Strategic Asset Allocation

QMS Advisors’ strategic investment decision making process relies on assumptions that incorporate future market expectations as well as the expected changes both in the underlying fundamentals and structure of the markets. We rely both on our qualitative and quantitative in-house research, as well as on the inputs and expertise of a range of industry contacts such as portfolio managers and product specialists to derive our expectations. We strive to ensure that our assumptions and their underlying rationales are consistent across asset classes by submitting them to rigorous qualitative and quantitative reviews.



QMS Advisors offers a review of its framework for deriving return, volatility and correlation expectations for sovereign and corporate bonds, equities, alternative investments (hedge funds, private equity, commodities and real estate), and foreign exchange. Our strategic asset allocation process involves 45 markets across seven asset classes for which our team provides long-term total return forecasts, volatility and correlation estimates.  Our approach consists in obtaining a set of model-derived expectations, and to further refine our forecasts with numerous qualitative inputs; a process that relies on the contributions of a range of industry experts including economists, portfolio managers, and product specialists.

Our rigorous quantitative and qualitative review processes ensure that our assumptions are based on sound economic and financial rationales. We further strive to utilize both comparable methodologies and common return drivers across assets to achieve consistency across our expectations (i.e. universal underlying macroeconomic assumptions):
  • Consistency with economic theory and practice: a wide array of economic and market factors are combined in order to derive robust return expectations for each asset class.
  • Consistency across business cycles: Macroeconomic factors are chosen for their ability to explain returns over multiple economic cycles.
  • Consistency across asset classes: Expected returns reflect congruent pricing of risk, measured by the exposure of each asset class to economic and financial factors.
  • Capture dynamic market features: Interaction between economic and financial signals and the variations in asset classes’ potential returns and risks over time.

The object of this exercise is to arrive at five-year return and volatility forecasts for each of the assets, which are then used as inputs for the final optimization process. To an extent, forecasting returns for a five-year period is less error-prone than for a much shorter period and also lends itself to a greater reliance on longer-term fundamentals as drivers of future performance. 
  • We implicitly assume that -as suggested by empirical evidence- most of the key variables used in our models will converge over the long-run. Therefore bond yields, GDP and dividend growth are expected to converge over longer periods. 
  • For most asset classes we use clearly specified multi-linear regression models to forecast returns, while relying on traditional models in the cases of equities and foreign exchange (Dividend Discount Model and Fair Value Model, respectively).
  • Additionally, all our models are supported by cross-checking procedures that aim to rationalize the initial forecast outputs.

QMS Advisors believes that regime-based asset allocation combined with tail risk hedging has the potential to deliver significant benefits when compared to traditional investment policies, which are most commonly static and benchmark based. Contingent on economic foresight, implementation of a regime-based approach can potentially help mitigate downside risks and add to cumulative performance over time, translating to an improved distribution of overall portfolio returns.
  • Current macroeconomic uncertainties have translated into fat tails and significant negative skewness in the distribution curves of most asset classes' returns. QMS Advisors believes that these non-Normal features can arise from the mere possibility of multiple equilibria, even if those multiple equilibria individually appear normal. Empirical evidence also suggests that markets tend to settle in specific states after a short period of transit between states, and that they remain in those temporary states until new macro economic conditions develop.
  • The current regime-switching environment markets we are in indicate that the core building blocks of asset allocation and option pricing should allow for the possibility of multimodality. This in our view significantly changes the conceptual approach towards portfolio construction and risk management. 
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