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.
| Published in | International Journal of Theoretical and Applied Mathematics (Volume 2, Issue 2) |
| DOI | 10.11648/j.ijtam.20160202.21 |
| Page(s) | 100-109 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2016. Published by Science Publishing Group |
Principal Component Analysis, Stock Selection, Screen Plot, Uncertainty
| [1] | Feeney, G. and Hester, D. ”Stock Market indices: a principal Component Analysis», in D Hester and J Tobin (eds), Risk aversion and portfolio choice, Wiley, (1967). New York. |
| [2] | Horne, B. and Camp, N. Principal component analysis for selection of optimal snp sets that capture intraganic, genetic variation. Genetic Epidemiology, (2004). 26: 11–21. |
| [3] | Kaiser, H. The application of electronic computers to factor analysis. Educational and Psychological Measurement, (1960). 20: 141–15. |
| [4] | Kanbur, S. and Marian, H. Principal component analysis of RR lyre light curves. (2004). http//:www.astro.umass.edu/shashi/paper 7.pdf. |
| [5] | Kritzman, M., Yaunzhen, L., Sebastien, P., and Roberto, R Principal component as a measure of systematic risk. MIT Sloan School Working Paper 4785., (2011). |
| [6] | Loretan, “Generating market risk scenarios using principal component analysis: Methodological and practical considerations”. (1997). Federal Reserves Board, htt//:www.bis.org/publ/ecsc07.pdf. |
| [7] | Mbeledegu, N., Odoh, M., and Umeh, M Stock feature extraction using principal component analysis. International Conference on Computer Technology and Science. IACSIT Press Singapore, (2012). DOI: 10.7763/IPCSIT V47.44. |
| [8] | Novosyolov, A. and Satchkev, D. Factor term structure modelling using principal component analysis. Journal of Asset Management, (2008). 9 (1): 49–60. |
| [9] | Sterns, J. Applied Multivariate Statistics for the Social Sciences. Hillsdale, NJ: Lawrence Erlbaum Associates. (1986). |
| [10] | Wang, Y. and In-Chan, C.” Market index and stock price direction prediction using machine learning techniques: An empirical study on the kospi and hsi. Science Directs, (2013). 1: 1–13. |
APA Style
Abonongo John, Oduro F. T., Ackora-Prah J. (2016). Selection of Stocks on the Ghana Stock Exchange Using Principal Component Analysis. International Journal of Theoretical and Applied Mathematics, 2(2), 100-109. https://doi.org/10.11648/j.ijtam.20160202.21
ACS Style
Abonongo John; Oduro F. T.; Ackora-Prah J. Selection of Stocks on the Ghana Stock Exchange Using Principal Component Analysis. Int. J. Theor. Appl. Math. 2016, 2(2), 100-109. doi: 10.11648/j.ijtam.20160202.21
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ijtam.20160202.21,
author = {Abonongo John and Oduro F. T. and Ackora-Prah J.},
title = {Selection of Stocks on the Ghana Stock Exchange Using Principal Component Analysis},
journal = {International Journal of Theoretical and Applied Mathematics},
volume = {2},
number = {2},
pages = {100-109},
doi = {10.11648/j.ijtam.20160202.21},
url = {https://doi.org/10.11648/j.ijtam.20160202.21},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtam.20160202.21},
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.},
year = {2016}
}
TY - JOUR T1 - Selection of Stocks on the Ghana Stock Exchange Using Principal Component Analysis AU - Abonongo John AU - Oduro F. T. AU - Ackora-Prah J. Y1 - 2016/12/10 PY - 2016 N1 - https://doi.org/10.11648/j.ijtam.20160202.21 DO - 10.11648/j.ijtam.20160202.21 T2 - International Journal of Theoretical and Applied Mathematics JF - International Journal of Theoretical and Applied Mathematics JO - International Journal of Theoretical and Applied Mathematics SP - 100 EP - 109 PB - Science Publishing Group SN - 2575-5080 UR - https://doi.org/10.11648/j.ijtam.20160202.21 AB - 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. VL - 2 IS - 2 ER -
Information
Email: