The Performance of Maximum Likelihood Factor Analysis on South African Stock Price Performance

  • Andile Khula North West University
  • Ntebogang Dinah Moroke North West University


Abstract: The purpose of this paper is to explore the effectiveness and applicability of Maximum Likelihood Factor Analysis (MLFA) method on stock price performance. This method identifies the variables according to their co-movement and variability and builds a model that can be useful for prediction and ranking or classification. The results of factor analysis in this study provide a guide as far as investment decision is concerned. Stock price performance of the seven well-known and biggest companies listed in the Johannesburg stock exchange (JSE) was used as an experimental unit. Monthly data was available for the period 2010 to 2014.Details of a trivariate factor model is: Factor 1 comprises of Absa and Standard Bank (Financial sectors), Factor 2 has Shoprite and Pick ‘n Pay (Retail sectors) while Factor 3 collected Vodacom MTN and Sasol (Industrial sectors). The companies contribute 46.9%, 12.7% and 10.8% respectively to the three sectors and these findings are confirmed by a Chi-square and the Akaike information criterion to be valid. The three factors are also diverse and reliable according to Tucker and Lewis and Cronbach’s coefficients. The findings of this study give economic significance and the study is relevant as it gives investors and portfolio manager’s sensible investment reference.

Keywords: Maximum Likelihood Factor Analysis, stock prices


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Al-Debie, M. & Walker, M. (1999). Fundamental information analysis: An extension and UK evidence. The British Accounting Review, 31(3), 261-280.
Al-Radaideh, Q.A., Assaf, A. & Alnagi, E. (2013).Predicting Stock Prices Using Data Mining Techniques. The International Arab Conference on Information Technology (ACIT’2013).
Bastos, J. A. & Caiado, J. (2010). The structure of international stock market returns. Rua do Quelhas, 6, 1200-781.
Brooks, C. (2002). Introductory econometrics for finance: Cambridge University Press. Cambridge
Cao, Y. (2010). A Bayesian Approach to Factor Analysis via Comparing Posterior and Prior Concentration.Thesis submitted to the University of Toronto.
Cheng, W. (2005). Factor Analysis for Stock Performance. Professional Masters thesis submitted to Worcester Polytechinic Institute.
Costello, A. B. & Osborne, J. (2005). Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Practical Assessment Research & Evaluation, 10(7).
Cronbach, L. J. & Shavelson, R. J. (2004). My current thoughts on coefficient alpha and successor procedures. Educational and psychological measurement, 64(3), 391-418.
Făt, C. M. & Dezsi, E. (2012). A Factor Analysis Approach of International Portfolio Diversification: Does it Pay Off? Procedia Economics and Finance, 3, 648-653.
Floyd, F. J. & Widaman, K. F. (1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological assessment, 7(3),286.
Greene, W. H. (2003). Econometric Analysis. Pearson Education, Upper Saddle.
Gu, A. & Zeng, P. (2014). Sector-Based Factor Models for Asset Returns.arXiv :1408.2794vi.
Gujarati, D. N. (2003). Basic Econometrics (4e). McGraw-Hill Inc. New York.
Hair, J. F., Black, W. C., Babin, B. J. & Anderson R. E. (2010). Multivariate Data Analysis: A Global Perspective, Prentice-Hall: Upper Saddle River, New Jersey.
Hui, T. K. (2005). Portfolio diversification: a factor analysis approach.Applied Financial Economics, 15(12), 821-834.
Illueca, M. & Lafuente, J. A. (2002). International stock market linkages: A factor analysis approach. Journal of Asset Management, 3(3), 253-265.
Kumar, R. (2013). The Effect of Macroeconomic Factors on Indian Stock Market Performance: A Factor Analysis Approach. IOSR Journal of economics and finance, 8(1),14-21.
Lev, B. & Thiagarajan, S. R. (1993). Fundamental information analysis. Journal of Accounting research,31, 190-215.
Muca, M., Puka, L., Bani, K. & Shahu, E. (2013). Principal Component and the Maximum Likelihood methods as tools to analyze large data with a psychological testing example. European Scientific Journal, 9(20), 176-183.
Napper, L. E., Wood, M. M. & Klahn, J. A. (2008). Convergent and discriminant validity of three measures of stage of change. Journal of the Society of Psychologists in addictive behaviours, 22(23), 362-371.
Osborne, J. W. (2005). Improving your data transformations: Applying the Box-Cox transformation. Practical Assessment, Research & Evaluation, 15(12), 1-9.
Raykov, T. & Marcoulides, T. R. G. A. (2008). An Introduction to applied multivariate analysis and structural equation modelling. Routledge, New York.
Rencher, A. C. (2003). Methods of multivariate analysis. John Wiley and Sons, New York.
Richard, A. J. & Dean, W. W. (2002). Applied multivariate statistical analysis.Prenticee Hall, London.
Ritchie, J. C. (1996). Fundamental analysis: a back-to-the-basics investment guide to selecting quality stocks: Irwin Professional Publishers.
Rossi, P. H., Wright, J. D. & Anderson, A. B. (2013). Handbook of survey research: Academic Press, San Diego.
Shadkam, E. (2014). FC approach in portfolio selection of Tehran’s stock market. Journal of Asian Finance Economics and Business, 1(2), 31-37.
Spearman, C. (1904). General Intelligence, objectively determined and measured. The American Journal of Psychology, 15(2), 201-292.
Tabachnick, B. G. & Fidell, L. S. (2007). Using Multivariate Statistics. Upper Saddle River, New Jersey, Pearson Allyn & Bacon.
Tsang, P. M., Kwok, P., Choy, S. O., Ng, S. C., Tsang, M. J., Koong, K. & Wong, T. L. (2007). Design and implementation o NN5 for Hong Kong stock price forecasting. Engineering Applications and Artificial Intelligence, 20, 453-461.
Tsay, R. S. (2005). Analysis of Financial Time Series, 3rd ed. Wiley and Sons, New York.
Tsay, R. S. (2010). Analysis of financial time series, 3rd ed., New York, John Wiley & Sons.
Tuluca, S. A. & Zwick, B. (2001). The effects of the Asian crisis on global equity markets.Financial Review, 36(1), 125-142.
Valadkhani, A., Chancharat, S. & Harvie, C. (2008). A factor analysis of international portfolio diversification. Studies in Economics and Finance, 25(3), 165-174.
Wang, Y. F. (2003). Mining stock price using fuzzy rough set system. Expert Systems with applications, 24(1), 13-23.
Wichern, D. W. & Johnson R. A. (2007). Applied Multivariate Statistical Analysis. Upper Saddle River. Prentice Hall, Inc.
Wu, M. C., Lin, S. Y. & Lin, C. H. (2006). An effective application of decision tree to stock trading. Expert Systems with applications, 31(2), 270-274.
Xaba, D. L., Moroke, N. D., Arkaah,Y. J. & Pooe, C. (2016). Modeling South African major banks’ closing stock prices: A Markov-Switching Approach. Journal of Economic and Behavioral Studies, 8(1), 36-40.
Xin, S. (2007). The application of Factor Analysis in Chinese Stock Market. Thesis submitted to the College of science Tianjin Polytechnic University Tianjin (300160).
How to Cite
KHULA, Andile; MOROKE, Ntebogang Dinah. The Performance of Maximum Likelihood Factor Analysis on South African Stock Price Performance. Journal of Economics and Behavioral Studies, [S.l.], v. 8, n. 6, p. 40-51, jan. 2017. ISSN 2220-6140. Available at: <>. Date accessed: 24 nov. 2017.
Research Paper