Brooks, C, Hoepner, AGF, McMillan, D, Vivian, A and Wese Simen, C 
ORCID: 0000-0003-4119-3024
(2019)
Financial data science: the birth of a new financial research paradigm complementing econometrics?
European Journal of Finance, 25 (17).
pp. 1627-1636.
ISSN 1351-847X, 1466-4364
Abstract
Financial data science and econometrics are highly complementary. They share an equivalent research 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 digitalisation. First, the ever-increasing computational power allows researchers to experiment with an extremely large number of generated test subjects (i.e. p-hacking). We argue that p-hacking can be mitigated through adjustments for multiple hypothesis testing where appropriate. However, it can only truly be addressed via a strong focus on integrity (e.g. pre-registration, actual out-of-sample periods). Second, the extremely large number of observations available in big data set provides magnitudes of statistical power at which common statistical significance levels are barely relevant. This challenge can be addressed twofold. First, researchers can use more stringent statistical significance levels such as 0.1% and 0.5% instead of 1% and 5%, respectively. Second, and more importantly, researchers can use criteria such as economic significance, economic relevance and statistical relevance to assess the robustness of statistically significant coefficients. Especially statistical relevance seems crucial, as it appears far from impossible for an individual coefficient to be considered statistically significant when its actual statistical relevance (i.e. incremental explanatory power) is extremely small.
| Item Type: | Article |
|---|---|
| Additional Information: | Source info: The European Journal of Finance, Forthcoming |
| Uncontrolled Keywords: | Financial data science, econometrics, big data, novel datasets, risk measurement |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 10 Mar 2020 13:30 |
| Last Modified: | 24 Jan 2026 02:09 |
| DOI: | 10.1080/1351847X.2019.1662822 |
| Open Access URL: | https://www.stir.ac.uk/research/hub/publication/14... |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3078475 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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