RSST ETF: Is Returns Stacking Hype Or Alpha?

I came across the Return Stacked U.S. Stocks & Managed Futures ETF (BATS:RSST) while researching managed futures funds. RSST is managed by Return Stacked Portfolio Solutions, a fund manager associated with Corey Hoffstein, an X-financial influencer I have read research articles from.

The RSST ETF provides a convenient way for investors to gain capital-efficient exposure to a U.S. large-cap equity portfolio and a managed-futures portfolio. Unfortunately, RSST’s implementation of its managed futures strategy resembles machine learning-based CTA funds that I have been critical. Furthermore, the RSST ETF recently suffered a large drawdown coinciding with the short-Yen carry trade unwind. This suggests the RSST ETF may not be providing any useful diversification benefits. I recommend investors place RSST on a watchlist and see if ‘returns stacking’ can actually generate some alpha.

Fund Overview

Historically, sophisticated institutional investors have used leverage, i.e. ‘returns stacking’, to add diversifying alternative strategies like managed futures to their core stock and bond allocations to enhance overall portfolio returns (Figure 1).

However, due to the complexity of managing derivative portfolios, many small institutions and retail investors do not have the luxury of employing ‘return stacking’. The U.S. Stocks & Managed Futures ETF (“RSST”) is an innovative fund that combines large-cap equity exposure and a managed future strategy in one convenient investment vehicle (Figure 2). For every $1 invested, RSST is designed to provide $1 of large-cap U.S. equity exposure and $1 of a managed futures strategy.

The RSST ETF has $207 million in AUM and charges a fairly steep 0.98% expense ratio for providing its innovative strategy (Figure 3).

Portfolio Holdings

To enable ‘return stacking’ while achieving 100% U.S. equity exposure, the RSST ETF must implement a portion of its equity exposure using capital-efficient instruments like equity index futures. This allows the remaining capital in the fund to be used as collateral for its managed futures strategy.

Figure 4 shows RSST’s current top-10 holdings, with the iShares Core S&P 500 ETF (IVV) having a 76% weight and S&P 500 EMini Futures holding a 42% weight, for a net long 118% equity exposure weight. 100% of the net equity exposure is to satisfy the 100% U.S. equity exposure strategy, while the excess 18% is part of RSST’s managed futures strategy.

RSST’s managed futures strategy can invest in one or more of 27 liquid futures contracts, selected to capture a robust cross-section of global assets (Figure 5).

Historical Returns

Since the fund’s inception in September 2023, the RSST ETF has performed well, with YTD returns of 18.5% to August 31, 2024 and since inception returns of 20.6% (Figure 6).

The challenge I have is how to properly assess RSST’s historical performance, since it is combines U.S. large-cap equity returns with a managed futures strategy.

For example, if we were to compare RSST against the SPDR S&P 500 ETF Trust (SPY), we can see that RSST has delivered lower returns and higher volatility since its inception compared to SPY (Figure 7).

However, the past year has not been kind for the managed futures strategy, with the SG CTA Index giving back most of its YTD gains in the last few months due to volatility in currency markets (Figure 8).

So perhaps it is too early to judge RSST’s performance given the headwinds for managed futures strategy.

Does RSST’s Managed Futures Strategy Have A Flaw?

Reading through RSST’s marketing literature, I have to point out there are certain aspects of RSST’s managed futures strategy that concern me. Specifically, RSST seeks to implement its managed futures strategy by replicating the Société Générale Trend Index (“SG CTA Index”) using both a top-down and a bottom-up approach (Figure 9).

RSST’s top-down approach uses machine learning techniques to find portfolio weights that replicate the index, while the bottom-up approach seeks to identify strategies that replicate the returns of the manager basket.

RSST’s top-down machine-learning approach is very similar to the approach used by the iMGP DBi Managed Futures Strategy ETF (DBMF), which I have been vocally critical about. As noted by RSST, the downside of the machine-learning approach is that the strategy only uses the “most recent data to estimate the current portfolio and may miss sudden changes in underlying manager positions.”

Sudden manager position changes tend to happen around market inflection points, when CTA fund managers have all crowded into the same trades and a catalyst causes those managers to quickly reverse their positions.

In DBMF’s case, the ‘escalator up / elevator down’ pattern has happened four times since I have been tracking the fund, with the most recent occurrence occurring in late-July/early-August due as CTA funds have piled into the short-Yen carry trade and the sudden reversal in the USDJPY exchange rate caused a mass exodus (Figure 10).

In RSST’s marketing literature, the fund claims to protect investors from these sudden moves because its 70% bottom-up approach can use “more data to create stable estimates” that can capture sudden changes in weights.

Unfortunately, the actual price performance of the RSST ETF shows the fund suffered a similarly large drawdown in late-July/early-August, coinciding with DBMF’s recent blow-up (Figure 11).

The magnitude of RSST’s recent drawdown is concerning, as RSST’s total returns went from up 32% since inception on July 10th to up only 8% since inception on August 5th, or a loss of ~20% in less than a month.

Furthermore, RSST’s YTD rally and drawdown suggest the fund is highly correlated to performance in the equity markets, which partially defeats the purpose of diversification using ‘return stacking’ with alternative strategies like managed futures.

Conclusion

The U.S. Stocks & Managed Futures ETF is an innovative fund that employs a ‘return stacking’ strategy, combining exposure to U.S. large-cap equities with a managed futures strategy tracking the SG CTA Index.

While RSST’s strategy sounds good on paper, so far, the RSST fund’s performance has been lacking due to headwinds facing managed futures funds. In particular, it appears the RSST fund suffered a large drawdown in recent weeks, similar to other machine-learning-based CTA funds like the DBMF which were caught in the short-Yen carry-trade unwind.

Until the RSST fund proves its managed futures overlay actually adds alpha, I recommend investors stay on the sidelines and place RSST on a watchlist.