Negative selection—a new performance measure for automated order execution

Automated Order Execution is the dominant way of trading at stock markets. Performance of numerous execution algorithms is measured through slippage from some benchmark. But measuring true slippage in algorithmic execution is a difficult task since the execution as well as benchmarks are function of market activity. In this paper, we propose a new performance measure for execution algorithms. The measure, named Negative Selection, takes a posterior look at the trading window and allows us to determine what would have been the optimal order placement if we knew in advance, before the actual trading, the complete market information during the trading window. We define the performance measure as the difference between the hypothetical optimal trading position and the actual execution. This difference is calculated taking into account all prices and traded quantities within the considered time window. Thus, we are capturing the impact caused by our own trading as a cost that affects all trades. Properties of Negative Selection, which make it well defined and objective are discussed. Some empirical results on real trade data are presented.