Teaching Open Science Analytics in the Age of Financial Technology

Barry Quinn

No 2022/01, QBS Working Paper Series from Queen's University Belfast, Queen's Business School

Abstract: We consider the challenge of teaching open science analytics in finance in the computer age. There is a crisis of confidence in science; especially finance. We argue that the unstoppable algorithmic transformation of financial services and the nascent field of financial machine learning provide an opportunity to redesign finance programmes for the age of financial technology. We argue it is time for a rethink of how we can extract reliable statistical inference from financial data given the proliferation of computing, 'Big Data,' and theunstoppable 'algorithmisation' of the finance industry. The paper begins by agnostically profiling the modelling paradigm choice. Next, we establish the developments in statistical inference in the computer age. Finally, we consider the idea of placing computation as acentral tenant in the finance curriculum and discuss the infrastructure and tools involved. We illustrate a use case where the infrastructure is on-boarded in a cloud computing suite with enterprise-level server software. We are not arguing that finance is computation, ratherby placing computation as a frictionless part of the curriculum, students can engage with the full suite of state-of-the-art inferential tools available to financial data science practitioners

Date: 2022
New Economics Papers: this item is included in nep-pay
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:qmsrps:202201

DOI: 10.2139/ssrn.4019430

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