Macroeconomic Time-Series & Financial Trend Analysis

Overview

This project focused on complex macroeconomic analysis and financial trend forecasting. By utilizing robust indexing and advanced data transformation techniques, the study normalized historical economic indicators and extracted clean signals from highly volatile stock market data.

Methodology

Inflation-Adjusted Pricing Models: Reconstructed historical pricing data into "real" (inflation-adjusted) terms using the Consumer Price Index for All Urban Consumers (CPI-U). Identified distinct periods where price inflation outpaced general macroeconomic inflation.

Comparative Productivity Indexing: Compiled annual real GDP per capita for multiple international economies over a 20-year span. Developed normalized baseline indices (base year 2005) to rigorously compare and chart long-term productivity growth rates across different sovereign nations.

Financial Time-Series Transformation: Downloaded and processed maximum-length historical monthly stock data for multiple publicly-traded enterprise companies. Applied a sequential double-transformation—executing 3-period moving averages to filter out stochastic market noise followed by logarithmic normalizations—to isolate and evaluate true underlying growth momentum.