TY - JOUR T1 - Proofs and Cross-Validations: <em>Three Lessons for Financial Data Science</em> JF - The Journal of Financial Data Science SP - 12 LP - 18 DO - 10.3905/jfds.2019.1.009 VL - 1 IS - 4 AU - Joseph Simonian Y1 - 2019/10/31 UR - https://pm-research.com/content/1/4/12.abstract N2 - Like any new research program, financial data science must successfully demonstrate its utility to researchers who are accustomed to working with more established analytical frameworks and tools. This is especially important in the early stages of financial data science, where much of the methodological groundwork of the field will be laid. Given this, in this article the author draws on the history of mathematics, an exemplar of a successful scientific endeavor, to provide three lessons for researchers in financial data science that the author hopes will assist them in aligning their research priorities more closely with those of mainstream finance. The author closes the article with some additional guidance on the related topic of effectively writing about and presenting financial data science research.TOPICS: Statistical methods, quantitative methods, big data/machine learningKey Findings• Financial Data Science must be in epistemic dialogue with traditional finance.• Financial Data Science must aim for epistemic transparency.• Financial Data Science must aim for epistemic connectivity. ER -