We propose a parsimonious measure based solely on daily stock returns to characterize the severity of microstructure frictions at the individual stock level and assess the impact of frictions on the cross section of stock returns. Stocks with the largest frictions command a value-weighted return...

Algorithmic Trading (AT) and High Frequency (HF) trading, which are responsible for over 70\% of US stocks trading volume, have greatly changed the microstructure dynamics of tick-by-tick stock data. In this paper we employ a hidden Markov model to examine how the intra-day dynamics of the stock...

Market microstructure and asset pricing both consider the behavior and formation of prices in asset markets. Yet neither literature explicitly recognizes the importance and role of the factors so crucial to the other approach. This survey seeks to join the two literatures by surveying the work...

Deep learning methods, which can accommodate wide ranges of various stock characteristics to identify optimal investment portfolio or stochastic discount factor (SDF), have been criticized for extracting their superior performances from difficult to arbitrage stocks, high limits-to-arbitrage...

We identify model-free mispricing factors and relate them to global stock prices and investor beliefs. The factors measure variation in the relative mispricing of closed-end funds and their underlying assets. We design three factors to reflect the beliefs and capital flows of important...

A goal for stock exchanges is to increase participation by firms and investors. We show how specific features of the microstructure can reduce perceived ambiguity, and induce participation by both investors and issuers. We develop a model with sophisticated traders, who we view as expected...

Asset prices observed in financial markets combine equilibrium prices and market microstructure noise. In this paper, we study how to tell apart large shifts in equilibrium prices from noise using high frequency data. We propose a new nonparametric test which allows us to asymptotically remove...