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

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.