A Holistic Approach to Financial Data Science: Data, Technology, and Analytics

Abstract

The scientific analysis of financial data, both for practical and theoretical purposes, has continuously been an active and evolving area. Traditionally, financial modeling and analysis have primarily relied on financial econometrics, which applies statistical methods to the problems of finance. Recently, advances in analytical and algorithmic methods, powerful technological solutions, and the expansion of the financial data ecosystem have created new opportunities and provided novel and robust ways to solve existing and new problems in finance. To expand the scope of financial econometrics and incorporate these novelties, the field of financial data science (FDS) has emerged. Crucially, FDS adds more interdisciplinary, methodological variety and technological components to the analysis process, thus generating the need for a holistic view in the management of an FDS project. In this article, the author presents a holistic and structured approach to efficiently and effectively manage an FDS project. The main contribution of the article is to provide a comprehensive, financial domain–oriented approach to FDS management that can be used by researchers and practitioners to exploit the opportunities arising from the evolving finance landscape.

TOPICS: Statistical methods, simulations, big data/machine learning

Key Findings

• • Financial data science is presented as an emerging field that complements classical financial econometrics.

• • The financial data ecosystem is discussed in detail, illustrating the types and structures of financial data and their distinguishing features.

• • A strategy of nine interrelated parts is proposed to efficiently manage a financial data science project. The proposed approach can be used by industry professionals and academic researchers.