The importance of considering related stocks data for the prediction of stock price movement has been shown in many studies, however, advanced graphical techniques for modeling, embedding and analyzing the behavior of interrelated stocks have not been widely exploited for the prediction of stocks price movements yet. The main challenges in this domain are to find a way for modeling the existing relations among an arbitrary set of stocks and to exploit such a model for improving the prediction performance for those stocks. The most of existing methods in this domain rely on basic graph-analysis techniques, with limited prediction power, and suffer from a lack of generality and flexibility. In this paper, we introduce a novel framework, called GCNET that models the relations among an arbitrary set of stocks as a graph structure called influence network and uses a set of history-based prediction models to infer plausible initial labels for a subset of the stock nodes in the graph. Finally, GCNET uses the Graph Convolutional Network algorithm to analyze this partially labeled graph and predicts the next price direction of movement for each stock in the graph. GCNET is a general prediction framework that can be applied for the prediction of the price fluctuations of interacting stocks based on their historical data. Our experiments and evaluations on a set of stocks from the NASDAQ index demonstrate that GCNET significantly improves the performance of SOTA in terms of accuracy and MCC measures.
翻译:许多研究表明,考虑相关库存数据对于预测股票价格变动十分重要,然而,用于模拟、嵌入和分析相互关联库存行为的先进图形技术尚未被广泛用于预测股票价格变动。这一领域的主要挑战是找到一种方法,模拟一套任意的库存现有关系,并利用这种模型改进这些库存的预测性能。这一领域的现有方法大多依赖基本的图表分析技术,预测力有限,缺乏一般性和灵活性。在本文件中,我们引入了一个新框架,称为GCNET,将一组任意的库存之间的关系建为图表结构,称为影响网络,并使用一套基于历史的预测模型,为图中一组库存节点的分类提供可信的初始标签。最后,GCNET使用图表算法分析这一部分标注的图表,预测每个库存流动的下一个价格方向。GCNET是一个一般的预测框架,可用于预测一组任意的库存之间的关系,以图示影响网络,并使用一套基于历史数据的基于历史数据的预测模型,利用一套历史预测的预测模型,用以预测战略服务局数据库的运行情况,并用其基准显示战略服务局数据库中的一项业绩评估。