Harnessing big data analytics for informed decision-making in financial markets

We live in the Digital Age, marked by unparalleled volume and variety of data production, popularly referred to as "Big Data". Financial services and markets have long relied on data-driven decision-making, and have witnessed and contributed to the current Big Data Revolution. Big Data Analytics offer both challenges and opportunities to practitioners, regulators and researchers. Great amounts of diverse data now regularly and increasingly become available, made possible by technological advances in telecommunications and computation, as well as the available digital infrastructure. Financial data are not only produced by traditional financial exchanges, but also generated by new technology platforms, where businesses and customers interact dynamically. In an era of information overload, fueled by the content and marketing strategies adopted by traditional financial news providers and popular social media, how can financial professionals cheapen the costs of decision-making while improving accuracy and quality? By using innovative models, algorithms and methodologies, that are able to reduce the dimensionality of the information space and extract useful signals from the noise (Gupta & Gupta, 2017; Khan et al., 2018; Lee & Kim, 2019). This is a highly challenging task, which requires collaboration between interdisciplinary teams involving professionals from different backgrounds, including finance, computer science and engineering, mathematics and statistics, economics and social sciences. Policy-makers and regulators should also be part of these teams, particularly in ensuring a fair playing field for industry players.