跨语言新闻情感与股市趋势相关，助力投资者决策。
Cross-Lingual News Event Correlation for Stock Market Trend Prediction
- 融合多语言新闻的情感、实体与语义分析，构建金融事件时间线。
- 两年数据验证：新闻情绪与巴基斯坦股市走势存在显著关联。
- 适合量化交易者和跨境投资研究者参考，提升决策精度。
在现代经济格局中，金融与金融科技（FinTech）的融合已成为股票趋势分析的关键。本研究填补了全球不同经济体间金融动态理解的空白，构建了一个结构化金融数据集，并提出一种基于自然语言的跨语言金融预测（NLFF）流程，实现全面金融分析。通过情感分析、命名实体识别（NER）和语义文本相似性，对新闻文章进行解析，提取、映射并可视化金融事件的时间线，揭示新闻事件与股市走势之间的关联。方法验证显示，股价波动与跨语言新闻情绪之间存在显著相关性，基于两年跨语言新闻数据，覆盖巴基斯坦证券交易所两个主要行业。该研究为关键事件提供深刻洞察，通过有效可视化增强投资者决策优势，发掘最优投资机会。
原文摘要 · Abstract (English)
In the modern economic landscape, integrating financial services with Financial Technology (FinTech) has become essential, particularly in stock trend analysis. This study addresses the gap in comprehending financial dynamics across diverse global economies by creating a structured financial dataset and proposing a cross-lingual Natural Language-based Financial Forecasting (NLFF) pipeline for comprehensive financial analysis. Utilizing sentiment analysis, Named Entity Recognition (NER), and semantic textual similarity, we conducted an analytical examination of news articles to extract, map, and visualize financial event timelines, uncovering the correlation between news events and stock market trends. Our method demonstrated a meaningful correlation between stock price movements and cross-linguistic news sentiments, validated by processing two-year cross-lingual news data on two prominent sectors of the Pakistan Stock Exchange. This study offers significant insights into key events, ensuring a substantial decision margin for investors through effective visualization and providing optimal investment opportunities.
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