Competitive advantage in algorithmic trading: a behavioral innovation economics approach

Abstract

Purpose

This paper investigates the strategic behavior of algorithmic trading firms from an innovation economics perspective. The authors seek to uncover the sources of competitive advantage these firms develop to make markets inefficient for them and enable their survival.

Design/methodology/approach

First, the authors review expected capability, a quantitative behavioral model of the sustainable, or reliable, profits that lead to survival. Second, they present qualitative data gathered from semi-structured interviews with industry professionals as well as from the academic and industry literatures. They categorize this data into first-order concepts and themes of opportunity-, advantage- and meta-seeking behaviors. Associating the observed sources of competitive advantages with the components of the expected capability model allows us to describe the economic rationale these firms have for developing those sources and explain how they survive.

Findings

The data reveals ten sources of competitive advantages, which the authors label according to known ones in the strategic management literature. We find that, due to the dynamically complex environments and their bounded resources, these firms seek heuristic compromise among these ten, which leads to satisficing. Their application of innovation methodology that prescribes iterative ex post hypothesis testing appears to quell internal conflict among groups and promote organizational survival. The authors believe their results shed light on the behavior and motivations of algorithmic market actors, but also of innovative firms more generally.

Originality/value

Based upon their review of the literature, this is the first paper to provide such a complete explanation of the strategic behavior of algorithmic trading firms.

Citation

Cooper, R., Currie, W.L., Seddon, J.J.M. and Van Vliet, B. (2023), "Competitive advantage in algorithmic trading: a behavioral innovation economics approach", Review of Behavioral Finance, Vol. 15 No. 3, pp. 371-395. https://doi.org/10.1108/RBF-06-2021-0119