Recent researches on stock prediction using deep learning methods has been actively studied. This is the task to predict the movement of stock prices in the future based on historical trends. The approach to predicting the movement based solely on the pattern of the historical movement of it on charts, not on fundamental values, is called the Technical Analysis, which can be divided into univariate and multivariate methods in the regression task. According to the latter approach, it is important to select different factors well as inputs to enhance the performance of the model. Moreover, its performance can depend on which loss is used to train the model. However, most studies tend to focus on building the structures of models, not on how to select informative factors as inputs to train them. In this paper, we propose a method that can get better performance in terms of returns when selecting informative factors using the cointegration test and learning the model using quantile loss. We compare the two RNN variants with quantile loss with only five factors obtained through the cointegration test among the entire 15 stock index factors collected in the experiment. The Cumulative return and Sharpe ratio were used to evaluate the performance of trained models. Our experimental results show that our proposed method outperforms the other conventional approaches.
翻译:利用深层学习方法对最近关于股票预测的研究进行了积极研究。这是根据历史趋势预测未来股票价格变化的任务。预测未来股票价格变化的方法仅根据图表上的历史变化模式而不是基本价值预测其变化的方法称为“技术分析”,在回归任务中可以分为单一和多变的方法。根据后一种方法,选择不同的因素和投入对于提高模型的性能很重要。此外,其性能取决于模型的哪些损失用于培训模型。然而,大多数研究倾向于侧重于建立模型结构,而不是选择信息因素作为培训模型的投入。在本文件中,我们建议一种方法,在利用综合测试选择信息因素时,在利用孔径损失学习模型时,可以提高回报性能。根据后一种方法,我们比较了两个RNN变异和孔径损失,在试验中收集到的整个15种股票指数因素中,只有5个因素。累积回报率和Sharpe 比率被用来评价经过训练的模型的性能。我们提出的方法显示常规方法。我们提出的方法显示了其他方法。