“It is difficult to make predictions, especially about the future.”¹ Yet predictions are an unavoidable necessity for any investor seeking to position their portfolio for 2020 and beyond. Any portfolio weighting decision should account for expectations of risk and return.
This leaves us with two sets of predictions: risk (correlation and volatility) and return. Risk is relatively easy; return is harder. Risk tends to be strongly persistent through time, and long-term risk is predictable using even very simple models.² While we believe that portfolio construction should rely heavily on risk inputs and diversification, the need for return forecasts remains unavoidable.³ So this still leaves us as investors with the considerably difficult task of estimating future returns.
Since any method of portfolio construction requires either explicit or implicit return assumptions, we prefer to make them explicit, so these assumptions can be examined and deemed sensible before driving critical investment decisions. What options do we as investors have at our disposal in determining our long-term return expectations? Differing asset definitions, data sets, and methodological details can result in a vast diversity of individual models and estimates, but we think most boil down to one of the following estimation archetypes:
- Estimating long-run return premia from historical data, as done in our whitepaper Forecasting Factor Returns.⁴
- Building up forecasts from current valuations, combining estimates for sub-components of longer-term returns such as income and growth expectations.
- Harnessing the wisdom of crowds, by surveying or estimating the average forward-looking views of market participants.
This Street View delves into a particular quantitative example of the third method: harnessing the wisdom of crowds. By applying a concept called reverse optimization to the worldwide investable asset portfolio, we show how long-term factor and asset class returns can be estimated from the aggregated allocation decisions of all investors.
We believe this exercise can help investors in a couple of ways. First, the implied return estimates for key risk factors and asset classes derived from global market allocations can be used as an input when formulating your own capital market assumptions, a key input into asset allocation decisions. Second, while we apply this reverse optimization methodology to the global market portfolio, an investor could perform this analysis on their own
portfolio to see what their own implied expectations are for future returns based on their existing asset allocation.
We’ll begin the Street View by setting up the reverse optimization problem and defining one of its key inputs: the global investable market portfolio. In the next section, we will uncover the outputs from the reverse optimization and translate them into a more usable format by establishing a bottoms-up “anchor” return estimate for global equity. Finally, we will estimate the long-term implied returns for several asset classes and see how they compare to realized returns over the past decade.