Multi-asset scenario building for trend-following trading strategies

This paper presents a new method for improving the performance of trend-following trading strategies. This new approach improves the inherent problem of trend-following strategies, which is their lagging signals. We simulate alternative price paths of financial assets using a modification of a distribution-free, semi-parametric approach that combines a GARCH-type process with historical simulation. These simulated price paths are used to construct and optimize trend-following trading strategies. The study is conducted in a multi-asset environment. Our empirical results demonstrate the superior performance for multiple assets on a large set of performance metrics compared to widely applied trend-following trading strategies. The results are robust to variations in input specifications, such as tested time and lookback period, number of simulated price paths, and price steps per simulation, but also in terms of trading strategy calibration and market positioning (long-only, long–short, short-only).