QLFNX vs. QHFNX
QLFNX (AQR LSE Fusion Fund Class N) and QHFNX (AQR MS Fusion HV Fund Fund Class N) are both mutual funds - QLFNX is a Long-Short fund actively managed by AQR, while QHFNX is a Multistrategy fund actively managed by AQR. Both are actively managed. Their correlation of 0.90 suggests significant overlap in exposure. QLFNX charges 6.55%/yr vs 6.94%/yr for QHFNX.
Performance
QLFNX vs. QHFNX - Performance Comparison
Loading charts...
Returns By Period
In the year-to-date period, QLFNX achieves a -4.08% return, which is significantly lower than QHFNX's -1.02% return.
QLFNX
- 1D
- -2.78%
- 1M
- -1.03%
- YTD
- -4.08%
- 6M
- -5.26%
- 1Y
- —
- 3Y*
- —
- 5Y*
- —
- 10Y*
- —
QHFNX
- 1D
- -2.66%
- 1M
- 0.09%
- YTD
- -1.02%
- 6M
- -1.76%
- 1Y
- —
- 3Y*
- —
- 5Y*
- —
- 10Y*
- —
QLFNX vs. QHFNX - Yearly Performance Comparison
| 2026 (YTD) | 2025 | |
|---|---|---|
QLFNX AQR LSE Fusion Fund Class N | -4.08% | 6.71% |
QHFNX AQR MS Fusion HV Fund Fund Class N | -1.02% | 4.97% |
Correlation
The correlation between QLFNX and QHFNX is 0.90, indicating a strong positive relationship between their price movements. Combining them offers limited diversification - they tend to fall together during downturns.
| Correlation | |
|---|---|
Correlation (All Time) Calculated using the full available price history since Nov 6, 2025 | 0.90 |
Compare stocks, funds, or ETFs
Search for stocks, ETFs, and funds for a quick comparison or use the comparison tool for more options.
Return for Risk
QLFNX vs. QHFNX - Risk-Adjusted Trends Comparison
This table presents a comparison of risk-adjusted performance metrics for AQR LSE Fusion Fund Class N (QLFNX) and AQR MS Fusion HV Fund Fund Class N (QHFNX). Risk-adjusted metrics are performance indicators that assess an investment's returns in relation to its risk, enabling a more accurate comparison of different investment options.
Values are calculated on a 1-year rolling basis and updated daily. Risk-adjusted metrics are more stable over longer periods — use the period switch above to explore them.
Risk / return metrics aren't available yet — we need at least 12 months of trading data to calculate them.
Loading charts...
Drawdowns
QLFNX vs. QHFNX - Drawdown Comparison
The maximum QLFNX drawdown since its inception was -14.54%, roughly equal to the maximum QHFNX drawdown of -13.87%. Use the drawdown chart below to compare losses from any high point for QLFNX and QHFNX.
Loading charts...
Drawdown Indicators
| QLFNX | QHFNX | Difference | |
|---|---|---|---|
Max DrawdownLargest peak-to-trough decline | -14.54% | -13.87% | -0.67% |
Current DrawdownCurrent decline from peak | -5.26% | -4.65% | -0.61% |
Average DrawdownAverage peak-to-trough decline | -5.49% | -4.86% | -0.63% |
Volatility
QLFNX vs. QHFNX - Volatility Comparison
Loading charts...
Volatility by Period
| QLFNX | QHFNX | Difference | |
|---|---|---|---|
Volatility (1Y)Calculated over the trailing 1-year period | 16.92% | 17.18% | -0.26% |
Volatility (5Y)Calculated over the trailing 5-year period, annualized | 16.92% | 17.18% | -0.26% |
Volatility (10Y)Calculated over the trailing 10-year period, annualized | 16.92% | 17.18% | -0.26% |
QLFNX vs. QHFNX - Expense Ratio Comparison
QLFNX has a 6.55% expense ratio, which is lower than QHFNX's 6.94% expense ratio.
Dividends
QLFNX vs. QHFNX - Dividend Comparison
QLFNX's dividend yield for the trailing twelve months is around 0.14%, while QHFNX has not paid dividends to shareholders.
| Position | TTM | 2025 |
|---|---|---|
QHFNX AQR MS Fusion HV Fund Fund Class N | 0.00% | 0.00% |
QLFNX AQR LSE Fusion Fund Class N | 0.14% | 0.14% |
Frequently Asked Questions
With a correlation of 0.90, QLFNX and QHFNX move almost identically. Holding both adds very little diversification - you're essentially doubling your position in the same market segment. Choosing one is usually more capital-efficient.
Find the right allocation for QLFNX and QHFNX
Add both to a portfolio and optimize allocations for your target — whether that's maximizing returns, minimizing drawdowns, or balancing risk across holdings.
Open Portfolio Optimizer