Understanding the Impact of News Articles on the Movement of Market Index: A Case on Nifty 50

ArXiv ID: 2412.06794 “View on arXiv”

Authors: Unknown

Abstract

In the recent past, there were several works on the prediction of stock price using different methods. Sentiment analysis of news and tweets and relating them to the movement of stock prices have already been explored. But, when we talk about the news, there can be several topics such as politics, markets, sports etc. It was observed that most of the prior analyses dealt with news or comments associated with particular stock prices only or the researchers dealt with overall sentiment scores only. However, it is quite possible that different topics having different levels of impact on the movement of the stock price or an index. The current study focused on bridging this gap by analysing the movement of Nifty 50 index with respect to the sentiments associated with news items related to various different topic such as sports, politics, markets etc. The study established that sentiment scores of news items of different other topics also have a significant impact on the movement of the index.

Keywords: Sentiment analysis, Stock price prediction, Topic modeling, Natural language processing, Index forecasting, Equities

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper uses relatively simple statistical models like Ridge and Lasso regression with a clear methodology for data scraping and preprocessing, resulting in moderate math complexity. However, it demonstrates high empirical rigor through extensive data collection (400k+ news items over 3 years), implementation of web scraping and sentiment analysis pipelines, and clear backtesting-ready setup for Nifty 50 index prediction.

  flowchart TD
    A["Research Goal:<br>How do news topic sentiments<br>impact Nifty 50 Index movement?"] --> B["Data Collection:<br>Nifty 50 historical data &<br>Multi-topic news articles"]
    B --> C["Preprocessing &<br>Topic Modeling LDA"]
    C --> D["Sentiment Analysis<br>Calculate scores per topic"]
    D --> E["Computational Model<br>Time-series integration & Regression"]
    E --> F{"Key Finding"}
    F --> G["Sentiment of diverse topics<br>politics, markets, sports<br>significantly predicts index movement"]