This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...

This paper proposes a two-state predictive regression model and shows that stock market 12-month return (TMR), the time-series momentum predictor of Moskowitz, Ooi, and Pedersen (2012), forecasts the aggregate stock market negatively in good times and positively in bad times. The out-of-sample...

This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...

Any lead-lag effect in an asset pair implies the future returns on the lagging asset have the potential to be predicted from past and present prices of the leader, thus creating statistical arbitrage opportunities. We utilize robust lead-lag indicators to uncover the origin of price discovery...

This paper conducts tests of the random walk hypothesis and market efficiency for 14 national public real estate markets. Random walk properties of equity prices influence the return dynamics and determine the trading strategies of investors. To examine the stochastic properties of local real...

The Granger causality procedure is used to assess the dynamics of market efficiency of 17 international stock indices. These indices are based on relatively smaller firms. The reference of market efficiency is a stock index, from the same economy, which is based on relatively larger firms. There...