Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning

• URL: http://arxiv.org/abs/2306.13661v1
• Date: Thu, 8 Jun 2023 13:04:44 GMT
• Title: Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning
• Authors: Joel Ong, Dorien Herremans
• Abstract summary: We present a new approach using Multi-Task Learning (MTL) in a deep neural network architecture that jointly learns portfolio construction and various auxiliary tasks related to volatility. We demonstrate that even after accounting for transaction costs of up to 3 basis points, our approach outperforms existing TSMOM strategies.
• Score: 5.88864611435337
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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