Sample optimization for volatility

The chart shows the growth of an initial investment of $10,000 in Sample optimization for volatility, comparing it to the performance of the S&P 500 index or another benchmark. All prices have been adjusted for splits and dividends. The portfolio is rebalanced Every 3 months.

Based on dividend-adjusted daily data since Nov 21, 2023, Sample optimization for volatility's average daily return is +0.02%, while the average monthly return is +0.38%. At this rate, an investment would double in approximately 15.2 years.

Historically, 87% of months were positive and 13% were negative. The best month was Dec 2023 with a return of +1.0%, while the worst month was Oct 2024 at -0.3%. The longest winning streak lasted 14 consecutive months, and the longest losing streak was 1 months.

On a daily basis, Sample optimization for volatility closed higher 62% of trading days. The best single day was Dec 13, 2023 with a return of +0.4%, while the worst single day was Apr 10, 2025 at -0.3%.

Sample optimization for volatility has an annualized alpha of 4.35%, beta of 0.03, and R² of 0.13 versus S&P 500 Index. Calculated based on daily prices since November 21, 2023.

• This portfolio captured 13.62% of S&P 500 Index gains and tended to rise during its downturns (downside capture of -6.75%) — a profile typical of hedging or uncorrelated assets.
• Beta of 0.03 may look defensive, but with R² of 0.13 this portfolio is largely uncorrelated with S&P 500 Index — low beta reflects independence, not downside protection. See the Volatility section for a true picture of this portfolio's risk.
• R² of 0.13 means this portfolio moves largely independently of S&P 500 Index — capture ratios reflect limited market correlation rather than active downside protection. Consider using a more representative benchmark.

Sample optimization for volatility has an expense ratio of 0.05%, which is considered low. Below, you can find the expense ratios of the portfolio's funds side by side and easily compare their relative costs.

Sample optimization for volatility ranks 99 for risk / return — in the top 99% of portfolios on our site. This means strong returns relative to risk — exactly what professional investors look for. Well-suited for investors who want to maximize return per unit of risk.

The Sharpe ratio helps investors understand how much return they're getting for the level of risk taken. A higher Sharpe ratio indicates better risk-adjusted performance, meaning more reward for each unit of risk.

Sample optimization for volatility Sharpe ratios as of Apr 11, 2026 (values are recalculated daily):

• 1-Year: 5.37
• All Time: 4.26

These values reflect how efficiently the investment has delivered returns relative to its volatility over different time periods. All figures are annualized and based on daily total returns (including price changes and dividends).

Compared to the broad market, where average Sharpe ratios range from 2.14 to 3.05, this portfolio's current Sharpe ratio is in the top 25%. This signifies superior risk-adjusted performance, meaning the portfolio is delivering strong returns for the level of risk taken compared to most others.

The chart below shows the rolling Sharpe ratio of Sample optimization for volatility compared to the selected benchmark. This view highlights how the investment's risk-adjusted performance has changed over time.

Sample optimization for volatility provided a 3.61% dividend yield over the last twelve months.

The Drawdowns chart displays portfolio losses from any high point along the way. Drawdowns are calculated considering price movements and all distributions paid, if any.

The table below displays the maximum drawdowns of the Sample optimization for volatility. A maximum drawdown is a measure of risk, indicating the largest reduction in portfolio value due to a series of losing trades.

The maximum drawdown for the Sample optimization for volatility was 0.56%, occurring on Apr 10, 2025. Recovery took 10 trading sessions.

The chart below shows the rolling one-month volatility.