**1. Introduction**

According to Sullivan and Sheffrin [1], diversification is the process of allocating capital in a way that reduces the exposure to any particular asset or risk. Fama and Miller [2] state that the Capital Asset Pricing Model (CAPM) introduces the concepts of diversifiable and non-diversifiable risk. Synonyms for diversifiable risk are unsystematic risk and security-specific risk. Synonyms for non-diversifiable risk are systematic risk, beta risk, and market risk. Thus, the CAPM argues that investors should only be compensated for non-diversifiable risk.

According to Pasini [3], the principal Component Analysis (PCA) is a method of multivariate analysis. The idea behind the PCA is to reduce the dimensionality of a dataset in which there are a large number of interrelated variables, to maximize the variance of a linear combination of the variables. It is a method applied to data with no groupings among the observations and no partitioning of the variables into subsets *y* and *x*. Particularly, the principal components are obtained by applying this method. The first one is the linear combination with maximal variance, the second one is the linear combination with maximal variance in the orthogonal direction

to the first principal component and so for the others. Moreover, they are ordered sequentially with the first one explaining much of the variation as it can.

With the help of the PCA, we measure how each sector is affected by market risk, measured by the first component. This article proceeds as follows. The next section presents relevant literature on PCA and the stock market, and the third section describes our methods and data. The fourth section presents the analyses of the findings, and lastly, we present our conclusions in the fifth section.
