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Does All-weather-style mix Beat the S&P 500? A 10-Year Backtest

All-weather-style mix against the S&P 500 from Jul 2016 to Sep 2026: returns, volatility, Sharpe and Sortino ratios, drawdowns, the COVID crash and 2022, year by year.

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Key takeaways

  • All-weather-style mix: +5.1% a year vs +15.2% for the S&P 500 (Jul 2016–Sep 2026, backtest).
  • Sharpe ratio 0.38 vs 0.88; max drawdown −21.6% vs −23.9%.
  • It beat the S&P 500 in 1 of 9 calendar years.
  • It lagged the S&P 500 on both raw and risk-adjusted return over this period.

All-weather-style mix vs the S&P 500: the results

From Jul 2016 to Sep 2026, all-weather-style mix returned +5.1% a year against +15.2% for the S&P 500, with volatility of 9.0% against 15.2%. It lagged the S&P 500 on both raw and risk-adjusted return over this period.

MetricAll-weather-style mixS&P 500
Annual return (CAGR)+5.1%+15.2%
Volatility9.0%15.2%
Sharpe ratio0.380.88
Sortino ratio0.551.39
Max drawdown (month-end)−21.6%−23.9%
COVID crash (Feb–Mar 2020)−1.8%−19.4%
2022−19.3%−18.2%
Calendar years beating the S&P 5001 of 9—
Correlation with the S&P 5000.711.00

Year by year

YearAll-weather-style mixS&P 500Difference
2026 (YTD)+1.5%+12.1%−10.6%
2025+13.1%+17.7%−4.6%
2024+5.6%+24.9%−19.3%
2023+9.9%+26.2%−16.3%
2022−19.3%−18.2%−1.1%
2021+8.3%+28.7%−20.4%
2020+16.8%+18.3%−1.5%
2019+18.9%+31.2%−12.3%
2018−2.5%−4.6%+2.1%
2017+11.4%+21.7%−10.3%

Growth of $10,000

End ofAll-weather-style mix
2016$9,521
2017$10,610
2018$10,350
2019$12,302
2020$14,370
2021$15,558
2022$12,551
2023$13,788
2024$14,560
2025$16,474
Sep 2026$16,720

How the backtest is built

Holdings: SPY 30 / TLT 40 / IEF 15 / GLD 7.5 / DBC 7.5. A mix of asset classes held at fixed or rule-based weights.

Ten years is one market cycle dominated by US large‑cap growth, so results for value, small caps and international‑style tilts can look very different over other periods. Compare every strategy side by side on the strategies page, or read why beating the S&P 500 is so hard.

Backtested (hypothetical) results calculated by Exent from historical ETF prices with dividends reinvested, monthly rebalancing unless stated, and no trading costs, taxes or fees beyond the funds’ own expense ratios. Backtests are not actual trading and have hindsight bias. Past performance does not guarantee future results. Not investment advice.

Measure your own portfolio the same way

  1. Upload a screenshot of your holdings; no brokerage login.
  2. Exent computes your return, volatility, Sharpe, Sortino and drawdowns and compares them with a risk‑matched benchmark, not just the S&P 500.
  3. The efficient frontier shows whether a different mix of what you already own took less risk for the same return.

Exent is our product. Educational content, not investment advice.

Frequently asked questions

In Exent's backtest from Jul 2016 to Sep 2026, all-weather-style mix returned +5.1% a year versus +15.2% for the S&P 500, with a Sharpe ratio of 0.38 versus 0.88. It lagged the S&P 500 on both raw and risk-adjusted return over this period. Results over other periods can differ.

It returned −19.3% in 2022, when the S&P 500 returned −18.2%.

How does your own portfolio compare?

Exent measures your portfolio against a risk-matched benchmark, shows its Sharpe ratio, drawdowns and crash exposure, and where it sits on the efficient frontier.

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Educational content, not investment advice. Examples are illustrative; past performance does not guarantee future results.

DISCLAIMER: Exent AI is an informational research platform and does not provide investment advice, financial advice, or act as an investment adviser, broker-dealer, or fiduciary. All content, analytics, and model outputs are provided for educational and informational purposes only and should not be relied upon as a recommendation to buy, sell, or hold any financial instrument. Investing involves risk, including the possible loss of principal. Exent AI does not execute trades, hold customer funds, or provide brokerage services. Past performance and hypothetical results are not indicative of future results. Third-party data and AI-generated outputs may be incomplete or inaccurate.