Big Data Science in Finance

Preface

Financial technology has been advancing steadily through much of the last 100 years, and the last 50 or so years in particular. In the 1980s, for example, the problem of implementing technology in financial companies rested squarely with the prohibitively high cost of computers. Bloomberg and his peers helped usher in Fintech 1.0 by creating wide computer leasing networks that propelled data distribution, selected analytics, and more into trading rooms and research. The next break, Fintech 2.0, came in the 1990s: the Internet led the way in low-cost electronic trading, globalization of trading desks, a new frontier for data dissemination, and much more. Today, we find ourselves in the midst of Fintech 3.0: data and communications have been taken to the next level thanks to their pure volume and 5G connectivity, and Artificial Intelligence (AI) and Blockchain create meaningful advances in the way we do business.

To summarize, Fintech 3.0 spans the A, B, C, and D of modern finance:

• A: Artificial Intelligence (AI)
• B: Blockchain technology and its applications
• C: Connectivity, including 5G
• D: Data, including Alternative Data

Big Data Science in finance spans the A and the D of Fintech, while benefiting immensely from B and C.

The intersection of just these two areas, AI and Data, comprises the field of Big Data Science. When applied to finance, the field is brimming with possibilities. Unsupervised learning, for example, is capable of removing the researcher's bias by ...