A Distillation-based Future-aware Graph Neural Network for Stock Trend Prediction
DishFT-GNN employs multi-channel attention-based fusion in a teacher-student framework to capture historical-future correlations, enhancing stock trend prediction.
What it examines
The paper introduces a novel distillation-based future-aware graph neural network (DishFT-GNN) for predicting stock trends. By employing a teacher-student framework and attention-based multi-channel feature fusion, the approach captures both historical price patterns and future distribution shifts, addressing non-stationary stock data for improved predictive accuracy.
What it concludes
The results show that DishFT-GNN significantly improves stock trend prediction accuracy over baseline models. The findings suggest incorporating future data via distillation can lead to robust financial forecasting. Potential applications include portfolio management, trading strategy design, and risk assessment. Future research may extend this framework to other time series domains.
Evidence objects
Researchers introduce DishFT-GNN, an innovative, future-aware graph neural network that effectively integrates historical and forward-looking data using a teacher-student distillation process, revolutionizing stock trend forecasting by capturing unseen market shifts.
key_findings bullet 1 · key_findings · validation V0
A teacher model trained on historical and future data transfers knowledge to a student, significantly boosting forecast accuracy by $5.41%$ on US indices via innovative attention fusion and Hilbert-Schmidt loss.
key_findings bullet 2 · key_findings · validation V0
Despite robust results and innovative techniques, the studys limited dataset and focus on American stocks constrain broader applicability, urging future research to expand data scope for globally relevant financial forecasting.
key_findings bullet 3 · key_findings · validation V0
Integrating graph neural networks with a teacher--student distillation framework, the paper introduces a future-aware approach that leverages long-term trend information to overcome non-stationary stock prediction challenges. This innovative method extends traditional GNN techniques with original, compelling ideas, providing a novel strategy that enhances predictive learning, adapting to dynamic market conditions.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
Abstract: Stock trend prediction involves forecasting the future price movements by analyzing historical data and various market indicators. With the advancement of machine learning, graph neural networks (GNNs) have been extensively employed in stock prediction due to their powerful capability to capture spatiotemporal dependencies of stocks. However, despite the efforts of various GNN stock predictors to… ▽ More Stock trend prediction involves forecasting the future price movements by analyzing historical data and various market indicators. With the advancement of machine learning, graph neural networks (GNNs) have been extensively employed in stock prediction due to their powerful capability to capture spatiotemporal dependencies of stocks. However, despite the efforts of various GNN stock predictors to enhance predictive performance, the improvements remain limited, as they focus solely on analyzing historical spatiotemporal dependencies, overlooking the correlation between historical and future patterns. In this study, we propose a novel distillation-based future-aware GNN framework (DishFT-GNN) for stock trend prediction. Specifically, DishFT-GNN trains a teacher model and a student model, iteratively. The teacher model learns to capture the correlation between distribution shifts of historical and future data, which is then utilized as intermediate supervision to guide the student model to learn future-aware spatiotemporal embeddings for accurate prediction. Through extensive experiments on two real-world datasets, we verify the state-of-the-art performance of DishFT-GNN. △ Less
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