Machine Earning – Algorithmic Trading Strategies for Superior Growth, Outperformance and Competitive Advantage

44 Pages Posted: 31 Mar 2021

See all articles by Nicholas Burgess

Nicholas Burgess

University of Oxford, Said Business School

Date Written: March 29, 2021

Abstract

In this paper we use the tools and frameworks from Oxford University’s postgraduate diploma in financial strategy to study the performance and benefits of algorithmic trading strategies (algos), and specifically those that use artificial intelligence (AI) and machine learning (ML).

We discover using valuation theory from (SBS2, 2020) that algos generate superior returns compared to human discretionary trading both in normal market conditions and during large market drawdowns, such as during the coronavirus (COVID-19) pandemic. Furthermore applying financial strategy techniques from (SBS1, 2020) we find that algos could be combined with existing core competencies at my organization RUS1 to create a sustainable competitive advantage and give RUS an edge over its competitors.

Finally considering M&A growth strategies from (SBS4, 2020) we conclude that for RUS algorithmic trading capabilities would be best acquired taking an organic approach as an inhouse build approach would be both cost-effective and allow for a more customized and bespoke integration.

Even if only a fraction of the potential benefits are monetized, algo trading could have a significant positive impact on earnings, which in turn would allow for reinvestment to facilitate sustainable growth and maintain a sustainable competitive advantage.

Keywords: algorithmic trading, algos, strategies, artificial intelligence, AI, machine learning, ML, macro environment, Covid19, Coronavirus, PESTEL analysis, growth, value proposition, SWOT analysis, VRINO analysis, strategy canvas, core competencies competitive advantage, mergers and acquisitions, M&A

JEL Classification: E44, E47, E60, E64, E66, F23, F37, F47, F55, F62, F65, G24, G32, G33, G34, O31, O32, O33, O43

Suggested Citation

Burgess, Nicholas, Machine Earning – Algorithmic Trading Strategies for Superior Growth, Outperformance and Competitive Advantage (March 29, 2021). Available at SSRN: https://ssrn.com/abstract=3816567 or http://dx.doi.org/10.2139/ssrn.3816567

Nicholas Burgess (Contact Author)

University of Oxford, Said Business School ( email )

Oxford, OX1 5NY
United Kingdom

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