Strategic Asset Allocation (SAA) is a long-term investment strategy that establishes target allocations across different asset classes, such as equities, bonds, and cash, based on an investor’s risk tolerance, objectives, and time horizon. While SAA offers a disciplined approach to portfolio construction—diversifying risk and aiming for stable, long-term returns—it is not without its challenges.

One of the key limitations of SAA arises from its reliance on historical data to derive asset class weightings. These weightings may not fully account for future market conditions or structural shifts in economic cycles. Additionally, portfolios can benefit from tactical asset allocation (TAA) and active stock selection, where adjustments are made to capitalize on short-term market opportunities or anomalies, thereby enhancing returns beyond the constraints of a purely strategic framework.

Sharpe Ratio

The Sharpe Ratio measures risk-adjusted returns, enabling investors to evaluate how much excess return they are earning relative to the risk they are taking. The formula is:

A higher Sharpe Ratio signifies better risk-adjusted performance, meaning greater returns for each unit of risk. In the context of SAA, the Sharpe Ratio is crucial for identifying the optimal mix of asset classes. The goal of SAA is to maximize the Sharpe Ratio, resulting in the highest possible return for a given level of risk. By optimizing portfolios according to this ratio, investors can ensure they are selecting allocations that offer the best trade-off between risk and return.

For example, an optimized SAA benchmark may achieve a Sharpe Ratio of 0.46, outperforming any single asset class due to the uncorrelated nature of returns between different assets.

Table 1: Sharpe ratios: Strategic Asset Allocation versus underlying asset classes over different timeframes

Sources: Bloomberg, Iress and Northstar Asset Management

Limitations of SAA

However, one significant limitation of SAA is the low probability that the conditions used to derive an optimal allocation will persist over meaningful shorter timeframes. Typically, historical returns and risk metrics for various asset classes are analysed and optimised to achieve the highest possible Sharpe Ratio. The assumption is that these historical trends will continue into the future. But such assumptions can be flawed.

For example, relying on data from 1980 to 2020—a period marked by declining interest rates—would skew a portfolio toward an excessive weighting in bonds. With the Global Bond Index down 23% from its highs over the past four years, this reliance on historical data would have led to significantly poor returns.

The limitations of SAA are further illustrated in the case of a typical medium- to high-risk client portfolio (see table above). An asset blend that historically delivered a 4.8% real return instead produced a disappointing -4% real return from March 1969 to April 1977, driven largely by poor bond performance. In contrast, the same SAA yielded a 16.3% real return over the five-year period from April 2003 to February 2008, benefiting from a strong equity market. These examples demonstrate that long-term risk and return metrics often do not apply over shorter yet meaningful timeframes, such as the five-year horizon generally recommended to clients in balanced funds. As the examples show, using SAA in isolation may not reliably achieve desired outcomes over periods as long as eight years.

Enhancing accuracy with additional tools

While SAA forms a robust foundation for multi-asset portfolios, it requires additional tools to enhance accuracy and improve outcomes for investors. Markets are not always perfectly efficient, and asset classes or individual securities may at times be mispriced relative to their intrinsic value. These mispricings create opportunities for tactical shifts—adjusting portfolio weightings to go overweight or underweight in specific assets based on forward-looking valuations.

At Northstar, we address this by aggregating the valuations of all analysed assets into three distinct buy lists: Global Equity, Local Equity, and Fixed Income. Each list is carefully optimised to a specific risk level, enabling us to add alpha (market beating returns) through precise stock selection. By applying rigorous risk-return metrics to each list, we make tactical adjustments to asset class weightings, improving the portfolio’s ability to outperform its benchmark.

Conclusion

In conclusion, while SAA is a critical component in constructing multi-asset portfolios, the limitations of relying solely on historical data calls for a more dynamic approach. By integrating tactical asset allocation and active stock selection, investors can significantly improve their risk-adjusted returns, ensuring portfolios are not only grounded in a solid strategic foundation but also adaptable to ever-changing market conditions.