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

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.

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