This research focuses on utilizing natural language processing techniques to
predict stock price fluctuations, with a specific interest in early detection
of economic, political, social, and technological changes that can be leveraged
for capturing market opportunities. The proposed approach includes the
identification of salient facts and events from news articles, then use these
facts to form tuples with entities which can be used to get summaries of market
changes for particular entity and then finally combining all the summaries to
form a final abstract summary of the whole article. The research aims to
establish relationships between companies and entities through the analysis of
Wikipedia data and articles from the Economist. Large Language Model GPT 3.5 is
used for getting the summaries and also forming the final summary. The ultimate
goal of this research is to develop a comprehensive system that can provide
financial analysts and investors with more informed decision-making tools by
enabling early detection of market trends and events.

该研究利用自然语言处理技术预测股票价格波动，旨在早期发现能够捕捉市场机会的经济、政治、社会和技术变化。通过从新闻文章中识别重要事实和事件，并使用这些事实与实体形成元组，以获取特定实体的市场变化摘要，最后结合所有摘要形成整篇文章的最终摘要。使用大型语言模型 GPT 3.5 进行摘要提取，并分析维基百科数据和《经济学家》的文章以建立公司和实体之间的关系。该研究旨在开发一种全面的系统，通过提前发现市场趋势和事件，为金融分析师和投资者提供更加明智的决策工具。