Overview

This project analyzes historical equity market data to evaluate long-term market performance, company-level returns, and risk-adjusted investment outcomes.
An end-to-end analytics pipeline was built using Azure SQL for data modeling and Power BI for visualization.

The analysis focuses on understanding how market growth evolved over time and whether company performance justified the level of risk taken.

Business Problem

Investors often evaluate performance based only on returns, ignoring risk exposure and market timing.
The objective of this analysis was to:

• Measure true investment growth using compounded returns

• Identify companies driving market performance

• Evaluate performance relative to volatility

• Understand when major market shifts occurred

Approach

The solution was designed using a layered analytics architecture:

1. Raw market data ingested into Azure SQL

2. Warehouse-style modeling using fact and dimension tables

3. Daily financial metrics calculated using SQL window functions

4. Returns aggregated using compounding methodology

5. Interactive dashboards built in Power BI

Analysis Performed

Key analytical components included:

• Market cumulative performance tracking

• Quarterly performance segmentation

• Company return ranking

• Volatility measurement

• Risk vs Return comparison

Financial returns were compounded rather than summed to accurately reflect investment growth.

Key Insights

• Market growth followed cyclical recovery phases rather than steady expansion.

• Performance gains were concentrated among a subset of companies.

• Higher volatility did not consistently translate into higher returns.

• Several companies showed inefficient risk profiles despite large price movements.

Tools & Technologies

• Azure SQL Database

• T-SQL

• Power BI

• DAX

• Data Modeling & Financial Analytics