Alternative Data for FinTech and Business Intelligence

32 Pages Posted: 11 Feb 2020

See all articles by Lin William Cong

Lin William Cong

Cornell University

Beibei Li

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management

Tony Zhang

University of Illinois Urbana Champaign

Date Written: October 10, 2019

Abstract

The authors introduce recent research in economics and business-related fields utilizing data from unconventional sources or of unstructured nature. Highlighting unifying themes of such big data and the methodologies for analyzing them at scale, this chapter elaborates the applications of (i) textual analysis in corporate finance, investment, and macroeconomic forecasts, (ii) image processing in financial markets and governance, (iii) digital footprints from social media and mobile devices, and (iv) emerging data from the Internet of Things. The authors also discuss promising directions of using alternative or unstructured data for both academics and practitioners.

Keywords: Alternative Data, Digital Economy, FinTech, Textual Analysis, Internet-of-Things

JEL Classification: C55, C81, C82

Suggested Citation

Cong, Lin and Li, Beibei and Zhang, Qingquan, Alternative Data for FinTech and Business Intelligence (October 10, 2019). Available at SSRN: https://ssrn.com/abstract=3521349 or http://dx.doi.org/10.2139/ssrn.3521349

Lin Cong (Contact Author)

Cornell University ( email )

Ithaca, NY 14853
United States

HOME PAGE: http://www.linwilliamcong.org

Beibei Li

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management ( email )

Pittsburgh, PA 15213-3890
United States

Qingquan Zhang

University of Illinois Urbana Champaign ( email )

Champaign, IL 61820
United States
6128406736 (Phone)
61820 (Fax)

HOME PAGE: http://https://giesbusiness.illinois.edu/profile/qingquan-zhang

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