EIGENVALUE-BASED PRINCIPAL COMPONENT ANALYSIS FOR DIMENSIONALITY REDUCTION IN FINANCIAL RISK ASSESSMENT OF SELECTED BANKING STOCKS

Abstract

This study investigates the application of Principal Component Analysis (PCA) as a dimensionality reduction technique for analyzing financial time-series data, with a specific focus on the Indian banking sector. Daily stock price data of thirteen selected private and public sector banking companies are examined to identify dominant sources of variation in asset returns. To ensure suitability for multivariate analysis, stock prices are transformed into logarithmic returns and subsequently standardized to eliminate scale effects and volatility differences across firms. PCA is implemented using Python, and eigenvalue decomposition is employed to extract orthogonal principal components that explain the maximum variance in the dataset.

The empirical results reveal that a relatively small number of principal components effectively capture the underlying structure of the stock return series. In particular, the first three principal components account for a substantial proportion of total variance, representing common market-wide movements as well as asset-specific risk characteristics. The extraction of these components significantly reduces data dimensionality and multicollinearity, thereby simplifying the complex correlation structure inherent in financial datasets.

The findings highlight the effectiveness of PCA in isolating systematic and idiosyncratic risk factors, offering valuable insights for portfolio management and risk analysis. By summarizing the behaviour of thirteen banking stocks through three dominant principal components, the study demonstrates how PCA can enhance interpretability without significant loss of information. Overall, the results confirm PCA as a robust analytical tool for financial data analysis, particularly in high-dimensional environments, and provide practical implications for investors, analysts, and policymakers seeking efficient methods for risk assessment and decision-making.