PORT-02 · Portfolio & Allocation

Minimum Variance

Minimum Variance portfolio minimizes total portfolio risk (variance) without requiring return predictions. Only uses the covariance matrix for optimization.

PortfolioOptimizationLow Volatility

At a glance

Best onRisk-averse allocation
Signal frequencyDaily
Typical holding periodMonths
Asset classesIndex, Commodity, FX
Markets tested1
Data through2026-11-10

What the backtest shows

  • Beat buy-and-hold on 0 of 1 markets by annual return over 5 years.
  • Had a smaller maximum drawdown than buy-and-hold on 1 of 1 markets.
  • Best market by Sharpe ratio: MIN-VAR (Sharpe 0.55, CAGR 3.4%).

Equity curve

0.701.171.632.102.57 201020122014201620182020202220242026
Minimum Variance Equal-weight basket · MIN-VAR, 2010-01-04 → 2026-11-10, growth of 1

Score and risk profile

Arena Score: 53.9MIN-VAR, 5 years
Return quality · 30%50
Drawdown control · 25%50
Consistency · 20%50
Robustness · 15%50
Significance · 10%89

Each bar is a percentile against every strategy on the same market and window (100 = best).

How it held up in sell-offs (MIN-VAR)

PeriodStrategyBuy & hold
Q4 2018 sell-off-2.8%-15.9%
COVID crash (Feb–Mar 2020)-6.6%-26.0%
2022 bear market-14.2%-4.4%
Market above its 200-day average (annualized) · 69%+1.9%+6.9%
Market below its 200-day average (annualized) · 31%+5.2%+1.7%

Sell-off dates follow the US stock market. Above/below the 200-day average uses the previous day's position, so there is no look-ahead.

Out-of-sample check (MIN-VAR)

CheckValue
Chosen parameters on their 3-year training windows (average Sharpe)0.41
Same parameters in the following 6 months, when traded (Sharpe)0.39
Default parameters over the same months (Sharpe)0.40
Parameter re-selections / changes28 / 7
Parameter settings with a positive Sharpe (full history)3 of 3
Sharpe across settings: worst / median / best0.45 / 0.46 / 0.46

Out-of-sample periods start on 2013-01-04. Training-window figures are flattered by picking the best of 3 settings; the out-of-sample figure is what the strategy actually earned. A large drop, or a result that depends on one setting, points to overfitting. How walk-forward testing works.

Results by market (5 years)

MarketCAGRBuy & hold CAGRSharpeMax drawdownBuy & hold max DDVolatilityArena Score
MIN-VARMinimum Variance3.4%13.8%0.55-16.5%-16.5%6.6%54

Strategy metrics use daily returns without trading costs. Buy-and-hold figures are for the same market and window. Rows are sorted by Sharpe ratio.

MIN-VAR across time windows

WindowTotal returnCAGRSharpeSortinoMax DDWin daysTrades
1 year+2.0%2.0%0.480.82-5.9%50%12
3 years+22.4%7.0%1.101.80-6.6%53%38
5 years+18.4%3.4%0.550.86-16.5%52%64
Full history+62.3%2.9%0.450.63-18.5%52%206

Rules

Entry

Solve min w'Σw s.t. sum(w)=1, w>=0

Exit

Monthly rebalance

Indicators

Covariance matrix

Inputs

Lookback = 60

Risk

Minimum achievable portfolio variance

Strategy evidence

Only available, validated bundles appear below. Missing evidence is not a passing validation result.

Evidence coverage

1 / 1 declared strategy–market pairs validated

Notes

  • No return forecasts needed
  • Often outperforms due to low volatility anomaly
  • Tends to concentrate in low-vol assets

Related strategies

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Backtests are historical simulations on daily data and do not include commissions or slippage. They do not guarantee future results and are not investment advice. See the methodology for how every strategy is tested.

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