Own-research

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.

This study investigates short-term price reversals—temporary retracements following adverse daily returns—and develops a systematic trading framework to capture this effect across multiple asset classes. Using daily data from six liquid ETFs spanning equities, fixed income, currencies, gold, and commodities over the period 2006–2025, the strategy applies a long-term trend filter based on a 200-day moving average combined with a multi-day pullback trigger. Trades are executed dynamically with volatility-adjusted position sizing and equal-weighted allocation across active signals. Parameter sweeps, sensitivity analyses, and sub-period tests are conducted to evaluate the robustness of the approach, including variations in moving average length, number of consecutive down days, holding periods, and alternative momentum indicators such as short-term RSI. The study also explores the practical integration of AI tools— ChatGPT and Claude—to assist in research, analysis, and visualization, assessing their effectiveness in generating coherent quantitative insights.

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.