2025/05/13 – In-Person and On-Line
The Science and Practice of Trend-following Systems
Seminar by Artur Sepp
The LQG is very grateful to our hosts of this seminar.
The Science and Practice of Trend-following Systems
We derive an exact relationship between the profit-and-loss (P&L) of a trend-following system with an exponentially moving average (EWMA) filter and the autocorrelation function of the stochastic process that generates the dynamics of returns. This generic result appears to be new. Using this formula, we analyze the impact of the lookback span of the EWMA filter on the P\&L of the trend-following system when returns dynamics are driven by autoregressive fractal processes with long memory. We show that the trend-following system is expected to perform well when autocorrelation of returns is positive over longer time scales, even if returns may exhibit mean reversion over shorter time scales. We also show that trend-following systems with longer lookback spans benefit from the square of the drift of the return process. Our result allows for the performance attribution of a trend-following system into autocorrelation, mean-reversion, and long-term drift components.
In the empirical part, we examine the evolution of the autocorrelation observed in the major futures markets and the performance of different trend-following systems applied to the futures markets. We discuss and contrast the so-called American, European, and Momentum-based specifications of trend-following systems. Finally, we demonstrate the defensive profile of trend-following systems, which provides diversification benefits to long-only portfolios.
This presentation is based on the joint work with Vladimir Lucic of Marex Solutions.
The paper may be accessed here.
About the Speaker
Artur Sepp
Head of Quant Global Investment Services
LGT Bank
Artur Sepp is the Head of Quant Global Investment Services at LGT bank in Zurich focusing on quantitative asset allocation and systematic investment strategies. Artur has almost 20 years of experience in financial markets, including heading quant research and portfolio management at a systematic hedge fund and a family office, as well as leading development of front-office quant strategies and derivatives at private (Julius Baer) and investment banks (Merrill Lynch/BofA). Artur has a PhD in Mathematical Statistics from the University of Tartu, an MSc in Industrial Engineering and Management Sciences from Northwestern University, and a BA cum laude in Mathematical Economics from Tallinn University of Technology. His expertise covers quantitative investing and asset allocation, quantitative modelling of derivative securities, machine learning and data science, and blockchain applications within decentralised finance. He is the author and co-author of several research articles on quantitative finance published in key journals. Artur won the Quant of the Year Award from Risk Magazine (2024). He is an active martial arts practitioner in his free time.
