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Cross-Lingual News Event Correlation for Stock Market Trend Prediction

Author

Listed:
  • Sahar Arshad
  • Nikhar Azhar
  • Sana Sajid
  • Seemab Latif
  • Rabia Latif

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.

Suggested Citation

  • Sahar Arshad & Nikhar Azhar & Sana Sajid & Seemab Latif & Rabia Latif, 2024. "Cross-Lingual News Event Correlation for Stock Market Trend Prediction," Papers 2410.00024, arXiv.org.
    • Handle: RePEc:arx:papers:2410.00024
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    References listed on IDEAS

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    2. Adeel Mumtaz & Tahir Saeed & M. Ramzan, 2018. "Factors affecting investment decision-making in Pakistan stock exchange," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 1-14, December.
    3. Das, Debojyoti & Kannadhasan, M. & Bhattacharyya, Malay, 2019. "Do the emerging stock markets react to international economic policy uncertainty, geopolitical risk and financial stress alike?," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 1-19.
    4. Yan Cheng, 2022. "Online Stock Price Prediction Based on Interval Data Analysis," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 13(3), pages 1-13, July.
    5. Yang, Xiaolan & Zhu, Yu & Cheng, Teng Yuan, 2020. "How the individual investors took on big data: The effect of panic from the internet stock message boards on stock price crash," Pacific-Basin Finance Journal, Elsevier, vol. 59(C).
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