This study examines the investment value of information provided by crypto-influencers, that is, social media influencers covering crypto assets on Twitter. We examine the returns associated with approximately 36,000 tweets issued by 180 of the most prominent crypto social media influencers covering over 1,600 crypto assets for the two years spanning through December 2022. Our primary results indicate that crypto-influencers’ tweets are initially associated with positive returns. However, these tweets are followed by significant negative longer-horizon returns, suggesting they generate minimal long-term investment value. These effects are most pronounced for tweets issued by crypto-influencers proclaiming to be crypto experts, for smaller cap crypto asset securities and for self-described experts with many Twitter followers. In an additional analysis, we use machine-learning methods to classify tweets and find that this pattern of results strengthens when the tweets have a more positive sentiment or relate to buy recommendations.

We examine the relations between tax avoidance and two components of firm risk (priced risk and idiosyncratic risk) using latent class mixture models. OLS regressions suggest that tax avoidance is negatively related to both priced risk and idiosyncratic risk. However, results from latent class mixture models reveal that 35.6 (58) percent of the sample exhibits a significant positive association between tax avoidance and priced (idiosyncratic) risk and firm characteristics are strikingly different across the latent classes. We use these differences to develop parsimonious models to predict latent class assignments, which we then use to demonstrate that firms predicted to have a positive relation between tax avoidance and priced risk or idiosyncratic risk have more negative future tax outcomes, including higher relative cash tax rate volatility, larger additions to tax reserves, larger payouts to tax authorities, and more negative tax-related news coverage over the following three years. Data Availability: Data are available from the public sources cited in the text.