Hull Tactical Asset Allocation’s HTUS aims to provide a differentiated experience from traditional S&P 500 index funds by incorporating sophisticated tools and strategies often associated with hedge funds. The exchange-traded fund (ETF) blends artificial intelligence (AI) and machine learning to enhance performance and manage risk.
At the helm of Hull Tactical is Petra Bakosova, who recently shared insights into the fund’s unique approach with Wealth Advisor Managing Editor Scott Martin. Bakosova explains how the Hull Tactical U.S. ETF (NYSE: HTUS) operates and why it might be a compelling option for advisors looking to diversify their clients’ portfolios.
The Foundation of HTUS: Data and Machine Learning
HTUS’s strategy is built on a foundation of extensive data collection and advanced mathematical modeling. Bakosova explains, “We collect and process about 30 different indicators, which we sometimes refer to as ‘micro alphas’.” These indicators span a wide range of market factors, including macro, fundamental, sentiment, and technical or anomaly indicators.
However, Bakosova emphasizes that the real power of their strategy’s design lies not just in the data itself but in how they build their models and perform mathematical modeling. This is where AI and machine learning come into play.
AI and Machine Learning in Finance
Understanding what AI and machine learning mean in the context of financial modeling is crucial for appreciating HTUS’s method. Bakosova provides a clear definition: “AI is an academic discipline that was formally defined in 1956. The field of AI is the field of research in computer science that, broadly speaking, studies the methods and software that enable computers to learn to make intelligent decisions and to take actions to achieve predetermined goals.”
Within the broad field of AI, HTUS specifically focuses on machine learning. Bakosova clarifies, “Machine learning is a study of different methods and different tools that enable a better study of data and enable machines to make informed decisions about data.”
This distinction is important because it sets HTUS apart from funds that might use more rudimentary or trend-following algorithms. Instead, HTUS employs sophisticated machine learning techniques to extract meaningful insights from Hull Tactical’s array of indicators.
The Power of Machine Learning in Market Analysis
Bakosova illustrates the power of machine learning with a simple analogy: imagine trying to predict traffic patterns by counting cars passing by your window. A simple linear model might conclude that fewer cars mean less traffic. However, a more sophisticated model—akin to what HTUS uses—would recognize that zero cars might actually indicate severe weather conditions causing gridlock.
This ability to recognize complex patterns and interactions is what sets HTUS’s construction apart. Bakosova states, “It’s not just about finding some nonlinear relationships in the data and being able to fit different types of curves, but also being able to discover interactions.”
This capability is particularly valuable in today’s market environment, where the interplay among economic factors can be complex and counterintuitive. As Bakosova notes, “Depending on what else is going on, having a good inflation number may be a bullish signal for us or may be a bearish signal.”
Transparency and Academic Rigor
One of the distinguishing features of HTUS’s structure is its commitment to transparency and academic rigor. “We are very transparent about this process,” Bakosova emphasizes. “We have published a lot of our findings in peer-reviewed articles. We do seek feedback from academia, we do seek feedback from practitioners, and we also want people to understand what the models can do and also what they can’t do.”
This commitment to transparency extends to the firm’s research output. To date, Hull Tactical has published five academic papers covering various aspects of its models, including their longer-term models, option strategies, and seasonal effects. Bakosova also reveals that they are currently working on a sixth paper focusing on their short-term models.
This level of openness not both provides advisors with a deeper understanding of HTUS’s methodology and demonstrates the firm’s commitment to continuous improvement and validation of their approach.
The Four Pillars of HTUS’s Strategy
HTUS’s strategy is built on four main types of indicators:
- Macro Indicators: Traditional economic measures such as inflation and unemployment rates;
- Fundamental Indicators: Company-specific or sector-specific financial metrics;
- Sentiment Indicators: Market mood and investor psychology; and
- Technical or Anomaly Indicators: Factors such as momentum and volume.
By combining these diverse indicators, HTUS aims to capture a comprehensive picture of market conditions.
Adapting to Market Conditions
One of the key advantages of HTUS’s machine learning feature is its ability to adapt to changing market conditions. In contrast to static models that might become outdated as market dynamics shift, HTUS’s models are designed to learn and evolve over time.
Bakosova notes, “We’re feeding in more and more data, and we’re hoping it learns more as we go.” This adaptive methodology allows HTUS to potentially navigate complex market environments more effectively than traditional buy-and-hold strategies.
The Role of Contrarian Indicators
Interestingly, many of the technical indicators HTUS deploys have a contrarian nature. “You’re looking for when the market’s overbought,” Bakosova says. “You’re looking for when the investors sentiment gets too bullish, and you tend to counter some of these extreme values.”
She also notes an important exception: “One of the well-known technical indicators, momentum, is known to continue anywhere from one month to a 12-month horizon. So, you wouldn’t be betting against momentum.”
This nuanced approach to technical indicators demonstrates the sophistication of HTUS’s strategy. Rather than following or countering trends, the fund’s models aim to discern when each approach is most appropriate.
Balancing Multiple Signals
One of the challenges in using multiple indicators is determining how to weight them, especially when they provide conflicting signals. Bakosova addresses this challenge: “You might have momentum, and you might have sentiment, what do you do when they disagree? We’re hoping to find a relationship and a balance. We’re hoping to mix the ingredients in a way that creates a balanced product.”
