Source: AFP
When unsure of the correct answer, the most effective strategy is to build a portfolio, consisting of a diverse set of exposures that provides both resilience and the potential for solid gains. This is true in investing as in other aspects of life.
Broadly speaking, "portfolio thinking" is an approach that involves spreading risk and improving potential rewards by diversifying decisions, investments, or efforts across a range of options, rather than relying on a single outcome. It can refer to any strategy which results in:
Here, we explore the concept of uncertainty. Recognising sources of uncertainty and factoring them into investment decisions are foundational to building out investment exposures and overall risk management.
Uncertainty sits at the core of investment decisions
Investing requires developing a view on expected (i.e., forward) returns, formed by considering a range of possible outcomes, each weighted by an assessment of their respective probabilities. When viewed on a standalone basis, any investment is undertaken because of an expectation that it can provide a favourable return, given a certain degree of acceptable downside. The actual return may and does usually deviate from the expected return. The downside may be deeper or shallower than originally anticipated. These divergences between predicted and actual outcomes, or so-called “errors in estimation,” can result in unintended outsized losses. Investors have at least some aversion to uncertainty surrounding expected returns because it introduces unpredictability into their investment outcomes. As a result, uncertainty makes it harder to plan for financial goals, manage risk, and maintain confidence in a portfolio strategy.
Often, even though we as investors recognise that uncertainty around an expectation exists, we proceed to apply a simple point estimate to anchor investment decisions. Instead, we can conceptually regard, and measure expected returns and downside risk, as ranges. These ranges constitute the “band of uncertainty” and reflect the degree of conviction subjectively placed on the forecast. We can then use this band, or range, as a key input to sizing investment exposures in diversified portfolios that provide more resilience to adverse scenarios than portfolios built from point estimates of return alone. Also, we can find solutions to reducing estimation error and reduce (albeit not completely) uncertainty.
The benefits of diversification
While uncertainty can never fully be eliminated, a diversified portfolio of investments goes a long way in reducing the overall investment uncertainty. Consider the following simplified example. Assume two assets, each expected to return 3%, but within a range of -1% to 7%. Assume these assets are independent of one another (i.e., the correlation of their returns is 0). If we create a portfolio split equally between these two investments, we can show that the expected return narrows from -1% to 7%, to 0.2% to 5.8% (the average of 3% remains unchanged). This is 70% the span of the original range of probable outcomes, and this shrinkage in likely scenarios can be valuable for investors who appreciate greater certainty for financial planning. The following figure illustrates this thought experiment showing the range of probable returns for various weights across these two assets.
Exhibit 1: Illustrative range of expected returns for varying sets of two independent assets
Source: Bank of Singapore.
This is overly simplified, in that most assets with similar characteristics will exhibit some non-zero (positive) correlation with one another. But the conclusion would still hold: assembling at least two or more assets which have similar expected returns and bands of uncertainty would produce a portfolio equal in expected return but with a higher level of conviction around that estimate.
In this piece, we focus on exploring the uncertainty inherent in the formation of return expectations. This is the single greatest source of uncertainty investors face, and arises from multiple factors, namely economic, market sentiment, policy and regulatory, and geopolitical. How can we use historical performance or available data to improve the forecast by boosting conviction and narrowing the uncertainty band? To help illustrate where uncertainty can enter return estimates, we look at two asset classes residing at opposite ends of the returns continuum: the S&P 500 Index, a mainstay of most diversified portfolios, and US Treasuries (UST), its standard hedge.
Uncertainty vs volatility
At this point, it’s helpful to distinguish between “return volatility” and “return uncertainty”: two related but different concepts.
Volatility, a more well-known concept in investing, refers to the statistical measure of the dispersion or variability of returns for a given asset or portfolio over a specific period. It quantifies how much the returns fluctuate from their average (mean) return. Higher volatility means the asset's returns swing more widely, both positively and negatively. Volatility is often expressed as the standard deviation of returns and is used as a proxy for risk.
Uncertainty, on the other hand, is broader. It refers to the lack of confidence in predicting future returns due to various known and unknown factors. As noted earlier, these factors may include economic conditions, geopolitical events, market sentiment, or unexpected policy changes, along with uncountable other drivers. While volatility captures the historical or expected variability in returns, uncertainty encompasses all the risks and unknowns that make future returns hard to predict.
Exhibit 2: Stylized chart of a cumulative returns index: volatility vs uncertainty
Source: Bank of Singapore.
Statistically, volatility measures the variability along a single path of returns, while uncertainty measures the variability of expected returns across different potential paths. These concepts are positively related (asset classes which have experienced high (low) volatility usually are those with high (low) uncertainty), which allows us to use the historic volatility of an asset class as an initial estimate for the range of potential outcomes. But because uncertainty pertains to the unknown future events or conditions that could affect returns, it could be high even if volatility has historically been low.
In terms of predictability, volatility can be observed and measured using historical data, providing some degree of expectation for future variability. The uncertainty band around returns, however, includes unforeseeable risks or new market dynamics that may not be reflected in the historical return volatility. Volatility can be computed using data; uncertainty also leverages data but involves a greater degree of qualitative assessment.
Use historical data thoughtfully in building return expectations
So how do we use historical data as a guide for expected performance? With a healthy understanding of its limits. There are ways to improve on the accuracy of forecasts. In the case of equities, we can explore other factors that correspond with returns, beyond the well-established deterministic relationship between actual P/E levels and profitability and their respective forward 1Y returns. By expanding the forecast horizon over a longer time frame to take advantage of mean reversion in valuations and profitability ratios, we can reduce the error around forecasts by removing the conditionality of correctly calling the business cycle. Moreover, these components exhibit a low correlation with one another, which suggests they behave independently, hence interaction across the building blocks is more limited and does not need to be modelled explicitly – another level of complexity that can be avoided.
A forecast for expected returns for the index depends on individual estimates around P/E changes, profit margin evolution, sales growth, dividends, and share buybacks or dilution. The degree of uncertainty around each adds to the overall uncertainty of the S&P 500 Index return forecast. Given this band of uncertainty around returns, approaching investing in a concentrated manner introduces an outsized exposure to miscalculations.
In the case of bonds, however, a forecast-free approach using starting yields as an anchor for expected returns, especially over the longer term, is a reasonable approximation and can meaningfully reduce the uncertainty around the expected return.
As we have seen in earlier segments, the range of historical outcomes is greater for the S&P 500 Index than for USTs, and this is reflected in the relative bands of uncertainty we should anticipate for the two asset classes. The following exhibit reproduces the ranges for equities and bonds.
Exhibit 3: Distribution of S&P 500 Index, UST Index return and their respective components (annual data, 1992-2023)
Source: Bloomberg, Bank of Singapore.
Putting equities and fixed income together: Diversification reduces uncertainty
As stated at the outset: When unsure of the correct answer, the most effective strategy is to build a portfolio - a diverse set of exposures that provides both resilience and the potential for solid gains. By holding a mix of assets with different risk profiles and return drivers, investors can reduce the impact of any one investment’s range of performance, thereby smoothing overall portfolio performance.
Investors who dislike uncertainty typically respond by adopting more conservative approaches to asset allocation in several ways:
The degree of investor aversion to uncertainty influences asset allocation; the more averse, the more conservative an approach to generate the intended narrower, and more certain, set of outcomes. The tradeoff is between greater certainty and higher potential returns: investors’ tolerance for uncertainty will determine where to comfortably reside on this spectrum.
Summary points
The degree of investor aversion to uncertainty will be key to asset allocation: The tradeoff is between greater certainty and higher potential returns.
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