Asset allocation

Our study explores a weekly-rebalanced dual-momentum-based Global Tactical Asset Allocation (GTAA) strategy applied to a diversified set of ETFs. The strategy selects assets based on relative momentum and applies an absolute momentum filter to avoid declining investments. Ultimately, a single combined strategy was created by merging two sub-strategies, incorporating both shorter- and longer-term momentum signals. Backtesting over an extended period demonstrates that this approach delivers attractive risk-adjusted returns, achieving attractive Sharpe and Calmar ratios, while maintaining lower drawdowns compared to a simple equally weighted benchmark.

For U.S. equities, fixed income, and commodities, reconstructing long-term historical datasets is relatively straightforward, and we have already explored these challenges in several previous studies, including 100 Years of Multi-Asset Trend Following, Extending Historical Daily Bond Data to 100 Years, and Extending Historical Daily Commodities Data to 100 Years. Moreover, the broader methodology of reconstructing missing market histories shares many similarities with the techniques discussed in How to Replicate Any Portfolio. Emerging markets, however, represent a particularly interesting opportunity for historical reconstruction, as reliable long-term data is often unavailable for much of the 20th century despite the growing importance of these markets in modern portfolio construction and asset allocation. In this article, we present the framework we developed to extend emerging market histories in a consistent and economically meaningful way, enabling more robust long-term quantitative research and modelling.

Most systematic trading strategies are pro cyclical by nature. They perform best when markets trend higher and volatility remains contained. During broad market expansions, equity risk premia, momentum and trend following approaches tend to generate stable positive returns.

This analysis demonstrates how crisis hedge strategies can be identified, evaluated and integrated into a model portfolio using the Quantpedia Pro framework.

Calendar strategies are often viewed as weak when assessed individually. Their annualized returns tend to be low, market exposure is limited, and trading activity is sparse. Compared to trend following or swing strategies, which can remain invested for extended periods, calendar strategies may appear inefficient at first glance. This impression, however, largely stems from evaluating these strategies outside of their intended context. Calendar strategies are not designed to operate as standalone trading systems. Their primary role is within a portfolio, where their structural properties become relevant rather than their individual performance metrics.

Pragmatic Asset Allocation is a systematic, multi-asset investment strategy designed to adapt dynamically to evolving market conditions. Rather than maintaining a static equity exposure, the model actively allocates capital across a diversified set of asset classes—including equities, bonds, commodities, gold, and cash-like instruments—using momentum-based signals and disciplined periodic rebalancing. The strategy’s primary objective is to deliver attractive long-term returns while materially reducing drawdowns during adverse market environments.

It has now been two highly volatile years since we first published our paper on PAA, making this an opportune moment to review the strategy’s performance over the past year.

The crypto-enthusiast’s mantra—“just add Bitcoin and watch the efficient frontier fly”—runs into a hard empirical wall when you extend the sample, tighten the econometrics, and force the asset to compete on identical risk-adjusted footing with equities. Alistair Milne’s new SSRN paper applies a textbook Markowitz mean–variance framework to a two-asset universe (S&P 500 vs. Bitcoin) and finds that the ex-ante optimal long-term weight on BTC is not merely small; it is outright negative. In other words, a rational, variance-averse allocator who believes expected returns equal historical equity premia plus a fair compensation for BTC’s non-diversifiable volatility should be short, not long, the flagship digital token.