The importance of human and machine

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Part 2:
Technology is little help without the expertise to use it

The ability of technology to analyse a massive amount of data within seconds is so impressive it can make us forget that it is human expertise that brings technology’s power to life. Take machine learning.

Machine learning in investment management


Not only can machine learning algorithms analyse reams of data in a flash and generate insights, they can determine relationships between a vast number of data inputs in a way that adapts to changing data patterns. In doing so, machine learning can learn faster than humans in addition to quickly analyse large amounts of data.

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Though the computational and adaptive ability of machine learning exceeds human ability, the ultimate success of the application (or any technology) is still reliant on human beings developing and refining it. To that end, SAE has built up its data and computer science capabilities over the past decade through hiring and developing talent, so we can both create and improve the technologies used in our process.

Hiring data scientists gains momentum

Source:, as of March 31, 2018.

Example case study: Signal combination

We explain what our research expertise looks in practice.

What is signal combination?

Signal combination is a machine-learning model which SAE researchers developed to learn the relationship between stock returns and a vast array of quantitative data, including accounting information and analyst forecasts.

How does the model analyse information?

One issue highlighted by our research was that the model was treating periods of stable performance and infrequent episodes of drawdown with equal importance when analysing market data.

How did research evolve the model?

This observation led to an important change where the algorithm was refined to penalise drivers of extreme negative short-term performance.

Put another way: SAE researchers had to calibrate and train the model to think and learn like an investor.

For illustrative purposes only.

Technology is not enough

When designing a machine learning technique it is additionally important to take into account what we as investors have already learned about markets. Making sure that new technology is adding value to existing models is a key tenant of any piece of SAE research. Thus, the expertise that is needed encompasses not just the ability to create and refine technology but also knowing to ask the right questions to vet ideas. For example, a highly sophisticated model using cutting-edge techniques, which simply rediscovers well-documented stock market behaviours such as “momentum” or “value” would represent a highly inefficient use of research resources. In practice, we encourage the technology we use to reveal insights we as investors know less about.

Download a Big Data due diligence checklist

Many asset managers hope to discover unique investment insights by applying machine learning and Big Data analysis. Download a checklist of questions to ask when conducting due diligence on this topic.

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