Stock Prediction with Company and Executive Knowledge Graphs

Summary

The document describes a stock movement prediction method designed to capture momentum spillovers among listed companies. It builds a Market Knowledge Graph with two entity types—companies and their executives—and combines explicit relationships with implicit links between them.

A Dual Attention Network, called DanSmp, uses the graph to learn spillover signals for predicting stock movements. The document reports empirical comparisons against nine baseline methods and says the proposed approach improves prediction on the constructed datasets. It provides no dataset details, numerical performance measures, or information about trading costs and live deployment, so the reported prediction gains do not establish that the method would produce profitable trades.

Key ideas

• The graph represents both listed companies and their associated executives.
• The market graph combines explicit relationships with implicit relationships.
• A Dual Attention Network learns momentum spillover signals from the graph.
• The method is evaluated against nine baseline approaches on constructed datasets.
• Prediction improvements alone do not show whether a trading strategy would be profitable.

Tags

# Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks


# Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks









Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in the real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose DanSmp, a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed DanSmp is capable of improving stock prediction with the constructed MKG.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.