Using macroeconomic variables in the prediction of stock market indices: A theoretical and empirical assessment within BRICS and selected developed economies

PhD Thesis

Ouattara, B S (2018). Using macroeconomic variables in the prediction of stock market indices: A theoretical and empirical assessment within BRICS and selected developed economies. PhD Thesis London South Bank University School of Business
AuthorsOuattara, B S
TypePhD Thesis

The prediction of stock market indices and issues/questions associated with such predictions, have been a
challenge for several academics, business analysts and financial researchers for many years. In the main, these
challenges have been addressed within developed economies; statistically using appropriately determined
macroeconomic independent variables. However, much less attention has been directed to the use of such variables
within developing economies. This sparse attention forms the research background (Chapter I) and provides
partial justification for the research itself. Thus, the research comparatively focuses on both, certain developing
and selected developed economies. The precise context of the research considers/compares the impact and
potential/possible relationships of meaningfully selected macroeconomic variables, upon respective stock market
indices of two sets of economies - BRICS (i.e. Brazil, Russia, India, China and South Africa) and five
meaningfully selected developed economies (i.e. France, Germany, Japan, UK and US). Thus, a significant
motivation for the research is to evaluate/test theoretical linkages and empirical relationships of selected
macroeconomic variables, in terms of their predictive power vis a vis related stock market indices. The research
then offers consequent policy implications/contributions. It is of benefit and significance to (inter alia) investors,
who would welcome “early signals” when evaluating stock markets via relevant indices. In so doing, the research
adds theoretical and empirical knowledge, with practical potential, to this domain. Finally, within its concluding
chapter, the thesis also offers some suggestions for further research and future researchers.
Against the above background, the research addresses ten individual, but related, objectives (Chapter II). These
objectives range from an attempt to identify the directional and potentially causal relationship between sets of
selected macroeconomic variables and relevant stock market indices (Objective 3), through to determining
dynamic relationships across sets of comparable indices (Objective 10).
The literature review (Chapter III) confirms the relative absence of relevant empirical literature within
developing countries. However, related literature within developed economies does prevail. For instance, in terms
of the U.S., Domian and Louton (1997) find evidence that stock price declines (and so of market indices) are
associated with abrupt decreases in growth rates of industrial production and increases are comparably associated
with mild increases in industrial production. Equally, in terms of Germany, France, United Kingdom, Sweden,
Japan, Canada and United States, Longin and Solnik (1995) provide evidence in terms of the predictive power
of macroeconomic variables related to stock prices (and by implication indices). Accordingly, the extant research
literature reveals a gap. There appears to be no study that comparatively analyses the effects of the 2007-8 financial
crisis between the BRICS and the five developed countries, selected for this analysis. Equally, in contrast to the
present research, there appears to be no study that (as “dummy” variables) tests the effect of the US quantitative
easing policy undertaken during the financial crisis, on the financial markets of BRICS and the five selected
developed countries. And, therein lies some of the uniqueness and original contribution of this research.
Saunders et al. (2016) who consider the construction of research with the six “layers” of their “Research Onion”
influence the research design and methodology (Chapter IV). Thus, with explanations provided within the
thesis, the research engages with five of these “layers” as follows: philosophy - positivist, approach - deductive,
strategy - archival, choice of method - quantitative – but with qualitative elements. The research time-horizon is
longitudinal, with, respectively, the same dependent (identified stock market indices) and independent (selected
macroeconomic variables) research variables being considered and analysed over a significant period of time
(January 2000 to December 2015). Thus, the research data are mainly stock market indices (dependent variables)
and meaningfully identified macroeconomic features (independent variables - derived from a Keran diagram),
over the research period. Equally, appropriately developed variables, intended to quantitatively capture the 2008
financial crisis and the US quantitative easing are also used as dummy variables within the independent variable
data set. The research data itself and its analysis, and the dependent and independent variables are identified and
rationalised within the thesis. And, in this context, the research draws on, and analyses, pre-existing quantitative
data stored (mainly) in the Bloomberg repository - a public database. This public accessibility obviates ethical
issues relating to the access, use and storage of the research data.
The research mathematical/statistical procedures and analyses (Chapter V), mainly computed descriptive and
inferential statistics, are developed and presented within the research, Firstly, in order to condition and/or quality
control variables, appropriate pre-statistical operations (including Units Roots Tests, Correlations, Seasonal
Adjustments and Log Transformations) are duly performed on quantitative data. Then, descriptive statistics
(including mean, mode, median and standard deviation) are developed (primarily) in order to reveal and describe
properties of the variables attached to the cases, and to be assured that the inferential statistical tests to be applied
to them are, indeed, appropriate. Finally, appropriate inferential statistics are applied and determined as
necessitated by individual and particular research objectives.

PublisherLondon South Bank University
Digital Object Identifier (DOI)
Publication dates
Print01 Sep 2018
Publication process dates
Deposited17 Dec 2018
Publisher's version
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