— Back to Blog

Half a Century of Trend Following Experience

Half a Century of Trend Following Experience

This summary is written by Rich based on a conversation in our CTA series between Marty Bergen, President and Owner of DUNN Capital Management, and the podcast hosts, Niels and Alan.

About DUNN Capital Management

Niels and Alan were joined by Marty Bergin, the Owner and President of DUNN Capital Management. The conversation focused on trend-following and managed futures investment strategy, which outperformed all other strategies in 2022. 

Marty shared some background information about DUNN Capital, which has been in business for over 48 years and is a pioneer in the systematic trading industry. The firm was founded by William Dunn, who was instrumental in shaping the regulatory landscape for CTAs and managed futures.

DUNN Capital is a trend-following firm that uses systematic strategies based on algorithms designed by its founder in the early 1970s. Over time, the firm has adapted its systems to incorporate advanced technology, but the core principles remain the same. 

Marty took over the company from William Dunn, and his primary responsibility is to ensure its continued success. 

The firm currently manages around $1.4 billion in assets and has experienced stable and consistent growth, particularly over the last five years. In addition to focusing on research, DUNN Capital also emphasizes education and promoting the advantages of systematic trading and trend following.

Alan and Marty discussed the persistence and success of the trend-following strategy over time. Marty pointed out that momentum strategies have been around since the beginning of investing, and despite claims that trend following is dead, it has proven to be a robust strategy. He believes the key is to stay focused on trend following and not get distracted by other strategies, which can dilute the benefits of trend following.

Some managers argue that combining trend following with other strategies can make the overall investment approach more palatable for investors, but Marty disagrees with this approach. He believes that investors should have a clear choice and that trend following should be presented as a distinct strategy, not mixed with other strategies. DUNN Capital has been researching an "all-weather" strategy but would not present it as a trend-following approach.

Marty believes that DUNN Capital's singular focus on trend following, and its adaptive risk management, sets the firm apart from its competitors. Their adaptive risk profile involves adjusting the risk target depending on the market environment, which has helped DUNN Capital stay competitive even in non-trending environments. Marty emphasized that trend following should be viewed as a core holding in an investment portfolio, providing a hedge against market downturns, and that timing the market is almost impossible, even for experts like himself.

Dynamic Position Sizing is a risk management technique that involves adjusting the position size of an investment based on the current market conditions, volatility, or other factors that could affect the risk and potential return of a particular asset. By doing this, an investor or trading strategy can better manage risk and optimize performance in different market environments. Marty's explanation of DUNN Capital's adaptive risk profile seems to align with this concept, as they adjust their risk targets based on the market conditions favoring or hindering trend-following strategies.

Use of the Sharpe Ratio to Evaluate Portfolios

Niels and Marty discussed the importance of trend following and how to improve it without deviating from the core concept. Marty mentioned that the goal should always be to improve, regardless of the performance measure used, but believes there might be too much focus on the Sharpe ratio. Marty emphasized that DUNN Capital aims to perform well during trending environments and that 2022 was a year of validation for trend followers.

Marty also highlighted a shift in institutional investors' perspective, as they are now recognizing the value of trend following as a part of risk mitigation, particularly after seeing that fixed income allocations did not provide the expected protection. Marty suggested that institutional players will increasingly look to trend following and CTAs as a liquid hedge for their private equity allocations.

Speed of Trend Following Systems

The conversation then moved to the topic of speed of systems and how it can impact performance during equity down markets. Marty acknowledged that shorter timeframes or lookback periods can mitigate drawdowns, but they may not provide the best overall performance. 

The speed of systems refers to the responsiveness of a trading algorithm or strategy to market changes. It essentially dictates how quickly a system can identify and react to new trends or changes in the market. In the context of trend following, the speed of systems can be a significant factor in determining the performance of a trading strategy during different market environments. There are mainly three categories of system speeds: fast, medium, and slow.

