Four lessons: New technologies are changing asset management

Technology is transforming the way we all work, and asset managers are no exception. We are discovering new innovations that could lead to better investment outcomes for our clients.

With each passing 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 (computer programs that analyse human speech), image recognition (the automated recognition of objects, people, places and writing) and machine learning (computer algorithms that improve through experience) to analyse it and uncover new investment insights.

BlackRock’s Systematic Active Equity (SAE) investment team has been using these technologies to analyse alternative data for more than a decade. During this time, the team has learned four key lessons about how to make the most of these technologies within an investment process.

Capital at risk. The value of investments and the income from them can fall as well as rise and are not guaranteed. Investors may not get back the amount originally invested.

Lesson 1: Target the “right” data and multiple data sources

Investment teams need to cast their net wide when looking for the right Big Data, but they also need to home in on the data sets that may help them reach their investment goals.

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Asset managers have on estimate nearly tripled their spending on data since 2017 (Alternativedata.org, June 30, 2019). But more important than the amount of data obtained is its quality and how it is used in investment decision-making.

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The global economy and financial markets are highly complex, so the data they generate are often unstructured and ‘noisy’. It’s important to evaluate as much data as possible to determine what might be of benefit to the investment process. To achieve this, Big Data needs to be structured in the right way – it then has the potential to provide valuable insight into company and industry prospects. Once we’ve found a data set that we believe may be able to provide these insights, it is cleansed and put through a number of statistical tests to ensure it can add value to the stock-selection process.

Lesson 2: Technology is only as good as the hands it’s in

The ability of technology to analyse larger amounts of data within seconds is impressive, but it is human expertise that brings technology’s power to life.

For example, machine-learning algorithms analyse reams of data and generate insights – determining relationships between a vast number of data inputs in a way that adapts to ever-changing data patterns. In doing so, machine learning can learn faster than humans and quickly analyse large amounts of data. Though the computational and adaptive ability of machine learning exceeds human ability, the ultimate success of machine learning relies on human expertise to develop and refine it.

Managers with specialised skill, resources and infrastructure are best equipped to take advantage of all the available data inputs. Sustainability issues are a growing investor priority and still in their infancy, meaning they can suffer from limited reliable datasets. This area in particular can require dedicated capabilities to extract an advantage. (See our Q&A with Debbie McCoy, Managing Director and Head of Sustainable Investing within the Systematic Active division)

Ultimately, data and technology are only as good as the hands they are in. Investors are wise to consider an investment team’s expertise, culture and experience in translating the power of technology into better investment outcomes. This kind of investing is all about staying one step ahead, so investment teams must be committed to finding new data sources and innovative technologies.

Risk. There can be no guarantee that the investment strategy can be successful and the value of investments may go down as well as up

Lesson 3: Technologies can – and do – lose their potency

Markets are continuously changing, so every investment opportunity has a lifecycle. To keep up with new opportunities as others fade, a successful investment team needs to:

1) Generate new ideas
2) Find new data sources
3) Explore innovative ways of using existing technology to analyse data

Consider, for example, the evolution of the SAE team’s natural-language-processing techniques for analysing data from natural-language processes– in this instance, corporate language to help forecast company prospects. The initial work focused on measuring sentiment through counting a predefined list of positive versus negative words. As time went on, the SAE team began to focus on the source and target audience of the text that was being analysed. One finding: remarks by Chief Executive Officer (CEO) tended to be more positive and scripted – so of less use. However, focusing on the Chief Financial Officer’s remarks or the Q&A sections of a CEO call with shareholders proved consistently more useful. Comparing sentiment from one source (such as information given to the market) with another source (such as information given to a regulator) also helped identify instances of company spin versus the hard facts.

Risk. There is no guarantee that research capabilities will contribute to a positive investment outcome

Lesson 4: Collaboration breeds innovation

Data generated by technologies like the internet and smartphones can be structured to provide investment insights. And, although dedicated data and computer science expertise is required to analyse the data, specific technical expertise is not enough. Knowing which data sets to target also requires expertise across markets, industries and asset classes.

A culture of collaboration and constructive debate is vital to successful innovation. Team members – from senior to the most junior – must be encouraged to work together regularly to improve current data techniques and to test ideas.

A culture that enables innovation to thrive is created over many years and depends on the right leadership and team chemistry.

This material is not intended to be relied upon as a forecast, research or investment advice, and is not a recommendation, offer or solicitation to buy or sell any securities or financial product or to adopt any investment strategy.