论文标题
使用注意模块和新闻情绪的动态和上下文依赖股票价格预测
Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment
论文作者
论文摘要
诸如替代数据之类的金融机器可读数据的增长需要新的建模技术，这些技术可以处理非平稳和非参数数据。由于基本因果关系依赖性以及数据的大小和复杂性，我们为财务时间序列数据提出了一种新的建模方法，即$α_{t} $ - rim（经常性独立机制）。该体系结构利用键值关注，以上下文依赖和动态的方式整合自上而下的信息和自下而上的信息。为了以这种动态方式对数据进行建模，$α_{t} $ - RIM使用了指数平滑的复发性神经网络，该网络可以对非平稳时间序列数据进行建模，并结合模块化和独立的经常性结构。我们将方法应用于S \＆P 500 Universe的三个选定股票以及他们的新闻情感评分。结果表明，$α_{T} $ - RIM能够反映股票价格和新闻情绪之间的因果结构以及季节性和趋势。因此，这种建模方法显着提高了概括性能，即看不见的数据的预测，并优于最先进的网络，例如长期短期内存模型。
The growth of machine-readable data in finance, such as alternative data, requires new modeling techniques that can handle non-stationary and non-parametric data. Due to the underlying causal dependence and the size and complexity of the data, we propose a new modeling approach for financial time series data, the $α_{t}$-RIM (recurrent independent mechanism). This architecture makes use of key-value attention to integrate top-down and bottom-up information in a context-dependent and dynamic way. To model the data in such a dynamic manner, the $α_{t}$-RIM utilizes an exponentially smoothed recurrent neural network, which can model non-stationary times series data, combined with a modular and independent recurrent structure. We apply our approach to the closing prices of three selected stocks of the S\&P 500 universe as well as their news sentiment score. The results suggest that the $α_{t}$-RIM is capable of reflecting the causal structure between stock prices and news sentiment, as well as the seasonality and trends. Consequently, this modeling approach markedly improves the generalization performance, that is, the prediction of unseen data, and outperforms state-of-the-art networks such as long short-term memory models.
