PT  - JOURNAL ARTICLE
AU  - Robert Kissell
AU  - Roberto Malamut
TI  - Algorithmic Decision-Making Framework
DP  - 2006 Mar 20
TA  - Trading
PG  - 82--91
VI  - 2006
IP  - 1
4099  - http://guides.pm-research.com/content/2006/1/82.short
4100  - http://guides.pm-research.com/content/2006/1/82.full
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.