Applying News and Media Sentiment Analysis for Generating Forex Trading Signals

ArXiv ID: 2403.00785 “View on arXiv”

Authors: Unknown

Abstract

The objective of this research is to examine how sentiment analysis can be employed to generate trading signals for the Foreign Exchange (Forex) market. The author assessed sentiment in social media posts and news articles pertaining to the United States Dollar (USD) using a combination of methods: lexicon-based analysis and the Naive Bayes machine learning algorithm. The findings indicate that sentiment analysis proves valuable in forecasting market movements and devising trading signals. Notably, its effectiveness is consistent across different market conditions. The author concludes that by analyzing sentiment expressed in news and social media, traders can glean insights into prevailing market sentiments towards the USD and other pertinent countries, thereby aiding trading decision-making. This study underscores the importance of weaving sentiment analysis into trading strategies as a pivotal tool for predicting market dynamics.

Keywords: Sentiment Analysis, Foreign Exchange, Trading Signals, Natural Language Processing, USD, Foreign Exchange

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Street Traders
  • Why: The paper relies on basic ML algorithms like Naive Bayes and simple metrics like weighted averages, keeping math complexity low; however, it is grounded in real-world financial data (news/social media) and outlines specific data sources and processing steps, giving it practical, implementation-oriented rigor.
  flowchart TD
    Goal["Research Goal:<br>Forecast Forex (USD) movements<br>using news & social media sentiment"] -->|Input Data| Data
    subgraph Data ["Data Inputs"]
        News["News Articles"]
        SocMed["Social Media Posts"]
    end
    Data -->|Processing| Methodology["Methodology:<br>1. Lexicon-Based Analysis<br>2. Naive Bayes ML Algorithm"]
    Methodology -->|Computational Process| SentimentScore["Generate Sentiment Scores"]
    SentimentScore -->|Signal Generation| TradingSignal["Create Trading Signals:<br>Buy/Sell/Hold"]
    TradingSignal -->|Validation| Findings["Key Findings/Outcomes:<br>- Sentiment predicts market movements<br>- Effective across market conditions<br>- Valuable for trading strategies"]