New Technologies Changing
Asset Management

Aug 16, 2018

Summary

  • Each second, oceans of new data are being generated by the Internet, smartphones, satellites and other innovations. This data is commonly referred to as “Big Data” or “alternative data.”
  • Many asset managers are seeking to harness the power of Big Data by using technologies like natural language processing, image recognition and machine learning to analyze it and find investment insights.
  • BlackRock’s Systematic Active Equity (SAE) team has been using innovative technologies to analyze alternative data for more than a decade. Over this time, the SAE team has learned four key lessons.
Lesson One
  • Key questions to ask:

    • What is their current number of alternative data sources and how much has that number grown over time?
    • What is their annual budget for new data sources and has it grown each year over the last five years?
    • Describe the processes in place to assess new sources of data.
    • What procedures does the asset manager follow to ensure its investment process is not compromised by privacy breaches of personal data?
Lesson Two
  • Key questions to ask:

    • How many data and computer scientists are dedicated to the firm’s investment teams, and what are their years of experience in their fields and their tenure at the firm?
    • What special steps (if any) is the firm taking to recruit and retain its data and computer science talent?
    • In what ways are data science, computer science and other technology expertise integrated into the investment process?
    • In what ways are data and computer scientists included in the vetting process for incorporating (or improving) new technology to ensure that the new technology will add value?
Lesson Three
  • Key questions to ask:

    • Over the past year, in what ways has the asset manager innovated the technologies that it uses to analyze alternative data within its investment process?
    • Give examples of the ways that innovations over the past year have found differentiated sources of alpha.
    • How is innovation incentivized on the team (e.g., is it connected to compensation)?
    • How long is the typical innovation cycle, from idea to implementation in live portfolios?
Lesson Four
  • Key questions to ask:

    • Does the asset manager have a culture of collaboration and constructive debate (that can sustain innovation over time)? Give examples.
    • On a daily basis, how exactly do data and computer scientists collaborate with team members with more traditional investment roles and backgrounds?
    • Meet with data scientists (and other technologists), portfolio managers and junior level members on the team. How would they describe their team’s culture?
    • Is knowledge sharing required and facilitated among all team members through formalized means, such as through an intranet page?