ETFs

Factor-based portfolio analysis provides a structured framework for understanding the drivers of investment performance, risk, and long-term behavior. This article applies a set of complementary methods to decompose portfolios into their underlying exposures, evaluate their statistical and economic significance, and assess their behavior across different market regimes.

The analysis is conducted using Quantpedia Pro tools, specifically The Multi Factor Analysis, Factor Analysis Models, The 100-year Portfolio Analysis and The ETF Replication. Together, these methods form a unified factor-based framework that connects decomposition, validation, and replication of portfolio returns. This approach allows for a more robust understanding of portfolio structure and highlights the extent to which observed performance can be explained through systematic factor exposures.

Diversification is a key principle in portfolio construction, yet equal-weight portfolios often fail to deliver true risk diversification. This study shows that capital-based allocation can mask strong concentration in a small number of underlying risk factors. We analyze a simple multi-asset portfolio of ten ETFs spanning equities, bonds, commodities, credit, private equity, and Bitcoin. Despite equal weights, risk is highly concentrated in a few volatile assets and amplified by strong cross-asset correlations, particularly within equity and credit markets. Risk parity reduces concentration by balancing risk contributions and improves risk-adjusted performance, though at the cost of lower returns. Further improvement is achieved through clustering-based allocation, which groups similar assets and allocates risk across more independent sources of return. The results demonstrate that effective diversification depends on the structure of risk factors rather than the number of assets or equal capital weights.

Commodities represent a vital but highly volatile asset class, characterized by pronounced cyclicality, lack of yield, and susceptibility to severe macroeconomic drawdowns. While cross-sectional (relative) momentum is a well-documented anomaly, its application in commodities often forces portfolios to hold the “least declining” assets during broad-based bear markets, resulting in unacceptable tail-risk. This study empirically evaluates the efficacy of a Dual Momentum framework—combining relative strength ranking with an absolute time-series trend filter—applied to a diversified suite of commodity sector ETFs (DBA, DBB, DBE, DBP) from 2007 to 2026. We demonstrate that while pure relative momentum exhibits high parameter sensitivity and inconsistent benchmark outperformance, the inclusion of an absolute momentum filter structurally mitigates drawdowns and universally outperforms a static, equally weighted benchmark across all tested parameter combinations. The findings suggest that Dual Momentum provides a robust, parameter-agnostic framework for portfolio managers seeking tactical commodity exposure with superior risk-adjusted return profiles.

Can crypto still help diversify an equity portfolio—or has that edge disappeared? That’s the practical question behind Crypto Contagion. The paper looks at how shocks move between crypto and U.S. equities, and more importantly, how that relationship changed after the launch of crypto ETFs. Instead of relying on simple correlations, the authors use a combination of jump detection (to isolate real stress events) and machine learning techniques to identify actual spillovers. By comparing periods before and after ETFs, they effectively show how the market structure—and with it, the behavior of crypto—has shifted .

The growth of passive investing and ETFs is indisputable. Consequently, this boom also affects financial markets (e.g., market elasticity or by creating predictable buys and sells) and assets that ETFs track. Even though all passive ETFs aim to replicate some benchmark index, there are two distinct approaches to doing so. The first approach is directly replicating the benchmark (by buying underlying assets) either by full direct replication or sampling. The second approach consists of synthetic replication using derivatives – most commonly by total return swaps (or futures). How do replication methods influence tracking error?