Own-research

Holidays put people in motion. In the days surrounding major U.S. holidays, airports become busier as travelers visit their families or take advantage of extended weekends. Financial markets themselves are known to display a holiday-related seasonality. In our previous research on the Pre-Holiday Effect in Commodities, we identified a short-term price drift in crude oil and gasoline before major U.S. holidays. Increased travel and the associated expectation of higher fuel consumption offered one possible explanation. This naturally raises another question: if holiday travel leaves a seasonal footprint in energy markets, can it also be detected in the stocks of the airlines transporting those travelers? To investigate this possibility, we analyze the performance of the U.S. Global Jets ETF (JETS) around major U.S. holidays. We first examine its daily returns from ten trading days before to ten trading days after each holiday and use the resulting return profile to identify the strongest seasonal windows. We then formulate two directional JETS strategies and a JETS–USO strategy.

A single backtest can show that strategy was profitable, but it does not always show whether the strategy was competitive. This is especially true for systematic futures strategies. A trend-following strategy can have a positive Sharpe ratio, a long live-like performance history, and a reasonable drawdown, but those numbers are difficult to interpret without a relevant comparison group.
A broad equity index is often not the right benchmark for this type of strategy. A monthly rebalanced multi-asset futures strategy has a different objective, different risk profile, and different return drivers than a long-only stock index. A more useful question is whether the strategy performs well compared with other systematic trend-following futures strategies.
This case study shows how the Quantpedia API can be used to build a custom peer group benchmark for strategy evaluation. The workflow has two steps. First, Quantpedia strategy metadata is used to screen and define a comparable peer group. Second, the historical equity curves of the selected strategies are downloaded, converted into daily returns, and aggregated into an equal-weighted peer group benchmark.

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.