基于深度神经网络的股票预测系统

Stock Forecasting System Based on Deep Neural Network

摘要: 股票市场受经济市场、政策等因素的影响，其内部变化规律极其复杂。随着中国股票市场的快速发展和投资者规模的扩大，股票市场产生了大量的交易数据，使获取有价值的信息变得更加困难。由于深度神经网络善于处理数据量大，非线性映射关系复杂的预测问题，本文基于深度神经网络设计了一个股票智能预测系统。主要工作内容如下：股票选股模型研究：从股票财务指标和股票变化趋势层面，研究了多因子量化选股的问题，提出一种基于股票趋势识别算法构建选股模型的方法。股票价格预测模型研究：针对股票交易数据量大，非线性关系复杂，难以准确预测股票价格等问题，基于LSTM (长短时记忆神经网络)深度神经网络对股票价格的预测进行研究，从股票选股模型的输出结果中选择可投资的股票。

Abstract: The stock market is affected by various factors such as economic markets, political, which lead to the complex of its internal changes. With the rapid development of China’s stock market and the expansion of investor scale, a large amount of transaction data of the stock market has been generated, from which it’s difficult to obtain valuable information. The deep neural network has certain advantages in dealing with large amount of data and complex nonlinear mapping. Therefore, based on deep network technology, an intelligent stock forecasting system has been designed. The main work is summarized as follows: Stock selection model: The problem of multi-impact factor quantitative stock selection is studied based on the stock financial indicators and stock change trend, and a stock trend identification algorithm is proposed to construct stock selection model. Stock price forecasting model: Because of the large amount of stock trading data and the complex nonlinear relationship, it forecasts stock price based on LSTM (Long Short Term Memory) deep neural network. We select investable stocks from the output of stock selection model.