Algorithmic traders acknowledge that their models are incorrectly specified, thus we allow for ambiguity in their choices to make their models robust to misspecification in: (i) the arrival rate of market orders (MOs), (ii) the fill probability of limit orders, and (iii) the dynamics of the...

Algorithmic trading is generally defined as using computer-generated algorithms to create and execute orders on marketplaces. Recently, such algo-trading strategies are increasingly being associated with the negative impact on capital markets – both from a technological as well as a business...

We introduce a new approach to algorithmic investment management that yields profitable automated trading strategies. This trading model design is the result of a path of investigation that was chosen nearly three decades ago. Back then, a paradigm change was proposed for the way time is defined...

These working notes on algorithmic trading are designed as an introduction to the basic equations that govern the day-to-day trading of an investment portfolio to meet a predefined strategic asset allocation. The algebraic framework applies generally to long only and long/short strategies. It...

A unique data set from NASDAQ OMX Nordic allows a deep analysis of trader types' activity and provides evidence on the roles played in the trading ecosystem. We specifically investigate the impact of algorithmic traders on market quality relative to the activities of other market participants...

One rationale for the regulation of algorithmic and high-frequency trading is the perception that algorithms are prone to overreacting to market events, for example by producing unanticipated interaction effects that exacerbate volatility and disrupt efficient price formation. This articles...