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huMan and machine,
not human versus machine

huMans and machines can create better outcomes

Asset managers are using big data to produce sustainable alpha ahead of others. But limits exist for both humans and machines and only when they connect can they produce better results.

Big data models also need to be refined and reviewed over time. Ancillary tests following a basic scientific method are often necessary.

Humans are emotion-driven and susceptible to cognitive biases
Machines lack an understanding of human thinking
Together they can create better outcomes

The powerful union of human and machine


Humans bring a much more sophisticated judgment on data but algorithms make things faster.
For example, Artificial intelligence can help fund managers go through something like 4,000 brokerage reports a day with 36,000 pages in 53 languages

Higher chance of success

It is easy to “get lost” with a lot of data so humans are there to judge if something is going in line with expectations or not

Deeper understanding

Machine learning and portfolio manager expertise are combined to analyse complex securities. For example, commercial mortgage-backed securities loans can contain hidden details, an algorithm alone might not be able to detect, so manual intervention is required

There’s a lot of noise in markets and you need an understanding of the market environment to put things into perspective and then make a call whether you want to sit tight, let it play for a bit longer or maybe something has changed structurally and indeed, the right action might be to change course and not use that particular source of data anymore.

Simon Weinberger

Managing director and portfolio manager, BlackRock

How are asset management firms embracing big data analysis?


Asset managers predominantly use big data to refine investment models and leverage alternative data. Companies are boosting their teams’ expertise with data scientists in order to make the most of big data.

But BlackRock goes a step further by integrating data science experts and investment professionals. While there needs to be dedicated data and computer science expertise within an investment team to analyse the data, integrating teams with diverse skill sets has shown having specific expertise on a team is not enough.