Collaborative Intelligence
A Powerful Combination of Human Insight and Leading-edge Technology
At Unigestion, we believe in collaborative intelligence and in the strength of humans and machines working together. We empower our investment teams with leading-edge technology to gain a deeper understanding of financial markets. This combined approach helps us manage risk effectively across our investment strategies to deliver better outcomes for our investors.
Markets are becoming increasingly complex and technology is advancing at a rapid pace. The way in which people interact, transact, work and consume has changed significantly from even a decade ago. In the financial world, technological innovation has unleashed a digital vortex that is transforming the asset management industry. In this fast-paced environment, the methods we use to navigate markets need to adapt constantly.
We combine sophisticated, systematic analysis with the forward-looking insights of our investment experts
Systematic Rigour
Unigestion’s systematic, model-based analysis of assets and markets provides a robust and repeatable means of evaluating risk. Our sophisticated, proprietary tools leverage some of the latest developments in machine learning and big data. They allow us to process vast samples of data – and to do so fast and often. Our models also provide structure and process, to help strip out emotional bias and an overreliance on any one individual.
However, the best investment ideas do not come from machines alone. Models can identify regime shifts and new risk factors, but are weak at interpreting data and adapting to new paradigms. Certain risks, such as geopolitical risk or ESG issues, require human vision to foresee and understand. Machines can only base their analysis on historical data, and so unprecedented events cannot be addressed through a purely quantitative process. Qualitative analysis and experience will always be of value at these times.
Human Wisdom
We complement our systematic models with discretionary, forward-looking analysis within and across asset classes. Our investment teams scrutinise the quantitative output, identify areas for improvement and overlay it with their assessment of future risks. Their collective experience, knowledge of market structures and interpretation of market nuances are critical to our assessment of risk. We implement all our discretionary views in a process-driven, collaborative way to minimise human bias.
Analysing financial markets is not an exact science; it is influenced by human behaviour, which is challenging to quantify. Computers excel in responding to well formulated questions with clear objectives. Humans remain key in asking the right questions, interpreting the results and adapting as markets evolve. We believe that a collaboration between human intelligence and machine-led analysis leads to smarter, faster and better-informed investment decisions.
We believe that a collaboration between human intelligence and machine-led analysis leads to smarter, faster and better-informed investment decisions.
Collaborative Intelligence in Action
At Unigestion, we use many proprietary systematic models when managing our investment strategies, but always with the oversight of our investment teams. Our investment managers work side-by side with quantitative analysts to maximise knowledge sharing and awareness of fundamental shifts in market sentiment. Here are just some of the ways we use collaborative intelligence across our investment decision-making processes:
Dynamic Risk Targeting for the Overall Portfolio
A key part of our multi asset risk management process is deciding how much risk to take on. We use both systematic and qualitative inputs to determine whether to run our portfolios at full risk or to implement hedging strategies.
For example, around scheduled events such as central bank meetings, we aim to ‘de-risk’ our portfolios if we believe markets are underestimating the likelihood of a negative outcome. We are able to quickly re-leverage the portfolios following the event to benefit from the market’s reaction. This type of event is challenging to capture systematically, so we rely on the judgement and experience of our team.
As an illustration, we anticipated a high likelihood of market volatility around the Brexit referendum. We therefore reduced risk by raising our cash position ahead of the vote. We also purchased some call options on European indices, which would generate performance in a positive market environment.
Understanding Risk across and within Asset Classes
We also use collaborative intelligence when assessing relative value across and within asset classes. Our macro risk-based asset allocation is primarily determined through a systematic process, but we also closely monitor market risk using qualitative techniques. For example, we evaluate current pricing to help avoid tail risk mispricing and monitor market positioning to identify overcrowding. We have developed a number of tools to structure our qualitative assessments:
- Carry analysis at a historical and cross-sectional level for asset valuation.
- Tracking market positioning by analysing different strategies’ beta and data from the Commodity Futures Trading Commission (CFTC).
- Analysing statements from G10 central banks and building a ‘heartbeat monitor’ for key institutions.
- Monitoring cross-asset behaviour via global asset correlations and Sharpe ratio analysis.
Here again, our investment teams are critical in interpreting the data and assessing the implications for portfolio allocation.
Understanding Equity Risk
Our equity process starts with a systematic screening model that sorts stocks based on criteria such as price stability, diversification, earnings quality and liquidity. Automating this initial screening allows us to increase scalability and better focus our team’s resources. However, systematic analysis does not tell the whole story about future risks. For this reason, we have embedded a discretionary risk assessment at every stage of the investment process. We conduct in-depth analysis of top-down and company-specific risks that are difficult to model, including:
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This human insight and validation allow us to anticipate new risks and adapt our portfolios accordingly. One important example is the inclusion of carbon risk within our 360-degree risk management process. Carbon emissions are now high on the global agenda, driven by greater investor awareness and regulatory changes. This was clearly not always the case and is a risk that systematic models would struggle to anticipate.
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Using Technology to Collaborate with Clients
We also use collaborative intelligence to co-create tailored solutions for our clients. Sharing our asset allocation and risk management models with clients is key to building an open and transparent dialogue. It allows us to highlight any potential dislocation between their portfolio risk profile and their investment goals. We can also show the impact of their preferences on risk, liquidity and expected returns, while integrating any regulatory or ESG constraints. This is becoming increasingly important as investors move into more sophisticated asset classes to achieve their investment goals.
