The Data Science Revolution
How the new richness and accessibility of data, and advances in data science, are enhancing both quantitative and traditional fundamental investment research—and sparking a revolution in active management.
So-called “big data”—the residue of information that we all leave behind as we buy things, sell things, browse the high street and the internet, use our smartphones and generally live our modern lives—is proliferating. At the same time, advances in cloud computing, machine learning and artificial intelligence allow us to extract coherent, strategic insights from these digital residues. Combined, as data science, they have the potential to be a richly enhanced source of information about our world—information that is deeper and more detailed than we have ever had before, and yet also broader and more comprehensive.
Spreading from its origins in technology and retail, data science is now making waves in the information- and research-driven world of finance and investment.
Here, we look at what big data means in this new context. With real-world examples, we explore why data science informs rather than replaces traditional investment research. We describe the importance of information flows between quantitative and traditional fundamental research and how they can be enhanced still further with data science. In fact, we argue that the true power of data science lies there—which has implications for the way a modern active management business should structure its research efforts.
Big data is out there, waiting to change our expectations of what investment research can achieve. To harness it, active managers need to embrace data science, but also structure their research efforts to exploit its full potential.
Poorly selected swatches of data, acquired passively and uncritically, compromised by gaps and anomalies and hosted on inappropriate platforms, will only take investors so far—and probably not far enough to justify the costs. For most practitioners, therefore, we believe disillusionment will follow and the hype will burn itself out.
The ones left to benefit will be those that not only make the investments in data science expertise and technology infrastructure that are necessary to glean strategic insights from the datasets, but also recognize its structural and governance implications. These are the practitioners who will take the big science revolution beyond the hype.