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

TL;DR

This paper introduces a deep multi-task learning approach for constructing time-series momentum portfolios that jointly learns portfolio signals and volatility forecasting, resulting in improved performance over traditional methods.

Contribution

The paper presents a novel deep multi-task learning framework that simultaneously optimizes portfolio construction and volatility prediction, enhancing TSMOM strategy effectiveness.

Findings

Outperforms existing TSMOM strategies after transaction costs.

Adding auxiliary tasks improves portfolio performance.

Demonstrates MTL as a powerful tool in financial modeling.

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.