Asset class picking

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.

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.

One year is again behind us (in this case, it was 2025), and we are all a little older (and hopefully richer and/or wiser). Turn-of-the-year period is usually an excellent time for a short recap. Over the past 12 months, we have kept our pace and published nearly 70 short analyses of academic papers and our own research articles. So let’s summarize 10 of them, which were the most popular (based on the Google Analytics ranking). The top 10 is diverse, as usual; once again, we hope that you may find something you have not read yet …

In this article, we explore whether it makes sense to incorporate leveraged ETFs into static and dynamic long-only asset allocation strategies. Leveraged ETFs promise amplified exposure to the underlying asset, offering the potential for significantly higher returns during favorable market conditions. However, this comes at the cost of much higher volatility, path-dependency, and the well-known issue of volatility decay, which can lead to substantial underperformance over longer periods. Our objective is to examine if — and how — leveraged ETFs can be systematically integrated into portfolio construction so that their benefits can be captured while mitigating their inherent risks.