GraphCNNpred: A stock market indices prediction using a Graph based deep learning system
Not on the map. Why?
Abstract (arXiv)
The application of deep learning techniques for predicting stock market prices is a prominent and widely researched topic in the field of data science. To effectively predict market trends, it is essential to utilize a diversified dataset. In this paper, we give a graph neural network based convolutional neural network (CNN) model, that can be applied on diverse source of data, in the attempt to extract features to predict the trends of indices of S&P 500, NASDAQ, DJI, NYSE, and RUSSEL. The experiments show that the associated models improve the performance of prediction in all indices over the baseline algorithms by about 4% to 15%, in terms of F-measure. A trading simulation is generated from predictions and gained a Sharpe ratio of over 3.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 4 Jul 2024.
Builds on
9 of 24 references.
References · 9
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2017Graph Attention Networkscited by 8,923
- 2018How Powerful are Graph Neural Networks?cited by 4,393
- 2018Temporal Relational Ranking for Stock Predictioncited by 102
- 2021Understanding Pooling in Graph Neural Networkscited by 69
- 2019HATS: A Hierarchical Graph Attention Network for Stock Movement Predictioncited by 37
- 2024Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformerscited by 35
- 2019Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysiscited by 27
- 2019U-CNNpred: A Universal CNN-based Predictor for Stock Marketscited by 6
Built on by
Citing papers per year: 2024: 3.
3 papers.