Covid-19 accelerated markets’ entropy and risk takers will evolve their investment approaches
Crises create disruption and Covid-19 is no exception, bringing new complexities, new opportunities and new risks to the investment landscape.
From our research angle, we realised that the pandemic triggered:
2. A common global ‘health’ shock affecting economies and spill-overs to the full spectrum of asset classes.
3. A deep disconnect between macro fundamentals and markets.
4. A larger role for central authorities in market functioning. Monetary policies in particular have been overwhelming asset classes’ specific drivers, blurring market correlations and inducing (dangerous) mispricing.
The net effect of these interventions remains difficult to ascertain, but it is clear we need to evolve our thinking in order to analyse this new macro financial ecosystem.
How we have analysed this evolving situation and our findings
We soon realised that the global health crisis was having a huge impact on economies via an unusual shock potentially triggering some structural changes. However, we remain convinced that macro factors anchored financial markets. Growth, inflation, rates, policy efforts and liquidity are likely to remain pivotal, so, the nature of this crisis requires an evolution in the way we look at these factors.
2. We built GREAT (Global Risks Exposure Attribution Tool) to systematise our analyses.
3. If macro factors influence markets dynamics in a more complex and unusual manner, then a macroeconomic (risk) factor-based approach3 is suited to capturing the broad common risks across an investment universe. This is why we calculate asset classes’ sensitivities to these factors on some proxies based on our internal forecasts and elaborations.
We revisited and consolidated our macro factor-based approach to asset allocation to enforce portfolio diversification, reduce volatility, and possibly improve portfolios outcome.
Moreover, the risk is that such an environment exacerbates the weakness of traditional asset allocation which operate on silos of asset-class bucket holdings (equity, fixed income, commodities, FX). Covid-19 has been the opportunity for our Regime-Based Approach to move one step forward towards operating in a more explicit factor-based fashion, using non-linear thinking and artificial intelligence to empower the search for cross-asset opportunities amid unconventional market behaviours. In our opinion, a macro (risk) factor4 asset allocation that allows for the budgeting of risk assigned to each macro factor while positioning cross asset according to the investor’s high convictions5 is the right approach in an environment in which macro factors are not only influencing markets, but are the “minimum common factor” on the a global scale.6
GREAT allows for the calculation of portfolio exposure to risk factors (beta) while tracking beta changes and risk budget consistency when different factor tilts are input.
The entire process has been fascinating and as well as challenging.
Remaining in control of the full process was paramount. Our regime-based framework articulated in the base and alternative scenarios with some clear references in terms of macro-economic assumptions and financial consequences had been quite helpful to fix reference values to initialise with some a priori the clustering procedure when flooded with tons of results. Furthermore, we also had to move beyond the comfort zone of time series analysis and parametric estimation. Consistency, reliability and significance of results become more challenging when directed at large volumes of information and evolving structural relationships. Machines deliver faster than humans, but it is the human brain that has to define the problem, to apply the most suitable pragmatic technique, and, eventually, to use common sense and judgement to interpret the significance of the final solution.We haven’t changed our thinking in that regard.
Factor-based asset allocation cuts across traditional asset classes, diversifying returns streams on a ‘truly’ cross-asset dimension.
We identified these macro (risk) factors as the key top-down discriminants cross assets amid the five financial regimes we defined ex ante. We derived state-dependent sensitivities for our investment universe.
As the path to reach the new equilibrium in the day after will not be straight, we have been adopting non-linear thinking to elaborate big data aimed at improving the comprehension of asset class sensitivities around risk factors.
The pandemic made a virtue of necessity.
1. We penciled in base and alternative scenarios and framed them into our financial regimes’ mapping to spot some asset class behaviour references.
2. We calculate expected returns on a broader asset class spectrum and frame them into base and alternative scenarios. We accelerated and sharpened our use of artificial intelligence to capture non-linear connections between macro factors and markets and dynamically assess the exposure to the macro (risk). To control and ensure the consistency of the outcome in terms of market sequencing and investment consequences, the algorithms run within the base and alternative scenarios information window9.
3. We use GREAT to nest our high convictions (including those on risk factors specifically) and derive the asset allocation preference.
High convictions and asset class preferences
Recovery is our central case. Hence, growth factors remain paramount. The recession should be confined to H120, followed by a short rebound in Q320, and then slow and bumpy convergence to pre-crisis levels (2022). Price dynamics should evolve from disinflation to below CB targets, with potential spikes down the road on components base effects (oil). Policy accelerators support risk assets. However, the decoupling from their fundamentals increases downside risks. In the medium term, we expect EPS to bounce back in a V-shaped fashion.
We expect ultra-accommodative monetary policies to persist, leading to stable and low interest rates worldwide. Potential upside for rates will be tempered by the strong demand coming from central banks while the recalibration vs the short end of curves will ease tensions in the long end. In fact, governments are financing the emergency with short-dated issuance. Central banks’ purchasing programmes and state guarantees safeguard default rates, at least in the short term. Turbocharged monetary policy should support easy financial conditions. Carry appeal in a low-yield environment overcomes the compressed expected returns.
In the table, we present the asset class preference according to our outlook convictions. Sensitivity to (geo)political risk is not explicitly considered. This is when we intervene with the qualitative layer to exclude PXJ and Latam equities from the recommendations, for example. Govies, gold and IG tick almost all our boxes, while European and US equity represent a more tactical play.
Learning never exhausts the mind
As a result of Covid-19, the financial world has become increasingly complex, irrational, non-linear and does not fit into a rigid and static analysis architecture. Macro factor expectations are more than ever driving financial markets.
The crisis triggered a transition to a new economic and financial equilibrium dynamically evolving into waves.
The current situation is a perfect storm: the world’s entropy has increased exponentially.
In such an environment, we believe that assessing macro risk factors is key to generating returns for investors and a cross-asset approach is required. In our opinion, tilting to individual macro-economic risk factors and combining asset classes under this new framework should facilitate diversification, improve risk budgeting and eventually improve portfolio returns.
Very often in the past, many investment ideas perceived as alpha opportunities have been involuntary beta transfer on factors not fully understood by risk takers. For this reason, a disciplined top-down approach that evaluates a factor in its articulation and interdependence across asset classes allows for the transformation of a specific risk into clear investment opportunity.
Research methodology has to evolve to fit the need to find answers to external shocks quickly in a flexible manner.
Over the last quarter, we moved forward on our implementation of machine learning and big data to economics, taking advantage of the learning systems’ ability to adapt to a changing macro financial environment.
Our approach to asset allocation evolved as well: we shifted from asset class to risk factor diversification, as Covid-19 is a shock shared across all economies and asset classes. Risk exposure is global, but the impacts on asset classes are local.
We believe that – in order to be competitive – asset mixes will need to be defined on factor tilts rather than traditional risk/return combinations. In addition, looking at return streams, portfolio objectives will need to shift from being benchmark-based to being goal-oriented. This will decide any change to asset allocation7.
You cannot use up creativity. The more you use, the more you have.
Dr Maya Angelou
New Frontiers for Central Banks
, The Day After #5, D. Borowski, May 2020.