In a recent episode of “Behind the Ticker,” Petra Bakosova from Hull Tactical discussed the unique approach behind the Hull Tactical Asset Allocation ETF, HTUS. Bakosova, who has been with Hull Tactical since its inception, has a background in applied mathematics and previously worked at proprietary trading firms. She explained that Hull Tactical’s investment strategy draws inspiration from founder Blair Hull’s background as both a blackjack card counter and a legendary options trader. Hull’s principles—making frequent, proportional bets based on advantage, and prioritizing risk management—form the backbone of HTUS’s tactical approach.
HTUS, launched in 2015, is a tactical asset allocation fund that aims to outperform the S&P 500 without exceeding its volatility. The fund combines data-driven market timing with quantitative models that analyze approximately 40 publicly available indicators. These indicators are organized into categories: macroeconomic factors, fundamentals, technical anomalies, and sentiment. The strategy is fully dynamic, adjusting S&P 500 exposure daily based on signals generated from these models. HTUS typically ranges from 50% to 150% exposure, though it has flexibility from fully invested to flat or even short.
A unique feature of HTUS is its daily rebalancing, a process that allows the fund to remain agile and responsive to market changes. Bakosova detailed that HTUS leverages SPY and E-mini futures for S&P 500 exposure and incorporates SPX options to adjust for market sentiment and volatility. The fund’s sentiment indicators include sources like MarketPsych, Halbert, and Ned Davis, which track social media and news sentiment. Bakosova highlighted the importance of diversification in HTUS’s modeling process, as each indicator provides different market insights over various time horizons.
HTUS is intended as a sophisticated, hedge fund-like strategy within an ETF structure, appealing to advisors seeking an alternative to traditional large-cap core holdings. Bakosova emphasized that Hull Tactical is focused on continual research and model refinement, with plans to incorporate more advanced AI and machine learning techniques.