The Volume Clock: Insights into the High-Frequency Paradigm
Execution Strategies in Equity Markets
Execution Strategies in Fixed Income Markets
High-Frequency Trading in FX Markets
Machine Learning for Market Microstructure and High-Frequency Trading
A “Big Data” Study of Microstructural Volatility in Futures Markets
Liquidity and Toxicity Contagion
Do Algorithmic Executions Leak Information?
Implementation Shortfall with Transitory Price Effects
The Regulatory Challenge of High-Frequency Markets
Asset managers are concerned that the algorithms they use to execute their orders may leak information to predators. Predators are traders who use this information to trade in the same direction as the asset managers, increasing the asset managers’ trading costs. Asset managers are particularly concerned about leaking information to high-frequency trading (HFT) predators.
In this chapter, we develop and empirically test a framework for evaluating whether algorithmic (“algo”) executions leak information. Based on the Goldman Sachs Electronic Trading (GSET) algo executions we tested, our main finding is that these algo executions do not leak information that predators, including HFT predators, can profitably exploit. The algos we tested, by slicing up large high-Alpha orders into smaller orders and executing them over time, make it expensive for predators to identify and profitably trade along these orders.
In the next section, we define information leakage and distinguish between good and bad information leakage. We next show how bad information leakage increases execution shortfall and introduce the BadMax approach for testing whether algos leak information to predators. In th