  • Fast systems: These are characterized by shorter lookback periods and timeframes. They are designed to identify and react to market trends quickly. Fast systems can potentially generate higher returns during short-term market moves or reversals, as they can enter and exit positions swiftly. However, they may also generate more false signals and higher transaction costs due to their increased trading frequency. These systems may be more suitable for high-frequency or intraday trading strategies.
  • Medium systems: Medium-speed systems use moderate lookback periods and timeframes, striking a balance between fast and slow systems. They are generally more stable than fast systems and can effectively capture medium-term trends in the market. These systems can offer a good balance between responsiveness and stability, with fewer false signals and lower transaction costs than fast systems.
  • Slow systems: Slow systems employ longer lookback periods and timeframes, which make them less responsive to short-term market fluctuations. These systems are designed to capture long-term trends and are less likely to be affected by market noise or short-term reversals. While they may generate fewer trading signals and lower transaction costs, slow systems may be slower to enter or exit positions, which could result in missed opportunities or higher drawdowns during sudden market reversals.

In the context of trend following and equity down markets, the speed of systems may play a crucial role in determining the strategy's effectiveness. Fast systems may react quickly to equity turning points and provide better protection during market downturns. However, they may also generate lower overall returns due to increased false signals and higher transaction costs. On the other hand, slow systems may be more focused on capturing large market moves and generate higher overall returns, but they might be less effective in protecting against short-term equity reversals.

To cater to the needs of different investors, trading firms may consider offering a range of products with varying system speeds. By doing so, they can provide investors with the flexibility to choose a trading strategy that aligns with their risk tolerance, investment objectives, and preferences for drawdown management and return potential.

Research Processes and Enhancements

Alan and Marty discussed the process of research and enhancements in their trading program. Marty emphasized the importance of avoiding data mining and maintaining robust research methods. He noted that most research projects do not yield significant improvements, but occasionally, they stumble upon valuable ideas.

Marty stated that research intensity often increases during difficult periods, but since the implementation of findings takes time, the markets might have already changed. The key is to maintain open communication, be receptive to new ideas, and learn from external sources like research papers.

When asked about DUNN’s original trading model from the 1970s, Marty explained that the essence of trend following remains the same. What has evolved is the implementation of signals, risk management, data analysis, trading, and portfolio development. He also mentioned the importance of market diversification and avoiding markets with high counterparty risk.

Marty described one of their enhancements as an exit strategy aimed at accelerating exits from trends. This is not a stop-loss criteria, but rather a profit-taking enhancement. Although it may not always be beneficial, Marty believes the overall positive impact outweighs the negatives.

Data mining refers to the process of extracting useful information and patterns from large datasets. While data mining can be valuable in many applications, it has some potential pitfalls when used improperly or excessively, especially in the context of financial modeling and trading strategies. The main pitfalls of data mining include:

  • Overfitting: One of the most significant risks of data mining is overfitting, where a model becomes too complex and captures noise rather than the underlying trend. Overfitted models often perform well on historical data but fail to generalize well to new, unseen data. This can lead to poor real-world performance and losses in financial markets.
  • False discoveries: Extensive data mining can lead to the identification of spurious relationships that appear statistically significant but are actually random occurrences. These false discoveries can result in the development of flawed models and trading strategies that underperform in real-world scenarios.
  • Multiple testing bias: Data mining often involves running numerous tests on a dataset, increasing the likelihood of finding statistically significant results by chance alone. This is known as multiple testing bias, and it can lead to false discoveries and overfitting if not properly addressed.
  • Overemphasis on past performance: Data mining can lead to an overemphasis on past performance, assuming that historical patterns will continue in the future. This can be dangerous in financial markets, where past performance is not always indicative of future results.
  • Confirmation bias: Data mining can contribute to confirmation bias, where researchers unintentionally select data or methods that confirm their preexisting beliefs. This can result in biased models and poor decision-making.
  • Complexity and lack of transparency: Excessive data mining can lead to complex models that are difficult to understand and interpret. This lack of transparency can make it challenging to evaluate the effectiveness of a model or trading strategy and may lead to a false sense of confidence in its performance.
  • Computational cost: Data mining can be computationally expensive, especially when dealing with large datasets and complex models. This can result in high costs, both in terms of time and resources, which may not always yield significant benefits.

