This Article argues that the rise of algorithmic trading undermines efficient capital allocation in securities markets. It is a bedrock assumption in theory that securities prices reveal how effectively public companies utilize capital. This conventional wisdom rests on the straightforward...

We develop a portfolio trading strategy that aims to achieve optimal performance against arrival prices by minimizing market impact and risk. We first lay the groundwork by formulating a single-security trading strategy and then generalize the framework to the portfolio trading context....

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 examine the effects of algorithmic trading (AT) on the US mutual fund industry and find that funds holding stocks with higher AT intensity have lower holdings returns and higher interim trading profits (return gap). This effect survives controls of effective spread and execution shortfall....

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...