RT Journal Article SR Electronic T1 Algorithmic Decision-Making Framework JF Trading FD Institutional Investor Journals SP 82 OP 91 VO 2006 IS 1 A1 Robert Kissell A1 Roberto Malamut YR 2006 UL http://guides.pm-research.com/content/2006/1/82.abstract AB The emergence of algorithmic trading as a viable and often preferred execution mechanism has created a need for new suites of trading analytics to assist investors to compare, evaluate, and select appropriate algorithms. Unfortunately, many of the existing algorithms do not provide necessary transparency to make informed trading decisions. In this paper we provide a dynamic algorithmic decision-making framework to assist investors in determining the most appropriate algorithm given overall trading goals and investment objectives. The approach is based on a three step process where investors choose their price benchmark, select trading style (risk aversion), and specify adaptation tactic. The framework makes extensive use of the Almgren & Chriss (1999, 2000) efficient trading frontier.