Using data from the Bucharest Stock Exchange, we examine the factors influencing the probability of informed trading (PIN) during February-October 2020, a COVID-19 pandemic period. Based on an unconditional quantile regression approach, we show that PIN exhibit asymmetric dependency with...

Estimating market liquidity based on low-frequency (daily) data is important for both empirical research and investment practice. We apply machine learning to estimate market liquidity by combining human-engineered liquidity proxies based on microstructure models and widely available...

The estimation of market impact is crucial for measuring the information content of trades and for transaction cost analysis. Hasbrouck's (1991) seminal paper proposed a Structural-VAR (S-VAR) to jointly model mid-quote changes and trade signs. Recent literature has highlighted some pitfalls of...