The emergence of algorithmic trading as a viable trading platform has created the need for enhanced trading analytics to compare, evaluate, and select appropriate algorithms. The lack of transparency of many algorithms (due to undisclosed execution methodologies), however, limits investors’ ability to measure the associated cost, risk, and efficiency of execution. In this chapter, we define a dynamic decision making framework that allows investors to select appropriate algorithms based on pre-trade goals and objectives. The approach employs a three step methodology requiring 1) selection of price benchmark, 2) specification of trading style–passive to aggressive, and 3) determination of adaptation ...
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