Asset allocation

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.

Market concentration has become one of the most discussed structural risks in today’s equity markets. A small group of mega-cap stocks—often the largest five to ten names—now accounts for an unusually large share of major market indices. This has led to widespread concerns that such concentration makes markets more fragile and that elevated index weights at the top may foreshadow weaker future returns. Many investors worry that history is repeating itself and that extreme concentration today implies disappointment tomorrow.

This article develops a price-based macro–financial model of gold that formally links its medium-horizon return dynamics to cross-asset risk-premium configurations. Although gold has traditionally been conceptualized as a non-yielding inflation hedge or safe-haven asset, contemporary empirical evidence reveals a substantially more intricate structure: gold’s forward returns are systematically conditioned by the joint momentum of (i) gold itself and (ii) long-duration U.S. Treasury total-return indices. The alignment of these two signals appears to encode macroeconomic information—specifically the direction of real interest rates, the stance and expected trajectory of Federal Reserve policy, and the prevailing global risk-appetite regime.