论文标题
基于股票价格和新闻的新颖的合奏深度学习模型，用于股票预测
A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News
论文作者
论文摘要
近年来，机器学习和深度学习已成为财务数据分析的流行方法，包括财务文本数据，数值数据和图形数据。本文建议使用情感分析从多个文本数据源中提取有用的信息，并采用混合整体深度学习模型来预测未来的库存运动。混合合奏模型包含两个级别。第一级包含两个复发性神经网络（RNN），一个长期术语内存网络（LSTM）和一个门控复发单元网络（GRU），然后是完全连接的神经网络作为第二级模型。 RNN，LSTM和GRU模型可以有效地捕获输入数据中的时间序列事件，并且完全连接的神经网络用于整合几个个体预测结果，以进一步提高预测准确性。这项工作的目的是解释我们的设计理念，并表明合奏深度学习技术可以真正有效地预测未来的股票价格趋势，并可以更好地帮助投资者做出正确的投资决策。
In recent years, machine learning and deep learning have become popular methods for financial data analysis, including financial textual data, numerical data, and graphical data. This paper proposes to use sentiment analysis to extract useful information from multiple textual data sources and a blending ensemble deep learning model to predict future stock movement. The blending ensemble model contains two levels. The first level contains two Recurrent Neural Networks (RNNs), one Long-Short Term Memory network (LSTM) and one Gated Recurrent Units network (GRU), followed by a fully connected neural network as the second level model. The RNNs, LSTM, and GRU models can effectively capture the time-series events in the input data, and the fully connected neural network is used to ensemble several individual prediction results to further improve the prediction accuracy. The purpose of this work is to explain our design philosophy and show that ensemble deep learning technologies can truly predict future stock price trends more effectively and can better assist investors in making the right investment decision than other traditional methods.
