Rethinking the glide path design

Target-date funds – often the default investment choice in retirement plans – promise a simple solution: Start with a high allocation to stocks and gradually move to safer assets like bonds and cash as retirement approaches. Behind this simplicity lies a complex challenge: how should the glide path – the scheme for shifting asset allocation – actually be designed? Romain Perchet, Mehdi-Vincent Hacini, Thomas Heckel and Koye Somefun explain.

Despite decades of academic research, most industry practices rely on rules of thumb because academic models typically do not scale well to multi-asset practice portfolios.

In our recently published article: “Practical and robust glide path design for multi-asset target date funds”¹ in The Journal of Retirement we explore a new, practice-oriented framework that aims to bridge the gap between theory and practice.

In this note we discuss some of the key ideas from the article.

Why glide paths are important

Glide paths are critical to the success of target date funds. They determine how much risk investors take at each stage of their working lives, balancing the potential for capital growth with the need for capital preservation as retirement approaches.

Traditional approaches often lack a solid theoretical basis, are very sensitive to assumptions or simply do not work when more than two asset classes are involved.

Regulatory changes and the growing size of pension assets – more than $4 trillion globally by the end of 2024 – make robust, transparent glide path design more important than ever.

The human capital perspective and its limits

Academic models often justify glide paths by taking into account “human capital” – the present value of future earnings – which is typically bond-like.

Early in a career, most wealth is in human capital, so financial investments can be riskier. As retirement approaches and human capital declines, portfolios must become more conservative.

However, these models are complex, sensitive to assumptions and difficult to implement for multi-asset portfolios.

A robust two-step approach

The article proposes a practical two-step framework:

1. Build a robust, efficient border¹: Use robust portfolio optimization² to generate a set of optimal portfolios for one period for different risk levels. This approach, unlike classical mean-variance optimization, is less sensitive to small changes in expected returns and produces more diversified portfolios.
2. Recursively construct the glide path: Starting one year before retirement, select the optimal portfolio from the efficient frontier for each period, working backwards. The goal is to maximize expected prosperity and manage downside risk (using Value-at-Risk³) upon retirement.

To illustrate, start one year before retirement by selecting the portfolio that optimizes ultimate wealth while balancing the impact of downside risk using Value-at-Risk. Then, two years before retirement, choose a portfolio from the efficient frontier that, together with the previously chosen one-year portfolio, produces the best possible final wealth over the past two years, again taking into account Value-at-Risk for downside protection. This process continues by working backward from year to year, determining allocations for each period until all years of early retirement have been addressed.

In the recursive process described above, the glide path depends only on risk appetite – and not on personal circumstances or accumulated wealth – making it easy to publish and communicate.

Scalability and flexibility

The sliding path design addresses the long-term allocation to the multi-asset portfolio by assuming an independent efficient frontier over one period, which greatly simplifies the framework but can be adapted to account for short-term tactical opportunities.

It allows us to split the glide path design process into two independent steps. As a result, the approach works for portfolios with many asset classes, while traditional approaches only look at the allocation between stocks and bonds. In other words, the approach is scalable by design to multiple assets.

Figure 1 shows an example of an efficient frontier with volatility on the x-axis and expected return on the y-axis. Figure 2 shows examples of allocations to the efficient frontier with increasing volatility.

In the article we use the above efficient frontier regardless of the years to retirement (since the underlying risk and return assumption does not depend on the years to retirement).

However, an easy extension, and something we do in practice, is to work with different efficient frontiers depending on the years before retirement, for example due to legal restrictions such as a minimum level of cash two years before retirement.

This will not fundamentally change the problem: that is, it does not increase the computational complexity of the problem (exponentially) and still results in glide paths that are known in advance, which is often a design requirement for practitioners.

The approach is flexible and allows for a range of adjustments that do not fundamentally change the approach. Currently, cash represents the risk-free asset at retirement. This can easily be adapted to allow alternatives to cash, such as annuities as a risk-free asset in retirement.

Furthermore, it is easy and intuitive to generate more defensive glide paths by simply varying the confidence level of the Value-at-Risk. Moreover, it is easy to replace a Value-at-Risk constraint with a constraint on other risk measures, for example the expected shortfall.

Numerical insights

Consistent with economic intuition, the resulting glide path reduces the allocation to risky assets as the investor approaches retirement.

Figure 3 illustrates the eight-asset universe approach, showing how the glide path gradually shifts from stocks to bonds and cash as retirement approaches. The method is robust to changes in expected returns and can be tailored to different risk preferences or regulatory requirements.

Conclusion

This new framework provides a practical and flexible way to design glide paths for multi-asset target date funds.

By focusing on investment decisions and risk appetite – rather than hard-to-measure personal factors – it provides a scalable solution that meets both regulatory and investor needs in a rapidly evolving retirement landscape.

Read our newspaper Practical and robust glide path design for multi-asset target date funds in the Pension Magazine.

[1] The efficient frontier graph helps investors understand risk versus return by plotting portfolios with the highest expected returns for a given level of risk. It emphasizes diversification to optimize returns while minimizing risk, highlighting the trade-off between risk and return.

[2] Portfolio optimization involves selecting the best mix of assets, for example aiming to achieve maximum returns at a chosen risk level. By analyzing the performance of asset combinations under different scenarios, investors can identify the allocation that best suits their investment goals and risk tolerance.

[3] This involves assessing potential losses and the likelihood that they will occur over a period of time. As a result of the analysis, higher than acceptable risks could, for example, lead to the sale of concentrated holdings.