We combine sophisticated, systematic analysis with the forward-looking insights of our investment experts
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 enhance our risk management processes, and to strengthen our ability to identify and manage risks across our investment strategies.
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 – to enrich our thinking, inform our decisions and create better outcomes for our investors.
Unigestion’s systematic, model-based analysis of assets and markets provides a robust and repeatable means of evaluating risk and potential return in a scalable way. Using sophisticated, proprietary tools, it leverages some of the latest developments in machine learning and big data for a comprehensive view of risk. Systematic models provide structure and process, to help strip out behavioural bias and an overreliance on any one individual.
However, the best investment ideas do not come from machines alone. Machines can only base their analysis on historical data, but certain risks, such as geopolitical or corporate governance issues, require human vision to foresee and understand. Unprecedented events cannot be addressed through a purely quantitative process – qualitative analysis and experience will always be of value at these times.
We complement our systematic approach with discretionary, forward-looking analysis within and across asset classes. Our investment teams scrutinise and validate the quantitative output, identify areas for improvement and overlay it with their current assessment of future risks. Their collective experience, knowledge of market structures and interpretation of market nuances are critical to our evaluation 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 clearly defined activities and respond to well formulated questions, but humans remain key in formulating the right questions, defining the end goal and interpreting the results. We believe that this collaboration between human intelligence and machine-led analysis leads to smarter, faster and better-informed investment decisions.
Collaborative intelligence in action
Here are just some of the many ways we use collaborative intelligence across our investment 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. Both systematic and qualitative inputs are used to determine whether to run the portfolio at full risk, to tactically de-lever or to implement specific hedging strategies.
For example, around scheduled events such as central bank meetings or political events, we aim to ‘de-risk’ our portfolios if we believe markets are underestimating the probability of a negative outcome. We are able to quickly re-leverage the portfolio following the event to benefit from the market’s reaction. This type of event is challenging to capture systematically, especially where this is no precedent, so we rely on the judgement and experience of our team.
As an illustration, we anticipated a high likelihood of volatility around the Brexit referendum and deteriorating risk/reward for market beta exposure. We therefore reduced our risk exposure 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 combine systematic and discretionary analysis 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 discretionary, qualitative techniques. For example, we evaluate current pricing to help avoid tail risk mispricing and monitor market positioning to identify overcrowding. We also closely watch monetary policy to anticipate correlation shocks and track cross-asset correlations to help highlight valuation concerns. We have developed a number of tools to structure these 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 the speeches and statements from G10 central banks and building a dedicated ‘heartbeat monitor’ for the key institutions, such as the ECB and US Federal Reserve.
- Monitoring cross-asset behaviour via global asset correlations and Sharpe ratio analysis.
Here again, the experience of our investment teams and their understanding of the market are critical in translating this quantitative information into a better understanding of asset risk and how this should be interpreted in terms of allocation changes.
- Understanding equity risk
Our long experience in equity investment has shown us that controlling a portfolio’s risk is about more than optimising for the lowest volatility – 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 our investment process. We conduct in-depth analysis of top-down and company-specific risks that are difficult to model, including:
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, but this was clearly not always the case and is a risk that systematic models would struggle to anticipate.