might be caused by either excess social media attention or lowoption liquidity. Lastly, we explore how our proposed approach fares throughouttime by identifying four underlying market regimes in implied volatility usinghidden Markov models. We find that most added value is achieved in regimesassociated with lower implied volatility, but optimal regimes vary per marketsector.

Nowcasting Stock Implied Volatility with Twitter