Week 4: Applied Financial Data Analysis with Python

Topics Covered:

• Advanced Data Analysis Techniques
• Python for Financial Research
• Post-Earnings Announcement Drift (PEAD)
• Data Quality and Validation

Core Resources:

• Primary Implementation Guide: WRDS PEAD Python Implementation
• Original Research: PEAD Original Study

Weekly Assignment :

Part 1: PEAD Replication

1. Data Preparation
   • Download and clean required data
   • Implement proper data quality checks
   • Document data sources and date ranges
2. Core Analysis
   • Implement PEAD calculation following WRDS example
   • Calculate abnormal returns
   • Form portfolios based on earnings surprises
3. Results & Validation
   • Compare your results with original findings
   • Document any discrepancies
   • Provide clear visualizations

Part 2: Extended Analysis

1. Updated Data Analysis
   • Update through 2024
   • Test in different market conditions
2. Robustness Tests
   • Different portfolio formation methods
   • Various event windows
   • Market condition analysis

Bonus: AI Integration

1. AI-Powered Analysis
   • Data validation using AI tools
   • Pattern recognition
   • Anomaly detection
2. AI Insights
   • Generate additional insights
   • Compare AI findings with traditional analysis
   • Document AI tool effectiveness

Implementation Guide:

1. Data Preparation

# Example data retrieval and preparation
import pandas as pd
import numpy as np
from pandasai import SmartDataframe

# Load and prepare data
earnings_data = pd.read_csv('earnings_data.csv')
market_data = pd.read_csv('market_data.csv')

# Basic quality checks
def basic_quality_checks(df):
    print("Missing values:", df.isnull().sum())
    print("Duplicates:", df.duplicated().sum())
    print("Date range:", df['date'].min(), "to", df['date'].max())

2. PEAD Analysis

# Core PEAD calculation
def calculate_car(data, event_window=(-2, 2)):
    """Calculate Cumulative Abnormal Returns"""
    # Follow WRDS implementation
    pass

def form_portfolios(data, n_portfolios=10):
    """Form portfolios based on earnings surprise"""
    # Follow WRDS implementation
    pass

3. AI Enhancement

# Optional AI analysis
from pandasai.llm import Ollama

# Initialize AI assistant
llm = Ollama(model="llama2")
df_smart = SmartDataframe(your_pead_results, config={'llm': llm})

# Example AI analysis
analysis = df_smart.chat("Analyze the PEAD patterns")

Submission Requirements:

1. Code Files
   • Well-documented Python scripts
   • Clear comments explaining each step
   • Requirements.txt file
2. Analysis Report
   • Data preparation steps
   • Implementation details
   • Results and comparisons
   • AI-enhanced insights (if used)
3. Presentation
   • Key findings visualization
   • Comparison with original study
   • Discussion of challenges

Best Practices:

• Code Organization
  • Follow WRDS code structure
  • Document any modifications
  • Use version control
• Data Management
  • Keep raw data separate
  • Document transformations
  • Maintain data versions
• Result Validation
  • Compare with benchmarks
  • Test across periods
  • Document discrepancies