Abstract

The general purpose of stock price prediction is to help stock analysts design a strategy to increase stock returns. We present the conceptual framework of an emotion-based stock prediction system (ESPS) focused on considering the multidimensional emotions of individual investors. To implement and evaluate the proposed ESPS, emotion indicators (EIs) are generated using emotion term frequency–inverse emotion document frequency ($\mathit{etf}-\mathit{iedf}$), which modifies term frequency–inverse document frequency ($tf-\mathit{idf}$). Stock price is predicted using a deep neural network (DNN). To compare the performance of the ESPS, sentiment analysis and a naïve method are employed. The prediction accuracy of the experiments using EIs was the highest at 95.24%, 96.67%, 94.44%, and 95.31% for each training period. The accuracy of prediction using EIs was better than the accuracy of prediction using other methods.

摘要

股票价格预测的一般目的是帮助股票分析师设计提高股票收益的策略。我们提出了一个基于情绪的股票预测系统（ESPS）的概念框架，重点考虑个人投资者的多维情绪。为了实施和评估提议的 ESPS，使用情感词频-逆情感文档频率生成情感指标（EI）。$\mathit{ETF}-\mathit{爱德夫}$)，它修改了词频 - 逆文档频率 ($吨F-\mathit{国防军}$）。股票价格是使用深度神经网络 (DNN) 预测的。为了比较 ESPS 的性能，使用了情感分析和一种朴素的方法。使用 EI 的实验预测准确率最高，每个训练周期分别为 95.24%、96.67%、94.44% 和 95.31%。使用EIs的预测精度优于使用其他方法的预测精度。