“Did algorithmic trading generate superior returns relative todiscretionary trading during the Covid19 pandemic and do they provide asustainable competitive advantage?” In this paper we use the tools andframeworks from Oxford University’s postgraduate diploma in financialstrategy to answer this question and study the performance and benefitsof algorithmic trading strategies (algos), and specifically those thatuse Artificial Intelligence (AI) and Machine Learning (ML). We discoverusing valuation theory from (SBS2, 2020) that algos generate superiorreturns compared to human discretionary trading both in normal marketconditions and during large market drawdowns, such as during thecoronavirus (Covid-19) pandemic. Furthermore applying financial strategytechniques from (SBS1, 2020) we found that algos could be combined withexisting core competencies at my organization RUS to create asustainable competitive advantage and give RUS an edge over itscompetitors. Finally, considering M&A growth strategies from (SBS4,2020) we conclude that for RUS algorithmic trading capabilities would bebest acquired taking an organic approach as an in-house build approachwould be both cost-effective and allow for a more customized and bespokeintegration. Even if only a fraction of the potential benefits aremonetized, algo trading could have a significant positive impact onearnings, which in turn would allow for reinvestment to facilitatesustainable growth and maintain a sustainable competitive advantage.
Source: SSRN Strategic Analysis of Japanese Megabanks (Burgess, 2020c) Nicholas Burgess / Int.Artif.Intell.&Mach.Learn. 2(1) (2022) 38-60 Page 50 of 60 Figure 12: SWOT Analysis for RUS
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Source: Strategic Analysis of Japanese Megabanks (Burgess, 2020c) Prior to assessing if algorithmic trading skills and capabilities could complement the existing value chain we performed a SWOT7 analysis (Whittington et al., 2020), see (Figure 12). This was to assess RUSs current capabilities, internal strengths and weaknesses, and current ability to manage the external opportunities and threats presented in Figure 1. RUS are currently unable to monetize core competencies due to capital constraints, low trading volumes and high cost to revenue ratios. Furthermore limited performance metrics act as a business tax that disables management from understanding RUSs value proposition, its core strengths and weaknesses. It also diminishes their ability to maximize profits, reduce costs and manage key risks.
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Furthermore using a competitor SWOT analysis (Figure 13) to contrast RUSs core competencies against key competitors reveals that RUS are only able to achieve competitive parity. RUS are better placed to manage external threats but poorly placed to exploit external opportunities. The SWOT analysis suggests RUS are more risk averse than its competitors. It is well placed to manage external threats from coronavirus workforce disruption to regulatory Libor reforms (Burgess, 2019a). However it is poorly placed to exploit advances in technology and lucrative government green finance initiatives. A VRINO8 analysis (Galpin, 2020) helps to evaluate if, how and to what extent an organization has a value chain (Figure 11) with resources and capabilities that when combined can achieve and sustain a competitive advantage (Whittington et al., 2020).
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We performed a VRINO analysis, in Burgess (2020c), based on RUSs current capabilities and value chain. In this paper we extend this analysis to examine if investing in algorithmic trading capabilities9 and combining this with the existing agile pricing and risk analytics (Burgess, 2020c) could create a new core competency Advanced Automation of Pricing, Risk and Execution that could offer superior trading and risk management capabilities and give RUS a sustainable competitive advantage. The VRINO analysis is performed below. 7 An acronym for strengths, weaknesses, opportunities and threats 8 VRINO is an acronym for Valuable, Rate, Inimitable, Non-substitutable and Organisationally Appropriable, sometimes also referred 9 to as VRIO without the Non-substitutable element. In this paper we are specifically referring to algorithmic trading systems that use artificial intelligence, machine learning and predictive technologies.
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