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Teaching Open Science Analytics in the Age of Financial Technology

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  • Quinn, Barry

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

Suggested Citation

  • Quinn, Barry, 2022. "Teaching Open Science Analytics in the Age of Financial Technology," QBS Working Paper Series 2022/01, Queen's University Belfast, Queen's Business School.
    • Handle: RePEc:zbw:qmsrps:202201
    DOI: 10.2139/ssrn.4019430
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    1. Daniel W. Apley & Jingyu Zhu, 2020. "Visualizing the effects of predictor variables in black box supervised learning models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 1059-1086, September.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    3. Mine Çetinkaya-Rundel & Colin Rundel, 2018. "Infrastructure and Tools for Teaching Computing Throughout the Statistical Curriculum," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 58-65, January.
    4. Jeffrey D. Blume & Robert A. Greevy & Valerie F. Welty & Jeffrey R. Smith & William D. Dupont, 2019. "An Introduction to Second-Generation p-Values," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 157-167, March.
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