Mergers & Acquisitions (M&A) Data Analysis Project

Overview

This project is a comprehensive data analysis of Mergers & Acquisitions (M&A), covering the entire data pipeline from data extraction and cleaning to exploratory analysis, statistical testing, and visualization. The goal is to analyze acquisition trends, financial behaviors, and industry patterns using data from Financial Modeling Prep API and Tracxn Excel datasets.

Project tracking and task management were conducted using Jira (Project Board)

Project presentation conducted in prezi (Presentation)

Additionally, an interactive dashboard was developed in Streamlit to enable data exploration and visualization. Further, Tableau visualizations provide additional insights.

🔗 Live Streamlit Dashboard: M&A Streamlit
📊 Tableau Visualizations: M&A Tableau

---

Features

Data Collection & Cleaning

• Extracted financial and M&A data from Financial Modeling Prep API and Tracxn.
• Cleaned and formatted datasets using Pandas.
• Stored structured data in a MySQL database.

Exploratory Data Analysis (EDA) & Hypothesis Testing

• Conducted descriptive statistics on M&A trends.
• Performed hypothesis testing, including:
  • ANOVA on Acquisition Prices for Large Acquisitions: Analyzing if acquisition prices differ significantly among acquirers.
  • ANOVA on Acquisition Prices Across Acquirers in the Same Industry.
  • Chi² Test on BlackRock's Preference for Mature Companies: Testing if BlackRock acquires companies older than 10+ years more frequently.
  • Industry-Level T-Test on Acquisition Prices: Comparing BlackRock and Berkshire Hathaway’s acquisition pricing within the same industry.
• Used t-tests, ANOVA, and chi-square tests for statistical validation.

Interactive Streamlit Dashboard

🛠 User Interaction

✅ Run Analysis - identifies acquisition trends and financial strategy via MySQL. ✅ Insert new M&A data - directly into MySQL via a Streamlit form.
✅ Delete existing M&A records - based on company name or symbol.

Interactive Tableau Dashboard

📊 M&A Visualizations & Insights

✅ Acquisition Trends Dashboard - interactive dashboard for deeper insights into KPIs.

---

Installation & Setup

🔧 Prerequisites

• Python 3.x
• MySQL database
• Streamlit, Pandas, SQLAlchemy, Matplotlib, Seaborn

📥 Clone the Repository

git clone https://github.com/ricgolding/M_A_Analysis.git

📌 Install Dependencies

pip install -r requirements.txt

🛠 Configure MySQL Connection

Set up the database connection in Streamlit secrets:

1. Create a .streamlit/secrets.toml file.
2. Add your MySQL credentials:

[database]
user = "your_username"
password = "your_password"
host = "localhost"
port = 3306
name = "mergers_acquisitions"

🚀 Run the Streamlit App

streamlit run app.py

---

Usage

• Visualize M&A trends across companies and industries.
• Analyze financial relationships between acquisitions and revenue/net income.
• Modify the database by adding or removing data entries interactively.
• Explore statistical findings on M&A behaviors.
• Use Tableau visualizations for deeper insights.

---

Project Structure

📂 M_A_Analysis
├── 📂 .streamlit
│   ├── secrets.toml  # MySQL credentials
├── 📂 data
│   ├── 📂 clean
│   │   ├── income_statement_data.csv
│   │   ├── mergers_acquisitions_data.csv
│   │   ├── profile_data.csv
│   │   ├── stock_data.csv
│   ├── 📂 raw
│   │   ├── tracxn_mna.xlsx  # Renamed from ma-activity-tracxn.xlsx
├── 📂 notebooks
│   ├── cleaning.ipynb  # Data cleaning & preprocessing
│   ├── EDA.ipynb  # Exploratory Data Analysis
│   ├── statistical_testing.ipynb  # Hypothesis testing & statistical analysis
├── 📂 src
│   ├── streamlit_functions.py  # M&A analysis functions for Streamlit
│   ├── sql_functions.py  # Database query functions
├── app.py  # Streamlit application entry point
├── requirements.txt  # Python dependencies
├── README.md  # Project documentation

---

Future Improvements

• Automate data extraction & updates from APIs.
• Add real-time M&A tracking functionality.
• Expand dataset to include more financial metrics.
• Implement predictive analytics for M&A trend forecasting.
• Enhance data visualization with more interactive elements.

---

Author

👤 Ricardo Golding
📧 goldingra@gmail.com
🐙 GitHub

---

🚀 This project delivers data-driven insights into the M&A landscape, helping analyze corporate acquisition strategies and trends.