Constructing Time-Series Momentum Portfolios with Deep Multi-Task
  Learning
Joel Ong Dorien Herremans
A diversified risk-adjusted time-series momentum (TSMOM) portfolio can
deliver substantial abnormal returns and offer some degree of tail risk
protection during extreme market events. The performance of existing TSMOM
strategies, however, relies not only on the quality of the momentum signal but
also on the efficacy of the volatility estimator. Yet many of the existing
studies have always considered these two factors to be independent. Inspired by
recent progress in Multi-Task Learning (MTL), we present a new approach using
MTL in a deep neural network architecture that jointly learns portfolio
construction and various auxiliary tasks related to volatility, such as
forecasting realized volatility as measured by different volatility estimators.
Through backtesting from January 2000 to December 2020 on a diversified
portfolio of continuous futures contracts, we demonstrate that even after
accounting for transaction costs of up to 3 basis points, our approach
outperforms existing TSMOM strategies. Moreover, experiments confirm that
adding auxiliary tasks indeed boosts the portfolio's performance. These
findings demonstrate that MTL can be a powerful tool in finance.