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...

We investigate the role algorithmic trading (AT) on days when the absolute value of the market return is more than two percent. We find that the abnormal return of a stock is related to the stock's AT intensity, that high AT intensity stocks experience less price drops (surges) on days when the...

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...

We assume that the drift in the returns of asset prices consists of an idiosyncratic component and a common component given by a co-integration factor. We analyze the optimal investment strategy for an agent who maximizes expected utility of wealth by dynamically trading in these assets. The...

This paper studies whether and why algorithmic traders exhibit one of the most broadlydocumented behavioral puzzles - the disposition effect. We use trade data from the NASDAQ Copenhagen Stock Exchange merged with the weather data. We find that on average, the disposition effect for human...