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...

We propose a model where an algorithmic trader takes a view on the distribution of prices at a future date and then decides how to trade in the direction of her predictions using the optimal mix of market and limit orders. As time goes by, the trader learns from changes in prices and updates her...

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...

Trading stops are often used by traders to risk manage their positions. In this note, we show how to derive optimal trading stops for generic algorithmic trading strategies when the P&L of the position is modelled by a Markov modulated diffusion. Optimal stop levels are derived by maximising the...

This paper documents a stark periodicity in intraday volume and in the number of trades. We find activity in both variables spikes by about 20% at regular intervals of 5 or 10 minutes throughout the trading day. We argue that this activity is the result of algorithmic trading influenced by human...