Applications of Big Data Analytics in Investment Management: A Review and Future Research Agenda Using TCM Framework

Introduction

Investment management (IM) is a data-driven field where analytics aims to provide solutions across the data value chain continuum for improved performance. Globalization, deregulation, improved lifespan of people and economic reforms across nations have created newer opportunities with increased fund managers' responsibilities. Visualization of portfolio performance, complete transparency on investments, and shrinking margins due to the 'Fulcrum fee' structure (linking fees with performance) require the deployment of innovative tools. Increasing regulatory compliance costs with no assurance of new business without showing performance-based fund alpha in the volatile investment world have made funds management complex. Improved performance requires a holistic understanding of the factors and processes involved in deriving competitive advantage from analytics with active organizational support and cultural transformation. Due to the heterogeneity and size of the data and availability of ‘Open-Data,' the efforts required to retrieve relevant information and harness value from the big data are impossible without advanced analytical tools (Dean and Ghemawat, 2008; Maté et al., 2015; Galetsi et al.,2020; Yu et al.,2021). The volume of investible assets is projected to cross $100 trillion soon, with a portfolio of assets under management from developing economies like South America, Asia, the Middle East, and Africa set to rise (Asset Management,2020). Thus the managers from developing economies need to invest in analytics for making informed decisions in the place of intuitive judgment. Analytics is now a socially relevant activity for resource mobilization and gainful deployment of scarce resources across all sectors. The paper explores the role of analytics in investment management (Allison, 2017; Pattekar, 2019) using raw finance data analysis and its variants that impact decision-makers.