Multi Asset: Strategic ******************************* This thread covers strategic asset allocation, momentum & target date. Papers included are: 1) Dual-Horizon Strategic Asset Allocation 2) Testing the Dual Momentum Strategy 3) Using a Life Cycle Model to Design Target Date

Dual-Horizon Strategic Asset Allocation (Rudin and Farley, 2022) Several researchers have emphasized the importance of selecting a correct time investment horizon in optimization, primarily in the context of multi-asset portfolios including direct real estate.

Other than real estate applications, it seems there is a lack of multi-asset strategic portfolio construction frameworks that explicitly consider the term structure of risk expectations and, most importantly, offering an actionable remedy.

In “Dual-Horizon Strategic Asset Allocation,” Alexander Rudin and Daniel Farley introduce a novel dual-horizon asset allocation framework that partially fills that gap. The framework balances an investor’s desire for long-term portfolio optimality with the requirement for

short-term risk control. The framework leverages the evidence that for many core asset classes, price patterns can be decomposed into two components: (1) a long-term, persistent component linked to economic fundamentals and (2) a transient, cyclical component linked to

excess volatility or other noise. The authors explain how (1) the proposed framework has universal applicability, particularly for strategic portfolios incorporating illiquid private assets and (2) allows explicit recognition of strong structural linkage between public

and private prices and delivers sensible outcomes, without introducing liquidity-based constraints or penalizing private asset forecasts.

Dual Momentum: Testing the Dual Momentum Strategy and Implications for Lifetime Allocations (Ha & Fabozzi, 2022) In a series of papers starting in 2011, Gary Antonacci proposed an asset allocation strategy that he referred to as a dual momentum strategy.

This asset allocation strategy for allocating between stocks and bonds combines time-series momentum with cross-sectional momentum which is used to switch among different equity indices. There are three reasons why the dual momentum strategy is preferable to a traditional

momentum strategy: (1) turnover is relatively low, (2) a momentum crash can be effectively avoided, and (3) the Sharpe ratio is high. For these reasons, the dual momentum strategy can address two common challenges associated with momentum: high turnover and a big loss

(momentum crash). In “Dual Momentum: Testing the Dual Momentum Strategy and Implications for Lifetime Allocations.” Ha and Fabozzi empirically test whether the dual momentum strategy is effective at the asset class level as an alternative asset allocation framework,

both in historical simulation and Monte Carlo simulation. Our results for the historical simulation show that the dual momentum strategy, as an alternative asset allocation framework, produces a higher return in all formation periods than global asset allocations.

However, statistical tests show that the strategy has weak statistical significance for some formation periods. We also find that the strategy: (1) generates a lower risk-adjusted return in the Monte Carlo simulation than in the historical simulation and, (2) as an overlay

is effective in enhancing the performance of current target date funds and protecting a portfolio from downside risk.

Using a Life Cycle Model to Design a Target Date Glidepath (Lanski et al., 2022) Because individual investors typically lack both the expertise and time to man age a portfolio of investments themselves, many engage financial advisors for this purpose.

In the early 1990s, an alternative solution became available to investors: the introduction of target date funds which provided a one-stop solution for investors saving for retirement, enabling them to direct their savings into a single multi-asset investment vehicle.

With a target date fund, which the Pension Protection Act of 2006 allowed retirement plan sponsors to offer as default investment options for plan participants, the asset allocation is automatically adjusted as investors age with the objective of continuing to allow investors

to meet their changing financial needs and risk profile. In “Using a Life Cycle Model to Design a Target Date Glidepath,” Ilia Lanski, Raj Paramaguru, Wesley Phoa, Yung Wang, and P. Brett Hammond argue that life cycle models should be applied in this context but that few

practitioners apply such models for the actual design and management of target date funds. For a real-world case, they examine the development of a new life cycle model to serve an existing set of target date funds. The authors begin with the key problems that managers of

target date fund sought to solve by building a life cycle model, including: (1) validating the existing glidepath, (2) determining sensitivity to key inputs such as capital markets assumptions, (3) aspects of the investor persona (e.g., labor income and saving patterns,

retirement date, withdrawal patterns, longevity), and (4) scenario testing shocks to labor income and withdrawal. They then assess what the model builders did to solve these research problems. A key finding of the article is that life cycle model development can and should be

informed by questions and challenges arising from target date fund design and management. Specifically, the model was used to determine the appropriate degree of stock–bond allocation flexibility at each stage along the glidepath.

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