This is a sponsored article from Robeco.

The rise of artificial intelligence (AI) has spurred discussions about the future of investing. Robeco explores leveraging next-generation techniques in its quant investing and beyond.

Following the release of ChatGPT, AI concepts like natural language processing (NLP) and machine learning have become firmly embedded in the public consciousness, creating tremendous excitement about their potential impact on society and the economy. But at Robeco, our journey of integrating novel signals into our investment strategies began much earlier, in 2019. This was when we developed and implemented a new short-term timing signal, using NLP, in our model for our enhanced index strategies.

Next-generation quant researchers are also diving into the new realm of alternative data. While traditional data sources, such as financial statements and market prices, have long been tapped by investors for insights, this new realm means exploring unconventional or non-traditional data types that have not been used in the past for investment decisions.

For example, next-gen quant researchers can use ‘web scraping’ to monitor alternative sources like 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. NLP allows researchers to analyse such data, separating the noise from the potential signals. Another example is monitoring the number of job vacancies at firms, viewing an increase as an expectation for future growth.

These next-gen techniques and new data sources allow for more complex and adaptive investment strategies that can navigate the ever-changing conditions in financial markets. These tools not only benefit our existing quant strategies, such as our benchmark-aware active quant strategies, but also enable us to create new next-generation strategies.

Next-gen strategy

One example is 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.

Firstly, the strategy uses a rigorously tested NLP algorithm to detect themes within a vast amount of alternative data, including company earnings calls, news articles 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, for example, on vocabulary choice – to select the most attractive companies.

Avoiding ‘AI washing’

Many asset managers are jumping on the AI bandwagon. But what should asset allocators or fund selectors look for when evaluating the credentials of AI claims? “Sometimes ‘innovation’ is a very overused term,” says Mike Chen, head of Next-Gen Research at Robeco.

“When assessing the quantitative investing capabilities of an asset manager, it’s important to look beyond marketing claims. Do the asset managers invest sufficiently in building their proprietary data sets? And do they have a thoughtful, measured, and transparent process, with a team who understands the proper use and potential misuse of new tools and data?” he asks.

Combining quant and human intelligence

Robeco is a top-tier quant house with strong fundamental equities and fixed income teams. Very few houses excel at leveraging the strengths of both like we do. In fact, the Robeco quant team started out by providing stock ranks for the portfolio managers’ input in our fundamental emerging market team.

Today, the quant team can get feedback from the fundamental teams on dynamics that the 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 long-proven factor research and next-gen signals.

In the future, alternative data, machine learning, and NLP will enhance collaboration by improving quant models and fundamental research, thereby strengthening our offering. Asset managers that can adapt and leverage the growing power of data and AI techniques will see differentiated advantages.

About Robeco Quant Investing

For over 30 years, Robeco has been at the forefront of quant research, contributing to academic research and client portfolios. We have established ourselves as one of the leaders in quant with our quant equity AUM ranking among the top three managers globally* and one of the largest quant equity research teams worldwide.

Find out more about Robeco quant investing and our active quant strategies.
 

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*Source: eVestment, December 2024. Based on a quarterly internal benchmarking report from Robeco Market Intelligence, including all quant managers in the All Global and All EM Equity universes in eVestment.

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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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This is a sponsored article from Robeco.