Cross-Lingual News Event Correlation for Stock Market Trend Prediction

Sahar Arshad, Nikhar Azhar, Sana Sajid, Seemab Latif, Rabia Latif
9/16/2024
q-fin.STcs.LG

No confident code implementation found for this paper. 1 possible match can be shown below.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Question-focused Summarization by Decomposing Articles into Facts and Opinions and Retrieving Entities

83% match2023

Natural Language Processing and Multimodal Stock Price Prediction

83% match2024

Automatic detection of relevant information, predictions and forecasts in financial news through topic modelling with Latent Dirichlet Allocation

83% match2024

BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights

82% match2024

Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing

82% match2023

Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines

82% match2024

Cite this paper

@article{arshad2024crosslingual,
  title  = {Cross-Lingual News Event Correlation for Stock Market Trend Prediction},
  author = {Sahar Arshad and Nikhar Azhar and Sana Sajid and Seemab Latif and Rabia Latif},
  year   = {2024},
  eprint = {2410.00024},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.00024v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2410.00024.svg)](https://codeofpaper.com/paper/2410.00024)

Discussion