DRAM vs DRMP vs HBMX: Which Is the Better Pick in 2026?

Overview

These three ETFs target the memory semiconductor ecosystem but pursue fundamentally different strategies. DRAM is a passive, growth-focused fund tracking memory chipmakers with no distributions. DRMP layers weekly options income on top of memory-stack holdings, seeking to extract returns through put credit spreads. HBMX is actively managed for capital appreciation, concentrating on memory producers and their supply chain—packaging, testing, and equipment makers—that support AI infrastructure.

How they differ

The core distinction is income strategy versus growth. DRMP generates a 37.31% distribution rate through a systematic put credit spread approach, paying out income weekly; the other two funds make no distributions. DRAM offers the lowest expense ratio at 0.65% with significant scale ($23.4B in assets), while both Tuttle Capital funds charge 0.95% and are vastly smaller ($6.67M and $30.2M respectively). HBMX differs from DRMP in scope—it targets the broader memory ecosystem including advanced packaging and equipment suppliers, whereas DRMP focuses strictly on memory semiconductor companies and related instruments. All three carry options or derivative elements (DRAM implicitly as part of thematic exposure; DRMP explicitly via put spreads; HBMX through active factor tilts), but DRMP's reliance on sold options for its stated income strategy introduces premium collection risk absent in the other two.

Who each is best for

DRAM: Fits investors seeking pure growth exposure to memory semiconductors without income needs, and who prefer low-cost passive implementation with institutional scale.

DRMP: Designed for investors comfortable with weekly income distributions and actively sold put spreads, willing to tolerate options-related volatility and concentration in memory names in exchange for high stated yield.

HBMX: Suits investors wanting active management and capital appreciation focused on the memory supply chain—not just chipmakers but the ecosystem of packaging, testing, and equipment firms enabling AI—with an annual distribution schedule and longer-term horizon.

Key risks to know

• NAV erosion at 37% distribution yield (DRMP): A weekly distribution rate of 37.31% substantially exceeds realistic long-term capital growth in memory semiconductors; distributions are likely to include return of capital or rely on options premium harvesting, both of which will erode net asset value over time.

• Options premium deterioration (DRMP): Put credit spread income depends on elevated implied volatility and wide bid-ask spreads in memory-related derivatives. If volatility collapses or options markets tighten, premium income will fall sharply, forcing either lower distributions or deeper NAV drawdowns to sustain payouts.

• Concentration risk (DRMP and HBMX): Both actively managed funds are non-diversified or tightly concentrated in memory semiconductors and related suppliers. A cyclical downturn in memory chip demand, oversupply, or geopolitical disruption to semiconductor supply chains will hit these funds harder than broad equity alternatives.

• Illiquidity relative to size (DRMP and HBMX): With only $6.67M and $30.2M in AUM respectively, both Tuttle Capital funds offer minimal liquidity; wide bid-ask spreads and the risk of fund closure or forced liquidation are material concerns for investors entering at current prices.

• Passive tracking fidelity (DRAM): The fund's thematic focus on memory semiconductors and AI may diverge from actual memory-stack company fundamentals, especially if the AI buildout slows or memory prices collapse while index providers add unrelated firms to capture the narrative.

Bottom line

DRAM suits buy-and-hold growth investors wanting scale and low costs; DRMP appeals to income-focused traders tolerant of options mechanics and NAV decay; HBMX targets active allocators betting on the broader AI supply chain beyond just chipmakers. The choice hinges on whether you prioritize growth without distributions (DRAM), high income with derivatives exposure (DRMP), or active ecosystem positioning (HBMX). Past performance in semiconductors does not predict future results, and memory cycles have historically been volatile.