Abstract

“Did algorithmic trading generate superior returns relative to discretionary trading during the Covid19 pandemic and do they provide a sustainable competitive advantage?” In this paper we use the tools and frameworks from Oxford University’s postgraduate diploma in financial strategy to answer this question and 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 found that algos could be combined with existing core competencies at my organization RUS 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 in-house 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, AI, machine learning, Covid-19, PESTEL analysis, SWOT analysis, VRINO analysis, strategy canvas