To avoid these pitfalls, it is crucial to use robust research methodologies, consider out-of-sample testing, apply proper statistical techniques, and remain mindful of the risks associated with data mining when developing models and trading strategies.

Evolution of DUNN’s Approach and Importance of Diversification

Marty discussed the evolution of DUNN's approach to trading and the importance of diversification in their portfolio. Key points include:

  • Shift in approach: In 2006, DUNN shifted from focusing on individual markets to taking a more holistic view of the overall portfolio, which made the system more robust and adaptive to changing market conditions. This approach relies on the systematic nature of their trading strategies to adapt automatically.
  • Number of markets: DUNN is comfortable with the number of markets they trade and does not add markets solely for the sake of expansion. They believe that some peers might have added more markets primarily to accommodate larger assets under management (AUM).
  • Diversification benefits: DUNN focuses on adding markets that offer diversification benefits to their portfolio. Marty mentions that they have recently added some metals to their portfolio to achieve further diversification.
  • Realistic number of markets: Marty doesn't specify an exact number of markets that provide optimal diversification. Instead, he emphasizes that their approach is to add markets based on the potential for diversification benefits and not just to increase their market count.
  • Overemphasis on diversification: Marty argues that some people may place too much weight on diversification, as studies have shown that it doesn't take too many markets to achieve adequate diversification. However, he acknowledges that diversification can play a crucial role during abnormal market conditions, when things can go wrong suddenly.

Views of Replication Methods

The conversation then turned towards CTA replication strategies and the potential risks associated with them. Key points from the discussion include:

  • CTA replication: Niels brought up the topic of CTA replication strategies, which have been a subject of discussion in the trading community. These strategies attempt to replicate the performance of managed futures funds by using simplified or cheaper trend-following models or linear regression based on performance data.
  • Simulated data: Marty expressed concerns about the lack of due diligence on these strategies, noting that the information used to make investment decisions is typically based on simulated data without any real trading or execution history. This lack of real trading history may cause investors to underestimate the risks involved in these replication strategies.
  • Market stress: Marty pointed out that it will be interesting to see how these replication strategies perform during times of market stress, as it might expose potential weaknesses or unexpected risks that investors did not anticipate when they allocated funds to these strategies.

In summary, the discussion highlighted concerns around CTA replication strategies and the potential risks involved due to the reliance on simulated data without a real trading history. It raised questions about the performance of such strategies during times of market stress and the need for thorough due diligence before investing in them.

Position Sizing and Risk Management

Alan and Marty discussed dynamic vs. static position sizing, volatility targeting, and the importance of market correlations. Marty shared that his firm moved towards dynamic risk sizing, adjusting positions on a daily basis based on volatility, correlations, and trend strength. He acknowledged that static position sizing may generate more positive skewness, but it sacrifices control over risk.

Marty emphasized that cross-market correlations play a bigger role in risk management than volatility. Their approach is 100% systematic, allowing data to dictate their decisions rather than relying on personal interpretations or emotions. The lookback periods used for measuring volatility and correlations are also constantly changing, gravitating between shorter and longer periods depending on market conditions.

While Marty's firm focuses on medium to long-term trend following, he acknowledged that there is a place for short-term, high-frequency trading. However, he believes that the costs associated with this type of trading are excessive and prefers not to compete in that space.

Capacity, Fees and Flows

Niels asked Marty about his thoughts on capacity and fees in the trend following space. 

Marty explained that DUNN is a niche player and not a large firm, so capacity is not a concern. He mentioned that in the past, they were approached by institutions offering a product at a very low fee level but they weren't willing to sacrifice their zero-management fee, incentive fee-only structure, as they've always prided themselves as being client-centric. 

He thinks that while there's a place for potentially offering a product that may not be the best available but provides a need for an investor or a client, a different fee structure may be negotiated for that particular investor. 

He believes that if they were to touch $3-4 billion in AUM, they'd readdress where they were. Bill Dunn (the founder of DUNN) used to say that if you ever had a capacity issue, you just raise the fee and that will take care of it, but Marty doesn't plan on doing that route. He acknowledges that there's a lot of competition out there but feels comfortable with DUNN's position in the industry as a whole.

