As any investment manager would attest, there are times when markets don’t go your way, and you must navigate periods of challenging performance. As I wrote previously, trend-following strategies have recently faced such a period. While it is sometimes difficult to look beyond short-term performance, it does present us with the opportunity to take a step back and focus on building the best possible trend system. This is not a trivial exercise and spans multiple areas from how you formulate trend signals, construct and monetise your portfolio, all the way through to harnessing generative AI to help accelerate trend-following strategy design.
Building a comprehensive suite of AI agents has been an area of particular interest, both as an enabler of research productivity and as an autonomous front-to-back trend signal generation framework, all trained on Man Group’s extensive research base. As we wrote recently, we don’t expect this to act as a replacement for human researchers, although it provides a tangible opportunity to accelerate the quantitative research process.
Markets are another key aspect of building an optimal trend system. Over the years, our pursuit of innovation has led us to new datasets and markets, often outside the boundaries of conventional systematic managers. While we started trading markets systematically in 1987, we have continuously worked to expand beyond traditional futures and foreign exchange into alternative markets. And coincidentally, this year marks the 20th and 10th anniversaries of our flagship alternative and frontier trend-following programmes, respectively. These markets are typically harder to access, but they provide investors with exposure to idiosyncratic, market-specific risk factors, which have the potential to deliver higher absolute returns.
New markets, however, are not always compatible with a trend-following approach, and as systematic investors, this has led us to adapt our way of thinking and expand our capabilities. This has introduced us to markets where a systematic approach is not conventionally applied. Cat bonds are a good example. They have been an exciting area of research as we look to access a more diversified pool of insurance risk, with a focus on gaining exposure to diversifying perils, such as marine, motor, satellite and drought, which are offered by other areas of the insurance-linked security market. Securitised credit is another area which is commonly associated with a discretionary approach, but we have found an edge for quant managers to exploit. Here, the wealth of data and breadth of assets available, with millions of price points, and billions of collateral records, naturally lends itself to a quant approach, which can screen and evaluate over 20,000 bonds every day, while considering multiple dimensions in real time.
The need to innovate and adapt has also driven advancements in how we think about and apply risk management, building on the success of our risk-managed long-only programme. Risk levels can evolve in different ways, for example through changes in volatility, or correlations, or the direction of price moves. A wider range of data, including intraday measures and an array of options data, can allow us to build a more comprehensive and dynamic picture of risk. Additionally, factors (which have traditionally been confined to the equity space) are now becoming more prevalent across macro markets. The ability to measure and forecast these risks more effectively means we are better able both to control risk and to redeploy it. In a fast-paced world, this is a very welcome addition to our toolkit.
Bibliography
Korgaonkar, R. (2025), “Trend Following and Drawdowns: Is This Time Different?”, Man Institute, Available at: Trend Following and Drawdowns: Is This Time Different? | Man Group
Luk, M., Abou Zeid, T., Van Hemert, O. (2025), “A Trend Following Deep Dive: AI, Agents and Trend”, Man Institute, Available at: A Trend Following Deep Dive: AI, Agents and Trend | Man Group
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