Low correlation portfolio formation with preselection using rich relational data

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Knowledge-Based Systems Pub Date : 2025-03-12 DOI:10.1016/j.knosys.2025.113287
Kui Fu, Jing Wang
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引用次数: 0

Abstract

The integration of return prediction and portfolio optimization has been widely proven effective. Traditional portfolio optimization approaches, however, rely solely on financial time series data, neglecting the inherent correlations among assets. This study introduces a novel low-correlation portfolio construction methodology utilizing rich relational data integrated via meta-paths. The proposed framework enhances return prediction while minimizing portfolio risk. In the first stage, Long Short-Term Memory (LSTM) networks are implemented to capture sequential patterns in the data. A Graph Neural Network (GNN) with a dual attention mechanism is employed in our framework. This network structure effectively summarizes information from relevant assets while selectively updating features. In the second stage, we develop an asset correlation scoring metric derived from the comprehensive relational data. Based on the predicted returns and correlation scores, we introduce two portfolio construction strategies: (1) a low-correlation strategy and (2) a hybrid strategy with high returns and low correlation. We use sample data from the S&P 500 Index between January 2017 and December 2021 to justify our proposed method. Results demonstrate that incorporating rich relational data significantly improves prediction accuracy. Under Markowitz’s framework, the correlation of high-quality assets is negatively related to their optimal weights. The correlation scoring metric is demonstrated to facilitate portfolio optimization. Assets exhibiting low correlations contribute to portfolio variance reduction and enhanced risk-adjusted performance. Our Prediction-based Low Correlation Portfolio (P-LCP) enhances returns at lower levels of risk. The Prediction-based Hybrid Portfolio (P-HP) demonstrates exceptional performance in terms of cumulative returns and Sharpe ratios. This work implements a data-driven portfolio construction method that utilizes historical and relational data, highlighting the effectiveness of combining predictive theory with low-correlation portfolio strategies.
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
期刊最新文献
Editorial Board Low correlation portfolio formation with preselection using rich relational data Attention Guided Filter and Refinement Feature Network for image semantic segmentation VQ-STE: Scene text erasing with mask refinement and vector-quantized texture dictionary Distilling vision-language pre-training models with modality-specific meta-learning
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