Hello all,
What have we accomplished in the last month?
– A new Live Strategies reporting section
– Quantpedia Awards 2026 Winners Interview
– 14 new Quantpedia Premium strategies
– 3 new related research papers
– 7 new backtests
– and finally, 8 new posts on our Quantpedia blog
Investors often face a simple but important problem. They receive a fund equity curve, a strategy track record, or a portfolio performance series, but they do not know what is actually inside. The manager may provide only a broad description, while the realized return stream may in practice be driven by a mix of momentum, tactical allocation, defensive overlays, cross-asset rotation, or other systematic effects.
One way to approach this type of problem is to use a specialized Multi-Factor Analysis report available in Quantpedia Pro. However, this case study focuses on the second approach: building a custom workflow through the Quantpedia API and AI-assisted methodology design. Instead of treating Quantpedia only as a static library of strategy ideas, the workflow uses it as an on-demand database of factor-like return streams. The unknown curve becomes the object to explain, while the Quantpedia strategy universe becomes the set of candidate explanatory building blocks.
In this test, the unknown equity curve was treated as a blind case. The “correct” answer was not used during the analysis. The task was therefore not to confirm a known decomposition, but to test whether an API-based workflow can identify which known systematic strategies best explain the behavior of a black-box curve.
In the modern retail attention economy, Bitcoin and the NASDAQ-100 are not merely separate assets; they are competing narratives. Both appeal to the same pool of speculative capital, the same appetite for asymmetric upside, and the same behavioral forces of FOMO, herding, and recency bias. When technology stocks dominate the imagination, capital clusters around QQQ and the artificial intelligence trade. When Bitcoin breaks out, the crowd’s attention pivots toward crypto’s promise of explosive upside.
This paper tests whether that rotation in attention leaves a systematic footprint. Using Donchian breakout signals across QQQ and Bitcoin, with cash as a fallback during periods of consolidation, we examine whether investors can harvest momentum without remaining permanently exposed to either asset’s full drawdown profile. The results suggest that the answer is yes: retail attention does not move randomly. It rotates, it concentrates, and—when measured through price breakouts—it can be systematically exploited.
An autonomous research agent replicated nine published US-equity anomalies on clean, survivorship-free data. The question is not only what it found (out-of-sample decay is the rule, and on a faithful build none survive — the lone apparent survivor turned out to be a construction error the discipline caught) but whether you can trust an agent to find it, and the checks that decide the answer.
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.
Mean-Variance Optimization remains the intellectual cornerstone of modern portfolio theory, yet its real-world deployment via plug-in MVO often delivers unstable, over-leveraged portfolios that collapse out-of-sample. The core insight from VertoxQuant’s analysis is profound: raw plug-in MVO does not merely propagate estimation error—it systematically amplifies it. This error-maximization phenomenon occurs because the optimizer’s inverse-covariance operator assigns extreme weights to directions that appear low-risk, which, in finite samples, are dominated by noise rather than signal. For academics, this reveals a fundamental statistical pathology; for practitioners, it explains why backtests sparkle while live portfolios bleed.
