More than 25 years of quantitative investing sees Robeco and technology come full circle
Investment company leverages next-generation research in its investing strategies, driven by the rise of artificial intelligence
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With the release of platforms such as ChatGPT, facets of artificial intelligence (AI) including natural language processing (NLP) and machine learning have become firmly embedded in the public consciousness, creating excitement about their potential impact on society and the economy.
But at Robeco, an asset management company headquartered in the Netherlands with offices across the globe, the journey of integrating novel – or new kinds of signals – began much earlier, in 2019. These signals are indicators that help a portfolio manager rank the attractiveness of assets into its enhanced index strategies. Over the last few years, with the maturation of data availability and the help of AI tools such as NLP, the company has been able to harvest new short-term timing signals more systematically, providing additional sources of “alpha”, or measure of performance, for its quantitative strategies.
Robeco has been at the forefront of quantitative (quant) investment research for more than 30 years, contributing to both academic research and client portfolios. It has established itself as a leader in quant investing, which uses algorithms and mathematical models to identify trading opportunities.
Robeco’s next-generation quant researchers – who integrate innovative approaches and advanced technology into their investment strategies – are also entering the new realm of alternative data. While traditional sources of data, such as financial statements and market prices, have long been tapped by investors for insights, this new realm means exploring unconventional or non-traditional types of data that have not been used in the past for investment decisions.
Next-generation quant researchers are able to use “web-scraped data” to monitor alternative sources, including social media and online reviews in real time for a tech product launch, giving them an edge over investors waiting for quarterly reports to gauge sentiment around the product’s reception. Using NLP allows researchers to analyse such data, separating the “noise” from the potential signals. They can, for instance, monitor the number of job vacancies at firms, viewing an increase as an expectation of future growth.
The use of these next-generation techniques and new data sources allows for more complex and adaptive investment strategies that can navigate the ever-changing conditions in financial markets. These tools do not just enhance Robeco’s existing quant strategies, such as its benchmark-aware active quant strategies, but also enable it to create new, next-generation strategies.
Emerging themes
These initiatives include Robeco’s multi-thematic equities strategy that harnesses AI to detect emerging themes such as cancer treatments and satellite communications, and identifies when to enter or exit themes and particular companies within those associated companies.
The strategy uses a rigorously tested NLP algorithm to detect themes within a vast amount of alternative data, including company earnings, news stories and management interviews. It then uses a different algorithm, based on sentiment analysis – a process which classifies whether something is positive, negative or neutral, based on vocabulary choice, for example – to select the most attractive companies.
Avoiding ‘AI washing’
While many asset managers are realising the potential of AI, uncertainty remains about factors that asset allocators or fund selectors should look for when evaluating the credentials of AI claims. Mike Chen, head of next-gen research at Robeco, believes innovation has become an overused word.
“When assessing the quantitative investing capabilities of an asset manager, it’s important to look beyond marketing claims,” he says. “Do asset managers invest sufficiently in building their proprietary data sets? And do they have a thoughtful, measured and transparent process, with a team that understands the proper use and potential misuse of new tools and data?”
Quant and human intelligence
Robeco classifies itself as a top-tier quant house with strong fundamental equities and fixed-income teams. The company says it is one of the few investment houses to excel at leveraging the strengths of both quant investing and a fundamental human approach. In fact, the Robeco quant team started out by providing stock ranks for the portfolio managers’ input in its fundamental emerging market team.
Today, the quant team can get feedback from the fundamental teams on dynamics the quant model might not pick up, such as stock-specific events, sector-specific adjustments or macro considerations in emerging markets. The fundamental teams can use quant tools to identify promising investment opportunities, relying on a combination of the quant group’s proven factor research and next-gen signals.
In the future, alternative data, machine learning and NLP will enhance collaboration by improving both quant models and fundamental research, thereby strengthening its offerings. Robeco says asset managers that can adapt and leverage the growing power of data and AI techniques will see differentiated advantages.
Find out more about Robeco quant investing and its active quant strategies here.
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Alpha refers to the excess return of an investment relative to a benchmark index and is a measure of performance.
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