Stock ranking prediction using a graph aggregation network based on stock price and stock relationship information

Song, Gwan Gyu; Zhao, Tianlong; Wang, Suwei; Wang, Hua; Li, Xuemei

doi:10.1016/j.ins.2023.119236

Cited by 17 publications

(2 citation statements)

References 42 publications

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“…These measures were found to be useful out-of-sample indicators of systemic risk, highlighting the multi factorial nature of systemic risk and the importance of understanding the connections and interactions among financial institutions. Guowei Song et al (2023) [15] introduced the Multi-relational Graph Attention Ranking (MGAR) network, which dynamically captures stock relationships to predict return rankings. While our work emphasizes distance correlation to detect complex relationships in equity markets, the MGAR network offers a distinct approach by focusing on stock ranking prediction through adaptive learning mechanisms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In a market graph, each node represents an asset (typically a stock) and the edges between nodes indicate the correlations between their returns [3]. These networks have been instrumental in analyzing market dynamics and predicting future prices [15]. Various algorithms such as the Minimum Spanning Tree (MST), Planar Maximally Filtered Graph (PMFG) [16] and Correlation Coefficient Threshold Method have been used to construct stock networks [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Distance Correlation Market Graph: The Case of S&P500 Stocks

Ugwu,

Miasnikof,

Lawryshyn

2023

Mathematics

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This study investigates the use of a novel market graph model for equity markets. Our graph model is built on distance correlation instead of the traditional Pearson correlation. We apply it to the study of S&P500 stocks from January 2015 to December 2022. We also compare our market graphs to the traditional market graphs in the literature, those built using Pearson correlation. To further the comparison, we also build graphs using Spearman rank correlation. Our comparisons reveal that non-linear relationships in stock returns are not captured by either Pearson correlation or Spearman rank correlation. We observe that distance correlation is a robust measure for detecting complex relationships in S&P500 stock returns. Networks built on distance correlation networks, are shown to be more responsive to market conditions during turbulent periods such as the COVID crash period.

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Section: Literature Reviewmentioning

confidence: 99%