The race is on for asset managers to find differentiation in the way they use artificial intelligence (AI) in their investment strategies. Many ‘quant’ approaches are looking for advanced techniques and data to generate additional investment performance, but only a select few will succeed.


Emmanuel Bourdeix

Chief Executive Officer
Seeyond
The way to generate alpha through AI is by combining infrastructure and methodology that capitalises on computing power and automation.

When did Seeyond decide to become an active quant investor?

Seeyond’s DNA and history have been branded as active quantitative for many years now. As rational investors, we combine the discipline of robust and interpretable model-based approaches with the active oversight and human accountability of financial market experts.

With the use of proprietary algorithms and quantitative tools dating back to 2007, but also given the technological progress the industry has known over the past years, it was only logical to us to evolve from Active Quant 1.0 to Active Quant 2.0. As an innovation driven firm, we are continuously looking for advanced techniques and data to bring added value.

Among potential pillars of transformation such as operational efficiency, distribution or risk management, Seeyond has chosen to focus on the use of AI for generating additional alpha. We believe the way to get there is through the combination of infrastructure and methodology capitalizing on computing power and automation.

In a nutshell, what is the key difference between traditional modelling and machine learning?

Beyond bells and whistles, we consider AI as a natural evolution of existing statistical techniques that we already implement across our investment process. Artificial intelligence is not only a question of more complex models but more a question of ‘experimental protocol’ and ‘infrastructure’.

Put simply, traditional modelling is a ‘handmade’, human-centred process, based on statistical inference, so it’s hypothesis driven. Machine learning, on the other hand, is an agnostic, computer-centred process – so, it’s based on autonomous learning – and is based on KPIs, so it’s objective driven.

When comparing both approaches when applied to an investment process, one difference clearly stands out: traditional modelling is incremental and time consuming, while machine learning is automatic and scalable.

How does AI change the role of traditional portfolio managers?

Artificial intelligence usually refers to the ability of a machine to learn from itself to execute human-like intelligence. For an asset manager, this means exploring new strategies within an enhanced decision-making framework. It involves critically analysing huge datasets in an efficient systematic manner, which is what we call the ‘Enhanced Portfolio Manager’.

Nevertheless, we think that AI power needs to be used carefully and with great awareness. At Seeyond, we consider it our fiduciary responsibility as an expert asset manager to provide our clients with an optimal risk return assessment and a deep comprehensive understanding of the models we use and their outcomes. These bringreadability and reliability to our clients. This is what we call being an ‘Accountable Portfolio Manager’.

How would you describe your AI journey as an asset manager?

We’ve always leveraged human decision making to rationalize new risk or market opportunities. We have therefore been gradually including AI internally.

One of our projects is to explore several AI use cases based on a robust machine-learning pipeline with a fintech specialized in machine learning infrastructure. With this framework, we aim to be able to quickly generate signals and explore new extra financials data sources.

But we don’t limit ourselves to organic growth and are also open to strategic partnerships with cutting-edge start-ups to explore new AI strategies. For example, we are currently co-developing a prototype that’s able to generate alternative stock picking signals with a fintech specialized in pattern recognition.

This cross-expertise approach is another growth driver for Seeyond and enables us to work in a more agile ecosystem. We learn fast on computer science expertise while they benefit from our business experience.

How have you seen AI and machine learning techniques develop over time?

Investors often think that ‘the same causes lead to the same consequences’. It’s why we created the ‘Back to the future’ tool.

This is a machine learning-based indicator that analyses US macroeconomic time-series through eight stages. We then apply signal treatment’s techniques to model each cluster’s evolution in time by a level and a trend time-series.

At any point in time, the state of the US economy can be read though a 16-dimension analysis: eight clusters by two factors, which is the level and trend.

Finally we use a machine-learning algorithm to screen history and detect periods where the US economy was similar as it is today.