In this research, we investigate the informational role of financial analysts. Using a trade-based empirical technique, we estimate the probability of information-based trading for a sample of NYSE stocks that differ in analyst coverage. We determine how this probability differs across stocks followed by many analysts, and we investigate whether analysts increase or create the flow of information. We also determine the `normal' level of noise trading in each sample stock, thereby giving us the ability to assess the depth of the market for stocks with differing analysts followings. Our most important empirical result is that the number of financial analysts is not a good proxy for information-based trading.

We investigate the role of market microstructure metrics in predicting price dynamics for five cryptocurrencies. We show that measures of liquidity and price discovery have predictive power for price dynamics measures used in electronic market making, dynamic hedging strategies, and volatility estimation. We identify own market and cross-market effects for Roll measures and VPINs in BTC and ETH. Our results change little during crypto winter or the 2022 change in interest regimes. Market dynamics of cryptocurrencies are similar to those of futures but exhibit greater toxicity. Our findings are relevant for proposals regarding the appropriate regulatory structure for digital currencies.