Competitive Advantage in Algorithmic Trading: A Behavioral Innovation Economics Approach

Review of Behavioral Finance, Vol. 15, Issue 3

40 Pages Posted: 11 Mar 2022 Last revised: 3 Jan 2024

See all articles by Ricky Cooper

Ricky Cooper

Illinois Institute of Technology - Stuart School of Business, IIT

Wendy Currie

Audencia School of Management

Jonathan Seddon

Audencia Nantes School of Management

Ben Van Vliet

Illinois Institute of Technology - Stuart School of Business

Date Written: January 13, 2022

Abstract

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

Methodology: First, we review expected capability, a quantitative behavioral model of the sustainable, or reliable, profits that lead to survival. Second, we present qualitative data gathered from semi-structured interviews with industry professionals as well as from the academic and industry literatures. We 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 we 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. We believe our results shed light on the behavior and motivations of algorithmic market actors, but also of innovative firms more generally.

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

Suggested Citation

Cooper, Ricky and Currie, Wendy and Seddon, Jonathan and Van Vliet, Ben, Competitive Advantage in Algorithmic Trading: A Behavioral Innovation Economics Approach (January 13, 2022). Review of Behavioral Finance, Vol. 15, Issue 3, Available at SSRN: https://ssrn.com/abstract=4008511

Ricky Cooper

Illinois Institute of Technology - Stuart School of Business, IIT ( email )

10 West 35th Street, 18th Floor
Chicago, IL 60616
United States

Wendy Currie

Audencia School of Management ( email )

8 Route de la Joneliere
Nantes Cedex 3, 44312
France

Jonathan Seddon

Audencia Nantes School of Management ( email )

8 Route de la Joneliere
Nantes Cedex 3, Cedex 3 44312
France

Ben Van Vliet (Contact Author)

Illinois Institute of Technology - Stuart School of Business ( email )

Stuart Graduate School of Business
565 W. Adams St.
Chicago, IL 60661
United States

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