基于情感分析和GAN的股票价格预测方法

股票价格具有非平稳性和波动性特点，且投资者容易受自身情感影响，投资决策行为具有非理性特征，因此股票价格难以预测.针对预测股票价格的卷积神经网络情感分析方法存在文本标记分布不平衡问题，本文提出一种基于情感分析和生成对抗网络的股票价格预测方法.首先，建立金融领域情感词典库；然后，使用基于词典的情感分析方法计算金融文本数据的情感极性和投资者每天的总体情感指数；最后，利用生成对抗网络对股市波动进行预测，其中生成器生成股票序列数据，而判别器采用卷积神经网络对生成数据和真实数据进行区分.该方法能动态地更新股票价格预测结果且误差较小.

Stock Price Prediction Method Based on Sentiment Analysis and Generative Adversarial Network

The stock price is nonstationary and volatile, the investors are easily influenced by their own sentiments, and their investment decision is irrational. Thus, the stock price is difficult to predict. Aiming at the problem of an unbalanced distribution of text labels in the sentiment analysis method based on the CNN neural network, this paper proposes a stock price prediction method based on sentiment analysis and a generative adversarial network. First, a sentiment dictionary database is established in the financial field. Then, the dictionary-based sentiment analysis method is used to calculate the sentiment polarity of financial text data and the overall sentiment trend of investors every day, that is, the sentiment index. Finally, the generative adversarial network is used to predict the stock market volatility, where the generator generates stock sequence data, and the discriminator uses a convolutional neural network to distinguish the generated data from the real data. This method can dynamically update the prediction results of stocks and obtain smaller error values.