Exploratory Data Analysis (EDA) on Stock News Headlines and Different company stock data

Table of Contents

1. Project Overview
2. Key Features
3. Methodology
4. Results and Insights
5. Correlation Analysis
6. Challenges
7. Technologies Used
8. How to Use

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Project Overview

The dataset comprises 1,407,328 news headlines from over 1,034 publishers, combined with stock price data for major companies (META, AMZN, TSLA, NVDA, GOOG, AAPL, MSFT). This project includes:

• Textual analysis to extract headline lengths, sentiment, and topics.
• Time series analysis to identify publishing patterns.
• Sentiment vs. Stock Returns correlation analysis to evaluate how news sentiment affects daily stock price movements.

Key Features

• Descriptive Statistics:

  • Headline length analysis (mean: 73 characters, max: 512).
  • Publisher activity (top contributor: Paul Quintaro with 228,373 articles).

• Text Analysis:

  • Topic modeling identified topics such as earnings reports, market reactions, and price targets.
  • Sentiment analysis to classify articles as positive, neutral, or negative.

• Stock Correlation Analysis:

  • Analyzed relationships between daily sentiment scores and stock price returns for seven major stocks.

• Time Series Analysis:

  • Publication trends by day, month, and hour.

Methodology

1. Data Preprocessing:

   • Handled missing values and ensured consistency between news dates and stock trading days.

2. Sentiment Analysis:

   • News headlines were analyzed to compute daily average sentiment scores using NLP techniques.

3. Stock Movement Analysis:

   • Calculated daily percentage stock price returns based on adjusted closing prices.

4. Correlation Analysis:

   • Performed Pearson correlation between sentiment scores and stock returns to assess the strength and direction of the relationship.

5. Descriptive Statistics & NLP:

   • Conducted headline length analysis, publisher contributions, and topic modeling.

Results and Insights

Descriptive Statistics

• Headline Lengths: Average length is 73 characters, with 50% of headlines between 47 and 87 characters.
• Publisher Contributions:
  • Top contributors include Paul Quintaro, Lisa Levin, and Benzinga Newsdesk.

Text Analysis

• Top Topics Identified:
  • Topic 1: Earnings reports and estimates (e.g., "EPS", "sales", "Q4").
  • Topic 2: Price targets and ratings (e.g., "upgrade", "downgrade", "target").
  • Topic 3: Market reactions (e.g., "shares", "trading", "performance").

Time Series Analysis

• Daily Trends: Peak publication activity occurs on Thursdays; weekends show minimal activity.
• Hourly Trends: Most news articles are published during market hours, with a sharp peak at 10:00 AM.

Correlation Analysis

The correlation analysis examined the relationship between average daily sentiment scores and daily stock returns. Results for each stock are summarized below:

Stock	Correlation	P-Value	Observation
META	-0.0061	0.7943	Weak negative correlation, not significant.
AMZN	-0.0194	0.3592	Weak negative correlation, not significant.
TSLA	0.0277	0.1909	Weak positive correlation, not significant.
NVDA	0.0091	0.6668	Weak positive correlation, not significant.
GOOG	0.0143	0.5007	Weak positive correlation, not significant.
AAPL	-0.0028	0.8944	Negligible negative correlation.
MSFT	-0.0118	0.5776	Weak negative correlation, not significant.

Key Observations:

• All correlation coefficients are close to zero, indicating negligible relationships between news sentiment and stock price movements.
• High p-values (greater than 0.05) suggest that the correlations are not statistically significant.

Challenges

• Date Alignment: Aligning sentiment data with valid stock trading days required rigorous data normalization.
• Sparse Sentiment Data: On certain days, a limited number of news articles resulted in less representative sentiment scores.
• Low Correlation: Results suggest external factors (e.g., market forces) may dominate short-term stock price changes, reducing sentiment influence.

Technologies Used

• Python: Primary language for data processing and analysis.
• Libraries:
  • pandas, numpy: Data manipulation and statistical analysis.
  • matplotlib, seaborn: Visualizations.
  • nltk, scikit-learn: Sentiment analysis and NLP tools.
  • TA-Lib, PyNance: Stock price indicators and financial metrics.

How to Use

1. Clone this repository:

   git clone https://github.com/Azazh/Sentiment-Driven-Financial-Insights.git  
   
   

2. Install the required libraries:

   pip install -r requirements.txt  
   
   

3. Run the analysis notebook:

   • Open and execute Stock_Analysis_Insights.ipynb in Jupyter Notebook.

4. Output Files:

   • Analysis results and visualizations will be generated under the output/ directory.