By Hendrik Bartel
Co-Founder and CEO, TruValue Labs
It’s no exaggeration that alternative data and the means to harness it have become must-haves for asset managers seeking superior market returns. Experts agree that factoring alternative data into investment analyses delivers an advantage that will separate the winners from the also-rans in the years ahead.
Consider this comment from EY: “We recommend all active managers begin their journey to augment their investment process with these new insights from alternative data sources.”
Or this from BlackRock: “We believe that in order to generate sustained alpha, investors should embrace acquiring, analyzing, and understanding the fast-growing universe of data. Those who are unable to do so run the risk of falling behind in a rapidly changing investment landscape.”
And here’s what Goldman Sachs has to say: “Big data can enable asset managers to see hidden connections and relationships between companies, including across industries … leading to a potential advantage in selecting investments.”
Experts are also noting the impact AI and big data are already having on passive strategies and ETFs, augmenting human judgment with the ability to analyze and synthesize millions of data points in seconds, and render unbiased, data-driven decisions.
So if there’s widespread agreement that big data analytics is a necessity to a disciplined investment process, the question becomes: how do you achieve it? How do you gain access to alternative data as well as the means to draw insights from it? Does it mean you have to hire a team of AI experts and data scientists to build a solution from scratch?
The short answer – and the good news – is no. TruValue Labs has already done the heavy lifting. We’ve spent years developing a highly customizable, AI-powered analytics engine capable of collecting unstructured data from a vast universe of sources, extracting relevant metrics and turning them into meaningful insights. It uses state-of-the-art machine learning and natural language processing (NLP) to quickly separate the signals from the noise, at the speed of events as they occur.
Moreover, our solution is rooted in deep subject matter expertise in ESG, intangible risk factors, and performance materiality. We’ve already built hundreds of signals and have a representative set of more than five years of back data. And to top it off, not only can our engine help you harness big, unstructured data, but it can also find signals in the proprietary data you have already accumulated.
Quite simply, there’s no reason to fear being left behind or to invest in a lengthy development project with little assurance of success. The solution to your alternative data needs is available now, and the advantages are just waiting to be seized.