International Journal of Theoretical and Applied Mathematics 2016; 2(2): 100-109 http://www.sciencepublishinggroup.com/j/ijtam doi: 10.11648/j.ijtam.20160202.21 Selection of Stocks on the Ghana Stock Exchange Using Principal Component Analysis Abonongo John * , Oduro F. T., Ackora-Prah J. College of Science, Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana Email address: abonongojohn@gmail.com (A. John) * Corresponding author To cite this article: Abonongo John, Oduro F. T., Ackora-Prah J. Selection of Stocks on the Ghana Stock Exchange Using Principal Component Analysis. International Journal of Theoretical and Applied Mathematics. Vol. 2, No. 2, 2016, pp. 100-109. doi: 10.11648/j.ijtam.20160202.21 Received: July 19, 2016; Accepted: September 12, 2016; Published: December 10, 2016 Abstract: A major problem in stock selection is the use of the right procedure(s) in identifying the best stock(s). The principal component analysis was employed as a data reduction technique in selecting stock(s) that characterize each sector on the Ghana Stock Exchange. The results indicated that, among the 9 stocks in the Finance sector, only 3 stocks (CAL, ETI, and GCB) were able to characterize the sector. The Distribution sector had 2 stocks (PBC and TOTAL) among the 4 stocks characterizing the sector. The Food and Beverage sector had only FML characterizing the sector out of the 3 stocks. Also, the information Technology had CLYD characterizing the sector out of the 2 stocks. The Insurance sector had EGL characterizing the sector out of the 2 stocks. The Manufacturing sector had only 2 stocks (PZC and UNIL) characterizing the sector out of the 10 stocks and for the Mining sector, 2 stocks (TLW and AGA) among the 4 stocks were the best. In effect, the 34 stocks considered from the Ghana Stock Exchange were reduced to 12 stocks (CAL, ETI, GCB, PBC, TOTAL, FML, CLYD, EGL, PZC, UNIL, TLW and AGA). The results also indicated that the selected stocks were able to explain much of the variance in their respective sectors compared to the rest of the stocks in that same sector and thus could be considered for further analysis and probably investment. Keywords: Principal Component Analysis, Stock Selection, Screen Plot, Uncertainty 1. Introduction Investing on the stock market is poised with high risks and high gains, hence, it attracts a great number of investors. Also, as far as information regarding stocks is concerned, it is often complex and has a lot of uncertainty, making it difficult to select attractive stocks. Even though the selection of attractive stocks is not easy for investors, Principle Component Analysis (PCA) can guide an investor in telling attractive stocks from unattractive ones. The PCA is more suitable in studying the covariance structure of a vector time series. It is appropriate when one have obtained measures on a number of observed variables and wish to develop a smaller number of artificial variables that will account for most of the variance in the observed variables; a variable reduction procedure. Principal Component Analysis technique has been extensively used in many studies in (e.g., [8]) described the joint structure with a model that can potentially be used for scenario estimation and analysis of the risk of interest rate- sensitive portfolios. Three variations of the principal component analysis technique to decompose global interest rate and yield curve implied volatility structure were examined, highlighting that global yield curve structure can be explained with 15 to 20 factors, whereas implied volatility structure needs at least 20 global factors, furthermore in (e.g., [8]) also used principal component analysis in the granting of loan. The result showed that the utility of principal component analysis in the banking sector to decrease the size of data, without much loss of information in (e.g., [2]) performed a selection of optimal SNP sets that capture intragenic genetic variation. Their results revealed that principal component analysis may be a strong tool for establishing an optimal SNP set that maximizes the amount of genetic variation captured for a candidate gene using a minimal number of SNP set in (e.g., [4]) used the principal component analysis in investigating the structure of light curves of RRabstar. They concluded that the principal component analysis was an effective way to account for