The power of technology is nothing new in asset management with systematic investing, sometimes referred to as quant investing, being an established part of the industry. Numerous reports by the large consulting houses, such as PwC, predict that AI will generate cost savings and increase efficiency, resulting in portfolio managers making more informed decisions and ultimately leading to better returns.
AI and big data enables decisions to be based on a larger number of sources of data than before. Now the data can be accessed in real time unlike traditional financial information which is only published at regular intervals. New technology and algorithms enable the analysis of masses of market data, risk evaluation and identification of asset classes that can deliver maximum returns.
But how does the asset management industry use AI in practice to its advantage and specifically how do they use competitive intelligence platforms in their investment process?
Bjarne Graven Larsen, a financial services and pension investment veteran, spoke with Twingly about the Qblue Balanced approach. Graven Larsen, formerly the CIO of ATP, the largest pension fund in Denmark, and more recently the CIO of Ontario Teachers’ Pension Plan (OTPP), one of the largest in Canada, founded the Copenhagen-based company five years ago. Under his leadership, the company has amassed 1.4 billion Euros in assets. As a systematic investor with a strong focus on sustainability, Graven Larsen’s team extensively utilizes alternative information sources, such as competitive intelligence platforms, in its investment process.
He said that there are many stereotypes about quant managers, such as lack of transparency with so called black-box approaches, data-mining, excessive risk taking, irrespective of the fact that there are many different types of quant approaches.
“Qblue Balanced starting point is always fundamental, i.e analysing company financials and other data, but it uses quantitative methods to reduce noise in the implementation of investment strategies. One of Qblue’s differentiators is its sustainability focus, where easily available, or traditional, data is not always granular or multidimensional enough,” he said.
Qblue uses empirical evidence and data-driven insights and disciplined portfolio construction, as opposed to discretionary, qualitative analysis used by most active managers. Graven Larsen said systematic rule-based approaches are not only more cost effective but also able to minimise behavioural human biases in decision-making.
As a manager with a sustainability focus, specifically climate risk and mitigating adverse sustainability impacts, Graven Larsen argues that it is even more important to use as wide a range of sources of data as possible. ”To have a fuller picture we need alternative sources because most of the traditional data comes from the companies themselves which only tells you one part of the story,” he said.
“In an increasingly competitive industry we must make use of all available, reliable sources to get as much useful information as possible in order to boost performance, while sticking to our principles. In addition, understanding probabilities is vital,” he said.
He added that a lot of the traditional data have a large-cap bias because large companies have the resources to report and disclose their datasets, which is often why they score better on traditional environmental, social and governance (ESG) data. “But you will not be a successful investor, nor solve the big global problems, such as climate change, if you do not identify innovative companies with a competitive edge. To find these companies, which are often smaller and younger, you have to sift through masses of data, and this is where AI and alternative data sets are very useful,” he said.
In order to identify these innovative companies, Qblue utilizes both external and internal resources. One such external company is Matter, a Danish firm that provides granular Environmental, Social, and Governance (ESG) data to investors. “The company scans hundreds of thousands of pages daily from NGOs, universities, newspapers, and other reliable sources. Applying machine learning techniques to this high-frequency data enables us to understand what is happening at a company in terms of their alignment with the 17 United Nations’ Sustainable Development Goals (SDGs), for instance. Our funds have strong alignments with several SDGs, including SDG 8*, 9*, and 13*. This approach gives us insights into how a company aligns with these goals and how it is perceived, based not only on the data they report themselves,” explained Graven Larsen.
He added that this approach also reduces the risk of greenwashing, where companies make unsubstantiated green or environmental claims. Furthermore, Qblue Balanced has created two unique datasets on Climate Transition Innovation and SDG Innovation using AI techniques to analyze millions of patent data dating back to 1995, covering more than 20,000 companies.
“AI and big data are kicking up the information flow a notch, and not just by replacing humans, as many believe. While it may do so to a certain extent, AI, combined with new data sources, brings innovative methods and possibilities to investment management. The speed, scope, and reliability, particularly in sustainability-related data, are increasing,” Graven Larsen noted.
“Success for asset managers, particularly those relying on proprietary data sources rather than open-source ones, is becoming more valuable and a key differentiator in a competitive industry. Consequently, large asset managers are striving to build their own proprietary datasets, which have significant intrinsic value,” he explained.
“We see ourselves cooperating with external providers who have smart people that can guide us and program the algorithms, or define and train them to meet our specific criteria. When we receive data, it's just a signal. We then have to filter out the noise and use our internal resources to rate the data as negative, positive, or neutral. The real value lies in how this is implemented and interpreted into the investment process,” Graven Larsen said.
“The ability to receive datasets faster and at a lower cost is driving investments and will change the way analytics is conducted in financial services in the future,” he said.
“Beyond the hype of AI, we do still need smart humans to ask the right questions and interpret the data that all these sources provide,” Graven Larsen concluded.
By Pirkko Juntunen
Twingly offers News APIs with over 3 million daily news articles from 170,000 active global sources. It gives you access to noise-free news data for timely updates and comparisons. As a complement, please also have a look at our Blogs API, where we monitor 3 million active blogs worldwide.
*SDG 8 – aims to promote inclusive and sustainable economic growth, full and productive employment and decent work for all
*SDG 9 – aims to build resilient infrastructure, promote sustainable industrialisation and foster innovation. It has eight targets and progress is measured by 12 indicators. The first five targets are outcome targets and the remaining three are means of implementation targets
*SDG 13 – aims to take urgent action to combat climate change and its impacts
