  This paper introduces DeepUnifiedMom, a deep learning framework that enhances
portfolio management through a multi-task learning approach and a multi-gate
mixture of experts. The essence of DeepUnifiedMom lies in its ability to create
unified momentum portfolios that incorporate the dynamics of time series
momentum across a spectrum of time frames, a feature often missing in
traditional momentum strategies. Our comprehensive backtesting, encompassing
diverse asset classes such as equity indexes, fixed income, foreign exchange,
and commodities, demonstrates that DeepUnifiedMom consistently outperforms
benchmark models, even after factoring in transaction costs. This superior
performance underscores DeepUnifiedMom's capability to capture the full
spectrum of momentum opportunities within financial markets. The findings
highlight DeepUnifiedMom as an effective tool for practitioners looking to
exploit the entire range of momentum opportunities. It offers a compelling
solution for improving risk-adjusted returns and is a valuable strategy for
navigating the complexities of portfolio management.
