The data advantage: How web scraping and NLP give investors a decision-making edge

Business Times

29-05-2025

In the past week, you would likely have interacted with artificial intelligence (AI) in one way or another. Whether it was with a customer service chatbot or simply unlocking your mobile phone with facial recognition, AI has seamlessly found its way into our daily lives.
But before AI even became part of everyday vocabulary, one company was already exploring how it could be used in determining investment strategies.
In 2019, global asset management firm Robeco tapped on natural language processing (NLP), which is a form of AI, to help them analyse large volumes of text and signals to find patterns that might influence markets.
An international asset manager with one of the world's largest quant equity research teams, Robeco has been at the forefront of quant research, contributing to both academic research and client portfolios for over 30 years.
While traditional sources of data such as financial statements and market prices have long been tapped by investors for insights, this new realm of alternative data means exploring unconventional or non-traditional types of data that have not been used in the past for investment decisions.
Next-generation quant researchers who delve into such data also use 'web scraped data' 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.
Using 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.
Evolving along with financial markets
The use of these next-gen 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 benefit existing quant strategies, such as Robeco's benchmark-aware active quant strategies, but also enable the firm to create new next-generation strategies.
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.
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.
Making the right choice
Today, 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.
Chen explains that Robeco is a top-tier quant house and one of the few with strong fundamental equities and fixed income teams. In fact, the Robeco quant team started out by providing stock ranks for the portfolio managers' input in their fundamental emerging market team.
Today, the quant team can get feedback from the fundamental teams on dynamics 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 both quant models and fundamental research, thereby strengthening the firm's offering. 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.
Disclaimer:
Important information – capital at risk
This information refers only to general information about Robeco Holding B.V. and/or its related, affiliated and subsidiary companies, ('Robeco'), Robeco's approach, strategies and capabilities. This is a marketing communication intended solely for professional investors, defined as investors qualifying as professional clients, who have requested to be treated as professional clients or who are authorized to receive such information under any applicable laws. Unless otherwise stated, the data and information reported is sourced from Robeco, is, to the best knowledge of Robeco, accurate at the time of publication and comes without any warranties of any kind. Any opinion expressed is solely Robeco's opinion, it is not a factual statement, and is subject to change, and in no way constitutes investment advice. This document is intended only to provide an overview of Robeco's approach and strategies. It is not a substitute for a prospectus or any other legal document concerning any specific financial instrument. The data, information, and opinions contained herein do not constitute and, under no circumstances, may be construed as an offer or an invitation or a recommendation to make investments or divestments or a solicitation to buy, sell, or subscribe for financial instruments or as financial, legal, tax, or investment research advice or as an invitation or to make any other use of it. All rights relating to the information in this document are and will remain the property of Robeco. This material may not be copied or used with the public. No part of this document may be reproduced, or published in any form or by any means without Robeco's prior written permission.
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