SPIN: Sparse Portfolio Strategy With Irregular News in Fluctuating Markets

IF 8.9 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-24 DOI:10.1109/TKDE.2025.3545115

Mengying Zhu;Mengyuan Yang;Yan Wang;Fei Wu;Qianqiao Liang;Chaochao Chen;Hua Wei;Xiaolin Zheng

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引用次数: 0

Abstract

The sparse portfolio optimization (SPO) problem is increasingly crucial in portfolio management, focusing on selecting a few stocks with the potential for strong market performance. However, sparse portfolio strategies often face significant short-term drawdowns during periods of market volatility. To this end, a news-driven portfolio strategy offers valuable insights to capture sudden market changes. Nevertheless, it encounters two main challenges: how to reasonably map the relationships between news and stocks and how to effectively utilize the irregular timing of news releases. To tackle the SPO problem in fluctuating markets while addressing these challenges, we propose a novel news-driven sparse portfolio strategy, named SPIN. Specifically, SPIN not only leverages industry-specific group structures existing among stocks for a more reasonable news-stock mapping and models news sequential patterns based on our devised novel news-driven forecaster to handle the irregularity of news releases. We rigorously prove that SPIN achieves a sub-linear regret. Extensive experiments on three real-world datasets demonstrate SPIN's superiority over state-of-the-art portfolio strategies in terms of cumulative wealth and short-term drawdowns.

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SPIN：波动市场中带有不规则消息的稀疏投资组合策略

稀疏投资组合优化（SPO）问题在投资组合管理中越来越重要，重点是选择少数具有强劲市场表现潜力的股票。然而，在市场波动期间，稀疏投资组合策略经常面临显著的短期回调。为此，以新闻为导向的投资组合策略提供了捕捉市场突然变化的宝贵见解。然而，它遇到了两个主要的挑战：如何合理地映射新闻与股票之间的关系，以及如何有效地利用新闻发布的不规则时间。为了在应对这些挑战的同时解决波动市场中的SPO问题，我们提出了一种新的新闻驱动的稀疏投资组合策略，称为SPIN。具体来说，SPIN不仅利用股票之间存在的行业特定的组结构来进行更合理的新闻-股票映射，而且基于我们设计的新颖的新闻驱动预测器对新闻序列模式进行建模，以处理新闻发布的不规律性。我们严格地证明了自旋实现了次线性遗憾。在三个真实世界数据集上进行的大量实验表明，就累积财富和短期损失而言，SPIN优于最先进的投资组合策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Knowledge and Data Engineering 工程技术-工程：电子与电气

CiteScore

11.70

自引率

3.40%

发文量

515

审稿时长

6 months

期刊介绍： The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.