This balancing act is where the power of machine learning truly shines. By analyzing historical relationships and current market conditions, HTUS’s models aim to optimize the weighting of different signals to produce the most effective overall strategy.
Practical Implications for Advisors
For wealth advisors, HTUS represents a potential “one-stop shop” for incorporating sophisticated trading strategies into client portfolios. As Bakosova puts it, “We think instead of everybody reinventing the wheel, we’re hoping to be the wheel that the advisors can use to deploy their vehicles on.”
This approach offers several advantages:
- Access to Hedge Fund–Like Strategies: HTUS provides exposure to strategies typically associated with hedge funds but in a more accessible ETF format.
- Dynamic Allocation: Unlike static buy-and-hold strategies, HTUS’s allocation changes based on market signals, potentially providing better risk management.
- Comprehensive Market Analysis: By incorporating a wide range of indicators, HTUS aims to capture a more complete picture of market conditions than single-factor models.
- Transparency: Despite HTUS’s complexity, Hull Tactical maintains a commitment to transparency, publishing research and explaining its methodology.
- Continuous Improvement: The fund’s use of machine learning means it has the potential to improve and adapt over time as it processes more data.
HTUS in the Future
Funds such as HTUS represent a new frontier in investment strategy. By leveraging AI and machine learning, the ETF aims to provide a sophisticated, adaptive approach to equity investing that goes beyond traditional index funds.
For wealth advisors, HTUS offers a way to potentially enhance client portfolios with hedge fund–like strategies without the high fees and lack of liquidity often associated with hedge funds. However, as with any investment strategy, it’s crucial for advisors to thoroughly understand the fund’s approach and consider how it fits within their clients’ overall investment objectives and risk tolerance.
As Bakosova emphasizes, transparency is key. Advisors interested in HTUS or similar AI-driven funds should take advantage of available resources, including published research and direct communication with fund managers, to gain a deep understanding of the strategy.
In an increasingly complex and fast-paced market environment, funds such as HTUS that harness the power of AI and machine learning may provide valuable tools for advisors seeking to navigate uncertainty and optimize client outcomes. As always, careful due diligence and ongoing monitoring remain essential components of incorporating any new strategy into client portfolios.
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Additional Resources
- Contact Hull Tactical Asset Allocation
- Hull Tactical US (HTUS) Summary
- HTUS Factsheet
- HTUS Investment Case
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Disclosures
Carefully consider the Fund’s investment objectives, risk factors, charges, and expenses before investing. This and additional information can be found in the Fund’s prospectus, which may be obtained by visiting www.hulltacticalfunds.com or calling toll-free 1-844-484-2484. Read the prospectus carefully before investing.
There is no guarantee that the investment objectives will be achieved. Moreover, past performance is not a guarantee or indicator of future results.
HTAA, LLC serves as the investment advisor. The Fund is distributed by Northern Lights Distributors, LLC (225 Pictoria Drive, Suite 450, Cincinnati, OH 45246), which is not affiliated with HTAA, LLC.
About the Hull Tactical US ETF (HTUS) Investment Strategy
HTUS is an actively managed exchange traded fund (ETF) driven by various proprietary analytical investment models that examine current and historical market data to attempt to predict the performance of the S&P 500® Index (the “S&P 500®”), a widely recognized benchmark of U.S. stock market performance that is composed primarily of large-capitalization U.S. issuers. The models deliver investment signals that the Adviser uses to make investment decisions for the Fund. The investment models used are to anticipate forward market movements and position the Fund to take advantage of these movements. Currently, signals are combined into an ‘ensemble’ array that spans statistical, behavior-sentimental, technical, fundamental, and economic data sources. This combined signal is generated each trading day towards the close of the market and dictates whether the Fund is long/short and the magnitude of position sizing. The Adviser routinely evaluates the performance and impact of each model on the Fund with the goal of realizing a risk/return profile that is superior to that of a buy and hold strategy.
The use of derivative instruments involves risks different from, or possibly greater than, the risks associated with investing directly in securities and other traditional investments. These risks include (i) the risk that the counterparty to a derivative transaction may not fulfill its contractual obligations; (ii) risk of mispricing or improper valuation; and (iii) the risk that changes in the value of the derivative may not correlate perfectly with the underlying asset, rate, or index. Derivative prices are highly volatile and may fluctuate substantially during a short period of time. The use of leverage by the Fund, such as borrowing money to purchase securities or the use of options, will cause the Fund to incur additional expenses and magnify the Fund’s gains or losses. The Fund’s investment in fixed income securities is subject to credit risk (the debtor may default) and prepayment risk (an obligation paid early) which could cause its share price and total return to be reduced. Typically, as interest rates rise the value of bond prices will decline and the fund could lose value.
While the option overlay is intended to improve the Fund’s performance, there is no guarantee that it will do so. Utilizing an option overlay strategy involves the risk that as the buyer of a put or call option, the Fund risks losing the entire premium invested in the option if the Fund does not exercise the option. Also, securities and options traded in over-the-counter markets may trade less frequently and in limited volumes and thus exhibit more volatility and liquidity risk.
The thoughts and opinions expressed in the article are solely those of the author. The discussion of individual companies should not be considered a recommendation of such companies by the Fund’s investment adviser. The discussion is designed to provide a reader with an understanding of how the Fund’s investment adviser manages the Fund’s portfolio.
