HERC Optimization

Hierarchical Equal Risk Contribution (HERC) is an advanced portfolio optimization method that groups your assets into natural clusters (like aggressive growth stocks, defensive assets, commodities, bonds), then balances them so each group contributes equally to your portfolio's total risk. HERC ensures that no single group dominates your portfolio's risk, preventing situations where one asset group crashes and drags your entire portfolio down.

HERC is an extension of Hierarchical Risk Parity (HRP), with the key difference being that HRP simply gives less weight to riskier groups, which can lead to overly conservative allocations. HERC instead equalizes risk contribution across all groups, resulting in more balanced diversification.

Mean-Variance Optimization

Maximizes returns for your chosen risk level using historical data.

Risk Parity

Balances risk equally across all assets instead of capital.

HRP Optimization

Groups similar assets and allocates risk within clusters.

HERC Optimization

Balances risk contribution equally across asset groups.

Add to portfolio

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Rebalance

Portfolio currency

What it affects

My Portfolios

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Optimization settings

Risk Measure

How risk is defined when optimizing your portfolio

Risk Free Rate

The minimum return you'd expect without taking any risk

%

Reoptimize Frequency

See how your portfolio would have performed if you run optimization on a regular schedule

Optimization Date

Results after this date show real, out-of-sample performance

Training Window

How much historical data is used to calculate optimal weights

Correlated Assets

Automatically removes assets that move too similarly to improve diversification

Constraints

Set minimum and maximum allocation limits for each asset

Min. Position Weight

%

Max. Position Weight

%

Benchmark

Compare your optimized portfolio against a market index

Benchmark Against

Benchmark

Optimal Asset Allocation

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Key Improvements

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Portfolio Performance

The chart shows the growth of an initial investment of $10,000 in Optimized Portfolio, comparing it to the performance of the S&P 500 index or another benchmark. All prices have been adjusted for splits and dividends.

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Allocation Over Time

This chart presents a detailed view of the portfolio's composition from its inception to the present day.

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Portfolio Sharpe Ratio

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Portfolio Drawdowns

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Portfolio Volatility

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Asset Correlations Table

The table below displays the correlation coefficients between the individual components of the portfolio, the entire portfolio, and the chosen benchmark.

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HERC vs HRP

Both methods use hierarchical clustering, but differ in allocation:

HRP: Weights inversely proportional to each cluster's risk (higher risk = lower weight)
HERC: Adjusts weights so each cluster contributes equally to total portfolio risk
Result: HERC typically produces more balanced risk distribution across distinct asset groups

When to Use HERC

HERC works best when:

Your portfolio has distinct asset groups (tech, bonds, commodities, cash, etc)
You want equal risk contribution at the cluster level, not just individual assets
You're optimizing for tail risk measures (CVaR, CDaR) rather than just volatility
You want to prevent any single group from dominating portfolio risk

Trade-offs

More computationally intensive than HRP (requires optimization at each node)
Performance can be less stable than HRP across different market conditions
Like HRP, doesn't require covariance matrix inversion (more stable than traditional MVO)
Works best with 15+ assets that form natural, distinct clusters