Cross-Lingual News Event Correlation for Stock Market Trend Prediction

TL;DR

This paper presents a cross-lingual NLP pipeline that analyzes news sentiment and events to predict stock market trends, validated on Pakistan Stock Exchange data, offering valuable insights for investors.

Contribution

It introduces a novel cross-lingual financial analysis method combining sentiment, NER, and semantic similarity for stock trend prediction.

Findings

Significant correlation between news sentiment and stock prices.

Effective visualization of financial event timelines.

Validated approach on Pakistan Stock Exchange data.

Abstract

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.