Computation of Implementation Shortfall for Algorithmic Trading by Sequence Alignment

Implementation shortfall measures the difference in performance between paper portfolio and real portfolio. It is decomposed as the sum of execution cost and opportunity cost. The authors show that the original framework is not directly applicable to algorithmic trading and propose a new framework to compute implementation shortfall and its decomposition. They use an efficient algorithm inspired by DNA sequence alignment techniques to align the trade records from both portfolios and then compute the implementation shortfall with a breakdown of execution cost and opportunity cost for diagnosis. Their framework is simple, objective, and computationally efficient—the complexity only grows linearly with respect to the numbers of trades of paper and real portfolios. Thus, the framework proposed in this article is applicable to high-frequency trading data. TOPICS:Portfolio construction, big data, performance measurement