The discussion then shifted to market selection and liquidity. 

Marty explained that they have avoided trading Chinese futures due to concerns about counterparty risk. The conversation then turned to liquidity and exaggerated moves in certain markets, such as the gilt market, cable earlier in the year, and the Treasury market in 2020. 

Marty noted that they are currently avoiding LME due to recent issues with internal corruption and are waiting to see how they respond. They also discussed the volatility profile of markets and how automated systems can get hurt in broken volume profiles, but by having experienced traders supervising the process, it mitigates that risk. They monitor slippage and have positive results in that area.

DUNN’s Location

The discussion shifted to the topic of DUNN's location in Stuart, Florida, which is not a financial center. 

Niels asked Marty if he thinks the location is an advantage or disadvantage for DUNN. Marty respondsed by saying that he sees it as a non-issue, as technology makes it easy to communicate with clients and due diligence can be done remotely. 

Marty also noted that while it may be less convenient for institutions to see and talk to them, people are comfortable when they come to see their offices and understand why they are located where they are. 

Marty added that Stuart was DUNN's headquarters before other banks started opening offices in Florida and that they were just ahead of their time.

The Role of Trend Following for a Larger Portfolio

Alan asked Marty about the role of trend following within a larger portfolio and how it should be funded. 

Marty stated that trend following should be a core allocation, potentially funded equally out of fixed income and equity. He also believes that institutions will look at trend following as a source of alpha during crisis environments, rather than a negative drag on the portfolio.

Alan then asked how investors should think about the potential returns of managed futures and trend following to determine how much to allocate and in what area of the portfolio. 

Marty suggested that investors should analyze their goals and ensure that they have allocated enough to trend following to see benefits. He also noted that private equity is often allocated to because of its smooth performance, but that trend following can act as a hedge during a recessionary period. Finally, 

Marty mentioned that the transparency and mark to market of trend following can be seen as a negative by some investors, but it also means that the value of their investments is always available to them.

Management of Cash for a Trend Following Program

Niels and Alan asked Marty about the cash management aspect of DUNN's managed futures products. 

Marty noted that most CTAs overlook cash management, but it is an important component of their process. They mitigate counterparty risk associated with brokers by minimizing the amount of money they have with a particular broker and rely on a firm they own 49% of the equity of in New York to manage all of their cash. 

They keep a close watch on every confirmation for every purchase or sale that's done in the fixed income sector to ensure transparency and control over the process. 

Marty believes that this approach has worked well for their investors. He also noted that the firm provides this service to other CTAs in the industry.

Misconceptions of Trend Following

Niels and Alan asked Marty if there is something people say about trend following that he disagrees with. 

Marty said that people often claim that trend following is dead, but he disagreed and pointed out that the market has not changed; it may be stretched out, but eventually, it snaps back to what you would expect to see. 

He emphasized the importance of ensuring that the manager is not experiencing style drift, which can take different forms, and making sure that you are getting what you expect from the investment. 

He noted that most managers will not answer questions about style drift unless you are on the inside looking at the changes that have happened in code, and so it is the allocator's job to try and see beyond the narrative and understand what is happening with the investment.

Outlook for 2023 and Beyond

Niels asked Marty about his thoughts on the upcoming year and what he is most excited about. 

Marty said that he has no concerns and believes that they are transitioning from an inflationary environment to a recessionary environment, which are different when trying to invest successfully. 

He also mentioned that there may be some pain related to the transition, which he is comfortable with. He is excited to see the growth and development of DUNN and the trend following industry as a whole. 

Marty thinks that the industry is coming into its own, and he would like to see it become more accessible to all parts of the investment community, including retail clients, with fees that are more accessible to make it profitable for retail clients. 

He is optimistic about the industry becoming more mainstream and being more accepted.

This is based on an episode of Top Traders Unplugged, a bi-weekly podcast with the most interesting and experienced investors, economists, traders and thought leaders in the world. Sign up to our Newsletter or Subscribe on your preferred podcast platform so that you don’t miss out on future episodes.