IDEAS home Printed from https://ideas.repec.org/a/eme/jrfpps/jrf-09-2019-0186.html
   My bibliography  Save this article

An augmented macroeconomic linear factor model of South African industrial sector returns

Author

Listed:
  • Jan Jakub Szczygielski
  • Leon Brümmer
  • Hendrik Petrus Wolmarans

Abstract

Purpose - This study aims to investigate the impact of the macroeconomic environment on South African industrial sector returns. Design/methodology/approach - Using standardized coefficients derived from time-series factor models, the authors quantify the impact of macroeconomic influences on industrial sector returns. The authors analyze the structure of the resultant residual correlation matrices to establish the level of factor omission and apply a factor analytic augmentation to arrive at a specification that is free of omitted common factors. Findings - The authors find that global influences are the most important drivers of returns and that industrial sectors are highly integrated with the global economy. The authors show that specifications that comprise only macroeconomic factors and proxies for omitted factors in the form of residual market factors are likely to be underspecified. This study demonstrates that a factor analytic augmentation is an effective approach to ensuring an adequately specified model. Research limitations/implications - The findings have a number of implications that are of interest to investors, econometricians and researchers. While the study focusses on a single market, the South African stock market, as represented by the Johannesburg Stock Exchange (JSE), it is a highly developed and globally integrated market. In terms of market capitalization, it exceeds the Madrid Stock Exchange, the Taiwan Stock Exchange and the BM&F Bovespa. Yet, a limited number of studies investigate the macroeconomic drivers of the South African stock market. Practical implications - Investors should be aware that while the South African domestic environment, especially political risk, has an impact on returns, global influences are the greatest determinants of returns. No industrial sectors are insulated from global influences and this limits the potential for diversification. This study suggests an alternative set of macroeconomic factors that may be used in further analysis and asset pricing studies. From an econometric perspective, this study demonstrates the usefulness of a factor analytic augmentation as a solution to factor omission in models that use macroeconomic factors to proxy for systematic influences that describe asset prices. Originality/value - The contribution lies in providing insight into a large and well-developed yet understudied financial market, the South African stock market. This study considers a much broader set of macroeconomic factors than prior studies. A methodological contribution is made by estimating and interpreting standardized coefficients to discriminate between the impact of domestically and internationally driven factors. This study shows that should coefficients not be standardized, inferences relating to the relative importance of factors will differ. Finally, the authors unify an approach of using pre-specified factors with a factor analytic approach to address factor omission and to ensure a valid and readily interpretable specification.

Suggested Citation

  • Jan Jakub Szczygielski & Leon Brümmer & Hendrik Petrus Wolmarans, 2020. "An augmented macroeconomic linear factor model of South African industrial sector returns," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 21(5), pages 517-541, November.
  • Handle: RePEc:eme:jrfpps:jrf-09-2019-0186
    DOI: 10.1108/JRF-09-2019-0186
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/JRF-09-2019-0186/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://www.emerald.com/insight/content/doi/10.1108/JRF-09-2019-0186/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1108/JRF-09-2019-0186?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adeabah, David & Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar & Hammoudeh, Shawkat, 2023. "How far have we come and where should we go after 30+ years of research on Africa's emerging financial markets? A systematic review and a bibliometric network analysis," Emerging Markets Review, Elsevier, vol. 55(C).
    2. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2023. "Which COVID-19 information really impacts stock markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    3. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2022. "The impact and role of COVID-19 uncertainty: A global industry analysis," International Review of Financial Analysis, Elsevier, vol. 80(C).

    More about this item

    Keywords

    Macroeconomic factors; factor models; global economic conditions; return generating process; time-series; standardized coefficients; C01; C13; C32; C58; G12; G15;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eme:jrfpps:jrf-09-2019-0186. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Emerald Support (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.