@article {2024,
	title = {DeepUnifiedMom: Unified Time-series Momentum Portfolio Construction via Multi-Task Learning with Multi-Gate Mixture of Experts},
	journal = {arXiv:2406.08742},
	year = {2024},
	abstract = {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{\textquoteright}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.},
	url = {https://arxiv.org/abs/2406.08742},
	author = {J. Ong and D. Herremans}
}
@mastersthesis {2024,
	title = {Modern Portfolio Construction with Advanced Deep Learning Models},
	volume = {PhD},
	year = {2024},
	abstract = {This thesis explores the modern application of deep learning techniques in portfo- lio construction, presenting innovative methodologies that significantly enhance traditional investment strategies. Central to this research are three advanced frameworks that leverage deep learning to optimize financial portfolios.
The first framework introduces a diversified risk-adjusted TSMOM strategy utilizing multi-task learning. This approach simultaneously optimizes portfolio construction and volatility forecasting, resulting in improved portfolio performance by learn- ing both momentum signals and volatility estimators. Experimental results involving a diversified portfolio of continuous futures contracts demonstrate that this method outperforms existing TSMOM strategies.
The second framework employs a multi-task learning model with a multi-gate mixture of experts to optimize momentum portfolios across multiple timeframes. This model excels over benchmarks across various asset classes, effectively capturing com- plex momentum dynamics in equity indexes, fixed income, foreign exchange, and commodities. Extensive backtesting highlights its capacity to enhance risk-adjusted returns, underscoring its practical utility for portfolio management.
The third framework presents an adaptive sparse Transformer model designed for index tracking. By combining sparse modeling with deep learning, this framework optimizes passive investment strategies. Backtesting spanning from 2005 to 2024 re- veals that it delivers higher excess returns and lower tracking errors compared to ex- isting models, showcasing the effectiveness of this approach in refining index tracking methodologies.
Lastly, we introduce the CurveMMOE model, a deep-learning framework for trad- ing commodity futures curves. This model integrates multi-task learning with a Mixture of Expert architecture, outperforming traditional methods on risk-adjusted re- turns. These frameworks contribute significantly to portfolio construction by harness- ing the power of deep learning techniques. They provide investment practitioners with innovative approaches to improve financial performance, particularly in challeng- ing market environments. This research advances our understanding of deep learning in finance and offers practical strategies for real-world investment scenarios.},
	author = {J. Ong}
}
@article {2013,
	title = {Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning},
	journal = {Expert Systems with Applications},
	volume = {230},
	year = {2023},
	month = {In Press},
	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, such as forecasting realized volatility. 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{\textquoteright}s performance. These findings demonstrate that MTL can be a powerful tool in finance.},
	doi = {10.1016/j.eswa.2023.120587},
	url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4288770},
	author = {J. Ong and D. Herremans}
}
