This paper proposes the Shannon entropy as an appropriate one-dimensional measure of behavioural trading patterns in financial markets. The concept is applied to the illustrative example of algorithmic vs. non-algorithmic trading and empirical data from Deutsche Börse's electronic cash equity...

This study examines the impact of corporate earnings announcements on trading activity and speed of price adjustment, analyzing algorithmic and non–algorithmic trades during the immediate period pre– and post– corporate earnings announcements. We confirm that algorithms react faster and...

Modern Algorithmic Trading ("Algo") allows institutional investors and traders to liquidate or establish big security positions in a fully automated or low-touch manner. Most existing academic or industrial Algos focus on how to "slice" a big parent order into smaller child orders over a given...

Academic research has identified several factors that affect price movements; however, the scenario changes abruptly in the case of very short time price changes (VSTPC). This topic is not specifically examined in the existing literature; nonetheless, the behavior of the market microstructure is...

We examine the mechanism by which social media sentiment affects stock prices. Specifically, we assess the impact of Twitter feeds on stock returns at the intraday level. We find that an increase in buyer-initiated trades has a significantly positive price impact. The impact is stronger with an...

This paper investigates the importance of speed for technical trading rule performance for three highly liquid ETFs listed on NASDAQ over the period January 6, 2009 up to September 30, 2009. In addition we examine the characteristics of market activity over the day and within subperiods...