Cross-Lingual News Event Correlation for Stock Market Trend Prediction
This work addresses stock trend prediction for investors by integrating FinTech with cross-lingual analysis, though it appears incremental as it applies existing NLP methods to a new financial dataset.
The study tackled the problem of understanding financial dynamics across global economies by analyzing cross-lingual news articles to correlate events with stock market trends, resulting in a meaningful correlation validated on two-year data from the Pakistan Stock Exchange.
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.