🤖 AI Summary
This study addresses the challenge of constructing end-to-end cross-asset futures timing strategies that outperform traditional rule-based approaches. To overcome the limitations of conventional two-stage paradigms—separate prediction followed by portfolio optimization—the authors propose a unified framework that directly maps market states to portfolio weights. They implement this approach using LSTM and Transformer architectures, innovatively training the models with a differentiable Sharpe ratio as the loss function. Empirical evaluation on 16 highly liquid CME futures contracts demonstrates that the proposed Transformer-based strategy significantly outperforms standard benchmarks—including equal-weight, risk parity, and time-series momentum portfolios—in out-of-sample tests. Notably, the strategy achieves superior performance with lower trading frequency and maintains robustness under moderate transaction costs.
📝 Abstract
Timing-based tilts across asset classes can drive much of the risk and return of a diversified cross-asset portfolio. The standard approach forecasts returns and then optimizes weights. We instead study an end-to-end AI-based policy that maps market states directly to portfolio weights, and we then ask when this one-step modeling approach outperforms simple rules-based strategies. We train these policies on the sixteen most liquid CME futures, where an edge is unlikely to be due to illiquidity, using a differentiable Sharpe ratio loss function, and we benchmark them against equal weighting, risk parity, and time-series momentum. The learned policies rank above the rules on the pooled cross-asset portfolio and in several sub-asset classes, but not uniformly. In gross terms, an LSTM and a transformer-based architecture perform comparably out-of-sample, but diverge when we consider transaction costs. The transformer generates the stronger learned policy, trades far less than the LSTM, and matches or exceeds equal weighting through moderate cost.