News groups in the stock market.

A significant correlation between financial news with stock market trends has been explored extensively. However, very little research has been conducted for stock prediction models that utilize news categories, weighted according to their relevance with the target stock. In this paper, we show that prediction accuracy can be enhanced by incorporating weighted news categories simultaneously into the prediction model. We suggest utilizing news categories associated with the structural hierarchy of the stock market: that is, news categories for the market, sector, and stock-related news. In this context, Long Short-Term Memory (LSTM) based Weighted and Categorized News Stock prediction model (WCN-LSTM) is proposed. The model incorporates news categories with their learned weights simultaneously. To enhance the effectiveness, sophisticated features are integrated into WCN-LSTM. These include, hybrid input, lexicon-based sentiment analysis, and deep learning to impose sequential learning. Experiments have been performed for the case of the Pakistan Stock Exchange (PSX) using different sentiment dictionaries and time steps. Accuracy and F1-score are used to evaluate the prediction model. We have analyzed the WCN-LSTM results thoroughly and identified that WCN-LSTM performs better than the baseline model. Moreover, the sentiment lexicon HIV4 along with time steps 3 and 7, optimized the prediction accuracy. We have conducted statistical analysis to quantitatively assess our findings. A qualitative comparison of WCN-LSTM with existing prediction models is also presented to highlight its superiority and novelty over its counterparts.金融新闻与股市趋势间的显著相关性已得到广泛研究。然而，针对依据目标股票相关性加权的新闻类别开展股票预测的相关研究却十分匮乏。本文证实，将加权后的新闻类别同步纳入预测模型，可有效提升预测精度。本文建议采用与股市结构层级相关联的新闻类别：即面向整体市场、行业板块及个股的相关新闻。在此基础上，本文提出了一种基于长短期记忆网络（Long Short-Term Memory, LSTM）的加权分类新闻股票预测模型（Weighted and Categorized News Stock prediction model, WCN-LSTM）。该模型同步融合了带学习权重的新闻类别。为进一步提升模型有效性，WCN-LSTM还集成了多项精心设计的特性：包括混合输入结构、基于词典的情感分析以及用于实现序列学习的深度学习方法。本文以巴基斯坦证券交易所（Pakistan Stock Exchange, PSX）为实验场景，采用不同情感词典与时间步长开展了多组对照实验。以预测精度与F1分数作为模型评估指标，本文对WCN-LSTM的实验结果进行了全面分析，证实该模型的表现优于基准模型。此外，搭配HIV4情感词典、采用时间步长3与7时，模型的预测精度达到最优。本文还通过统计分析对研究结论进行了定量验证，并与现有主流预测模型开展了定性对比，充分凸显了WCN-LSTM的优越性与创新性。