Paper: arXiv 2410.00024

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.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper relies on standard NLP techniques like sentiment analysis and NER with minimal advanced mathematical formalism, but presents a structured pipeline validated on real, two-year cross-lingual news and stock data from the Pakistan Stock Exchange.

Research Flowchart

  flowchart TD
  A["Research Goal<br/>Cross-Lingual Stock Trend Prediction"] --> B["Data Collection<br/>2-year news + PSX data"]
  B --> C["Preprocessing<br/>NER & Alignment"]
  C --> D["Core Processing<br/>Sentiment & Semantic Similarity"]
  D --> E["Analysis<br/>Event Correlation"]
  E --> F["Outcome<br/>Validated Prediction Model & Insights"]