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.

研究表明，在一个深度神经网络结构中使用多任务学习可以提高投资组合的绩效，一些辅助任务如波动率预测也可以改善投资组合的表现。此外，通过对股市交易数据的回测，发现该方法优于现有的基于时间序列动量的投资策略。