Momentum Hedge Funds – Worth Investing? ^Featured

Recently, one of our clients asked us to screen the best momentum hedge funds. At first glance, it seems like a straightforward task: screen funds based on performance, identify those with consistent upward trends, and voilà—problem solved. But in reality, it’s far more complex.

Challenges of Momentum Funds Assessment

1. Defining Momentum. What qualifies as "momentum" isn't always clear. Should we focus on short-term gains, medium-term trends, or long-term performance? Different funds define and execute momentum strategies differently.
2. Momentum Relative to What? How do we measure momentum effectively? Should we compare a fund's performance against a benchmark, a peer group, or a market factor? Momentum funds operate on the principle that assets with recent strong performance will likely continue to outperform. However, identifying the right comparison isn't simple. Hedge funds often span multiple asset classes and markets, each with its own unique benchmarks. A fund excelling in equities might not align with a benchmark for commodities or fixed income, complicating the evaluation. This variability makes it challenging to filter and rank funds accurately, as the "momentum" must be measured against the appropriate reference points.
3. Survivorship Bias. Many funds with poor momentum don’t survive long enough to make it onto screening lists, skewing the data toward top performers. This gives a false sense of consistency and inflates the perceived success of the strategy.
4. Performance vs. Risk. Momentum strategies can deliver impressive returns, but they often come with higher volatility and drawdowns. Screening based on returns alone without evaluating risk-adjusted performance could lead to poor investment decisions.
5. Market Trends. Momentum strategies can perform exceptionally well in certain market conditions but fail in others, such as during periods of high volatility or rapid market reversals. Screening needs to account for how each fund performs across various market cycles.
6. Factor Analysis. Momentum often interacts with other factors like value, growth, or even macroeconomic shifts. Identifying funds solely based on momentum without considering these influences can be misleading.

Our Approach: Screening Momentum Funds

For simplicity, we focus on momentum funds investing in global equities, which simplifies benchmark selection. Widely recognized indices such as the S&P 500, MSCI, or FTSE Global Equity can be used as reference points. Next, we classify momentum funds into two categories based on their strategy: short-term momentum and medium-to-long-term momentum.

For short-term funds, we may use two main risk statistics: the β+ and Upside Market Capture. The β+ measures the regression slope against the positive returns of the benchmark. This way we are trying to select funds with the highest sensitivity to the benchmark during its positive returns. The second benchmark is slightly different: we aim to filter funds with the maximum performance ratio during benchmark’s bullish trends.

For the medium-to-long-term momentum funds, we use our proprietary Macroeconomic Scenario™ screening, which utilizes the Trend Segmentation™ analysis and measures funds’ performance during aggregated market trends – in our case, all bullish trends of the selected global equity index.

Screening Filters and Hedge Fund Pool

For this study, we analyzed all unique funds from three major hedge fund databases—BarclayHedge, EurekaHedge, and Morningstar—totaling 12,366 funds. These funds have been used as a peer group to analyze best performing momentum funds.

Filters applied to all screening models: minimum return history 36 mths. The result of the search: 699 funds.

Filters applied to the Macroeconomic Scenario™ Screening (Group A): minimum monthly return of 1.1% and VaR more than -0.4% during aggregated up trends of S&P500. This search returned 699 funds.

Filters applied to the Upside Market Capture model (Group B): Up Market Capture against S&P500 higher than 1.2. That screening filter returned in 411 funds.

Filters applied to the regression sensitivity model (the Beta model, Group C): the β+ higher than 1.2. Finally, the last screening returned 734 funds.

Selecting Best Momentum Funds

Are momentum screening filters alone sufficient to identify high-performance funds with strong risk-return profiles? Apparently not. Each group created using these filters includes a wide range of funds, some with solid risk-return characteristics and others that fall short. Consider, for example, how a few funds from Group C (the Beta model) performed compared to their peer group. Over 95% of the peer group funds delivered better returns than the selected momentum funds. To identify top-performing momentum funds in each filtered group, we used Risk Shell’s FlexiRank™ metrics, which incorporated the following risk statistics for the last 36mths period:

• Mean RoR
• VaR⁹⁵
• Maximum Drawdown
• Time-Under-Water
• Stress Drawdown Average
• Stress Drawdown Maximum
• Semi-deviation
• Downside Deviation
• Positive Return Ratio

When calculating stress statistics, we took into account 31 historical extreme events since 1998. The top three momentum hedge funds for each group and their Flexi ranks are shown in the following table:

Funds	Group	FlexiRank™	Percentiles
			Mean	VaR95	MaxDD	TUW	Stress~	Stress^	SemiDev	DownDev	Positive %
Amber Hill ES Currency Arbitrage Fund SP - Class C	A	35.879	0.676	0.883	0.989	1.000	0.345	0.437	0.890	0.987	0.970
Elysium Global Arbitrage Fund		35.624	0.418	0.933	0.999	1.000	0.349	0.440	0.986	1.000	1.000
Sona Credit Master Fund Ltd		31.360	0.512	0.888	0.989	1.000	0.347	0.439	0.944	0.984	0.970
Amplify SCI Real Income Retail HF D	B	36.242	0.020	1.000	1.000	0.968	0.318	0.987	0.950	0.856	0.895
Janus Henderson Biotech Innovation Fund, LLC		34.178	0.031	0.892	0.840	0.839	0.343	0.999	0.975	0.916	1.000
MKT Capital LP		33.583	0.047	0.832	0.867	0.968	0.318	0.987	0.950	0.856	0.895
Challenger Trade Finance SP Class BB	C	36.275	0.022	0.998	0.951	1.000	0.398	0.933	0.991	0.964	0.998
Belmont Multi-Strategy Fund Limited B		35.708	0.023	0.983	0.996	0.971	0.398	0.933	0.997	0.964	0.848
Hudson Bay Fund LP		35.546	0.021	0.985	0.996	0.971	0.399	0.933	0.998	0.997	0.810

 

Peer Group Analysis for Top Momentum Funds

Conclusion

1. Momentum screening filters by themselves are not enough to identify top-performing funds with strong risk-return profiles. Additional risk filters and ranking methodologies must be applied for more reliable selection.

2. Among the three momentum fund screening models—Macroeconomic Scenario Screening, Upside Market Capture, and β+  - the Macroeconomic Scenario Screening model delivers better results, consistently identifying funds with higher relative rankings within their peer group.

3. Medium-to-long-term momentum funds appear to deliver stronger performance, supporting the hypothesis that short-term momentum gains are often short-lived.

Advanced Risk Management

To learn more about our advanced risk models including Multi-Statistic Peer Group Analysis and Stress Testing, join our coming special events.