XLE — XLE | S&P 500 Energy Sector ETF 13F holdings and portfolio analysis

PrêtXLE | S&P 500 Energy Sector ETF · Michael Riley

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Baseline

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Baselinetested
Référence Q&R de stratégie3 Q&R de référence

Q&R pré-générée sur ce fonds. Utilisez-la comme contexte de référence pour votre propre analyse.

baseline13FChat AI
What does directly following the disclosed baseline portfolio expose an investor to?

Directly following the baseline means accepting a top-heavy energy portfolio with weak benchmark-relative quality. strategyViews.baseline.topHoldings shows XOM at 23.72%, CVX at 17.61%, and COP at 7.07%, while strategyViews.baseline.concentration puts top5 at 57.07%, top10 at 75.72%, and top20 at 98.21. strategyViews.baseline.sectorWeights is 100.02% Energy, so there is essentially no sector diversification. The backtest summary reinforces the risk: annualizedReturn 5.49%, alpha -4.63, sharpe 0.33, and maxDrawdown -68.39. An investor is therefore exposed mainly to oil-price, commodity-cycle, and mega-cap energy concentration risk rather than a balanced equity portfolio.

↳Show me how baseline concentration would change if I capped any single name at 15%.
baseline13FChat AI
Which recent baseline periods best explain the risk-return trade-off?

The recent baseline periods show that returns come with unstable, episodic payoffs rather than steady compounding. strategyViews.baseline.periodPerformance highlights strong relative months like 2025-02-28, when optimizedReturn was 2.19% versus benchmarkReturn -6.48% for +8.68% excess, and 2025-01-31, with +1.51% versus -2.79% for +4.29% excess. But that upside was offset by weak stretches such as 2025-03-31, when optimizedReturn was -11.59% versus -0.91% for -10.68% excess, and 2025-09-30, when optimizedReturn was -2.12% versus +2.05% for -4.17% excess. The turnover burden also spiked in some difficult periods, such as 3.44 on 2025-03-31 and 3.64 on 2025-07-31, which helps explain why the long-run Sharpe stayed low at 0.33 despite some good rebound months.

↳Rank the last 12 baseline periods by excess return and turnover so I can see the cleanest wins and worst whipsaws.
baseline13FChat AI
What should a user inspect next before deciding whether the baseline is acceptable?

A user should inspect three things next: concentration, drawdown path, and turnover stress. concentration.top5 is 57.09% and concentration.top10 is 75.74%, so first confirm whether that level of name concentration is tolerable. Second, inspect the drawdown path in strategyViews.baseline.drawdownSeries and navSeries, because the baseline reached -68.39% max drawdown and needed 495 recoveryDays. Third, review strategyViews.baseline.turnoverSeries and periodPerformance: turnover hit 3.64 in 2025-07-31 and 3.44 in 2025-03-31, while the baseline also logged -10.68% excess return in 2025-03-31. If those implementation and downside characteristics are unacceptable, the baseline probably is too cyclical even before considering valuation or fundamentals.

↳Summarize the worst baseline drawdown windows and the turnover levels that occurred around them.
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