Financial Data Science: The Birth of a New Financial Research Paradigm Complementing Econometrics?

The European Journal of Finance, Forthcoming

21 Pages Posted: 14 May 2020

See all articles by Chris Brooks

Chris Brooks

University of Reading - ICMA Centre

Andreas G. F. Hoepner

Smurfit Graduate Business School, University College Dublin; Stockholm School of Economics - Mistra Financial Systems (MFS); European Commission's Technical Expert Group for Sustainable Finance

David G. McMillan

University of Stirling

Andrew Vivian

Loughborough University

Chardin Wese Simen

University of Liverpool Management School

Date Written: August 2, 2019

Abstract

In this paper, we compare and contrast financial data science with econometrics and conclude that the former is inevitably interdisciplinary due to the numerous skill-sets needed within a competitive research team. The latter, in contrast, is firmly rooted in economics. Both areas are highly complementary, as they share an equivalent process with the former’s intellectual point of departure being statistical inference and the latter’s being the data sets themselves. Two challenges arise, however, from the age of big data. First, the ever increasing computational power allows researchers to experiment with an extremely large number of generated test subjects and leads to the challenge of p-hacking. Second, the extremely large number of observations available in big data sets provide levels of statistical power at which common statistical significance levels are barely a challenge. We argue that the former challenge can be mitigated through adjustments for multiple hypothesis testing where appropriate. However, it can only truly be addressed via a strong focus on the integrity of the research process and the researchers themselves, with pre-registration and actual out-of-sample periods being the best technical though in themselves potentially insufficient tools. The latter challenge can be addressed in two ways. First, researchers can simply use more stringent statistical significance levels such as 0.1%, 0.5% and 1% instead of 1%, 5% and 10%, respectively. Second, and more importantly, researchers can use additional criteria such as economic significance, economic relevance and statistical relevance to assess the robustness of statistically significant coefficients. Especially statistical relevance seems crucial in the age of big data, as it appears not impossible for an individual coefficient to be considered statistically significant when its actual statistical relevance (i.e. incremental explanatory power) is extremely small.

Keywords: Big Data, Econometrics, Financial Data Science, Statistical Relevance, Statistical Significance Levels

JEL Classification: C10, C52, C55, G00, G20, G23

Suggested Citation

Brooks, Chris and Hoepner, Andreas G. F. and McMillan, David G. and Vivian, Andrew and Wese Simen, Chardin, Financial Data Science: The Birth of a New Financial Research Paradigm Complementing Econometrics? (August 2, 2019). The European Journal of Finance, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3580729 or http://dx.doi.org/10.2139/ssrn.3580729

Chris Brooks

University of Reading - ICMA Centre ( email )

Whiteknights Park
P.O. Box 242
Reading RG6 6BA
United Kingdom
+44 118 931 82 39 (Phone)
+44 118 931 47 41 (Fax)

Andreas G. F. Hoepner (Contact Author)

Smurfit Graduate Business School, University College Dublin ( email )

Blackrock, Co. Dublin
Ireland

Stockholm School of Economics - Mistra Financial Systems (MFS) ( email )

MISUM
Box 6501, SE-113 83 Stockholm
Sweden

European Commission's Technical Expert Group for Sustainable Finance ( email )

2 Rue de Spa
Brussels, 1000
Belgium

David G. McMillan

University of Stirling ( email )

Stirling, Scotland FK9 4LA
United Kingdom

Andrew Vivian

Loughborough University ( email )

The Business School
Ashby Road
Loughborough LE11 3TU
Great Britain

Chardin Wese Simen

University of Liverpool Management School ( email )

Management School
University of Liverpool
Liverpool, L69 7ZH
United Kingdom

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