Alternative data in finance and business: emerging applications and theory analysis (review)

Yunchuan Sun (), Lu Liu (), Ying Xu (), Xiaoping Zeng (), Yufeng Shi (), Haifeng Hu (), Jie Jiang () and Ajith Abraham ()
Additional contact information
Yunchuan Sun: Beijing Normal University
Lu Liu: Beijing Normal University
Ying Xu: Beijing Normal University
Xiaoping Zeng: Beijing Normal University
Yufeng Shi: Shandong University
Haifeng Hu: Beijing Normal University
Jie Jiang: Beijing Normal University
Ajith Abraham: Scientific Network for Innovation and Research Excellence Auburn

Financial Innovation, 2024, vol. 10, issue 1, 1-32

Abstract: Abstract In the financial sector, alternatives to traditional datasets, such as financial statements and Securities and Exchange Commission filings, can provide additional ways to describe the running status of businesses. Nontraditional data sources include individual behaviors, business processes, and various sensors. In recent years, alternative data have been leveraged by businesses and investors to adjust credit scores, mitigate financial fraud, and optimize investment portfolios because they can be used to conduct more in-depth, comprehensive, and timely evaluations of enterprises. Adopting alternative data in developing models for finance and business scenarios has become increasingly popular in academia. In this article, we first identify the advantages of alternative data compared with traditional data, such as having multiple sources, heterogeneity, flexibility, objectivity, and constant evolution. We then provide an overall investigation of emerging studies to outline the various types, emerging applications, and effects of alternative data in finance and business by reviewing over 100 papers published from 2015 to 2023. The investigation is implemented according to application scenarios, including business return prediction, business risk management, credit evaluation, investment risk prediction, and stock prediction. We discuss the roles of alternative data from the perspective of finance theory to argue that alternative data have the potential to serve as a bridge toward achieving high efficiency in financial markets. The challenges and future trends of alternative data in finance and business are also discussed.

Keywords: Alternative data; Behavioral data; Commercial data; Credit evaluation; Enterprise management; Finance innovation; Investment; Market efficiency; Price prediction; Risk evaluation; Sensing data (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1186/s40854-024-00652-0 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-024-00652-0

Ordering information: This journal article can be ordered from
http://www.springer. ... nomics/journal/40589

DOI: 10.1186/s40854-024-00652-0

Access Statistics for this article

Financial Innovation is currently edited by J. Leon Zhao and Zongyi

More articles in Financial Innovation from Springer, Southwestern University of Finance and Economics
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().