sum up:

This thesis focuses on the relationship between macroeconomic variables and the
stock market. The study has been delimited to the U.S. market due to the extensive
and reliable datapoints but also due to the share size of the market. To get a better
understanding of the relationship is something that’s important for various
stakeholders such as investors, regulators, and firm management. The idea of better
understanding the relationship could lead to increased forecasting abilities of both
macroeconomic variables and the stock market which could create value in different
aspects of the stakeholder’s view.
In the literature review section, prior findings and methodologies revolving the
research question was presented. The relationship between macroeconomic variables
and the stock market don’t have a common conclusion due to the researchers
presenting conflicting findings and theories. This thesis contributes to the research
question with its dual model usage, focus on the U.S. market with an extensive data
sample both in regard to time but also different asset classes.
The empirical analysis begins with data collection of large U.S. asset classes as well
as expectations of U.S. real GDP and inflation. Different statistical tests are performed
to emphasis a reliable statistical model. The data is then run through both an OLS-
regression and a Granger causality model, in order to answer the thesis question.
6.1 OLS model
The results show that changes in CPI expectations had a significant negative effect on
the returns of Dow Jones Industrial Average, Nasdaq 100, S&P500 and on Gold. This
means that an increase in changes in CPI expectations negatively affect the asset
classes. The result is consistent with the findings of Alzoubi (2022), Camilleri,
Sciclunaa and Bai (2019), and Olokoyo, Ibhagui & Babajide (2020), while the
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significant relationship without specifying the coefficient is consistent with Jareno,
Ferrer and Miroslavova (2016), Hong, O’Brien, and Ryan (2014), Fang, Lin &
Parbhoo (2008), Geetha, Mohidin, Chandran & Chong (2011), Lokeswar Reddy
(2012), Zhang (2021), and Chen (2008).
The negative effect of CPI on asset classes can further be explained by the theory of
Fama (1980) who argues that inflation is negatively correlated with stock market
returns. This is because the market is worried about the effect inflation has on real
economic activity. If inflation becomes too high this will affect the stock market
negatively.
Alzoubi (2022) argues that the relationship can be explained through the relationship
between inflation and interest rates. Inflation and interest rates are often correlated and
since interest rates are the prices for borrowing money, investments are becoming
more expensive. Fama (1990) further argues that an increase in interest rates will
increase the discount rate for firm cash flows and cause a decline in stock prices.
Moreover, rising inflation and interest rates discourage future investments, spending,
and raise the cost of capital, which affect stock prices negatively, argues Alzoubi.
Camilleri, Sciclunaa and Bai (2019) explain that inflation negatively affects the real
value of financial assets, impacts businesses profitability and is likely to alter the
demand for stocks since they are usually a sought-out hedge for inflation.
Furthermore, inflation impacts business profitability through competitive changes on
the international markets.
Olokoyo, Ibhagui & Babajide (2020) showed that inflation is cointegrated with stock
market performance and that inflation had a negative effect on the stock market.
Inflation having a negative impact on the stock market is supported by Fama
(1981), who argues that a negative relationship arises because of the lack of a
substantial variation of the nominal quantity of money with respect to real activity.
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6.2 Granger causality model
The results show that that real GDP expectations Granger causes CPI expectations and
that returns in DJI, S&P500, NDQ and NYSE composite Granger causes real GDP
expectations. We can’t see that either real GDP expectations or CPI expectations
Granger causes returns in the stock indices. The Granger causality model show that
stock market data leads real GDP expectations and show no signs of macroeconomic
variables leading the stock market. The result is consistent with the findings of
Pradhan (2018), Zalgiryte, Guzavicius and Tamulis (2014), Andersson & D ́ Agostino
(2008), Schwert (1990), Swiston (2008), Stock & Watson (2003), Cole, Moshirian &
Wu (2008), Hassapis & Kalyvitis (2002), Şendeniz-Yüncü, Akdeniz & Aydoğan
(2018), and Nurudeen (2009), but inconsistent with the prior research mentioned in
the OLS model.
The results of the stock indices Granger causing real GDP expectations can be
explained by Rangvid (2021). Rangvid argues that the stock market is forward looking
and that signs of a decrease in business activity will be anticipated and therefore the
stock market will start to decline before real evidence can be seen in the business
activity. This indicates that the market quickly prices in expectations and moves
before the data is realized.
6.3 Summary of conclusion
The thesis research question “How is the relationship between changes in inflation
expectations, changes in real GDP expectations and stock market returns?”, can be
answered by the following paragraph.
The conclusion is that CPI expectations are negatively impacting the stock market
(OLS) and that the stock market lead expectations of real GDP (Granger). With
different findings and including the prior research, the relationship is a complex one
and differs depending on data sample, geographical area, statistical model and more.
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6.4 Model differences
What can explain the differences in results between the two models?
The main difference between the models is that Granger causality uses lags in order to
predict the dependent variable, in this thesis the lag was chosen to be one. Granger
causality is used to investigate a possible causation using lagged values of the
independent variable to predict the dependent variable. In this thesis the Granger
causality model provides more results and more causation results than the OLS model
since it conducts more tests due to the switching positions of the variables being
dependent or independent.
The OLS model doesn’t use lagged values. The OLS model fits a line between the
datapoints that minimizes the sum of the squared error terms. If correct independent
variables have been chosen, then the dependent variable can be significantly explained
by the independent variables and an error term.
Due to the common usage of both models in prior research such as the research
provided by Camilleri, Sciclunaa and Bai (2019), Chen (2008), Pradhan (2018), and
Alzoubi (2022), it was relevant to include both models to get a more nuanced result
and conclusion.
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7. Perspectives
In this chapter, the conclusion will be further discussed together with other
perspectives while also discussing possible errors and room for further research.
According to the conclusion, we can see that CPI expectations are negatively
impacting the stock market returns (OLS) and that the stock market returns lead
expectations of real GDP (Granger). With different findings and including the prior
research, the relationship is a complex one and not fully agreed upon or understood.
Pradhan (2018) also tested the relationship through a Granger causality model.
Pradhan found a conjoined back-and-forth relationship between these two variables.
On some occasions, per capital economic growth leads to development of the stock
market. On other occasions, it is the development of the stock market that determines
the level of per capital economic growth. The relationship is therefore dependent on
the sample time period data and is varying. Pradhan also found cases where both
factors were independent of each other. It is the conjoined back-and-forth relationship
between these two variables that is the highlight of this study and guides the future
research on this topic.
Pradhan’s result can serve as a basis for this research and a conclusion based on the
prior research. Evidence is found in both directions and no clear evidence can present
the relationship in another way than to say that reasons for both macroeconomic
variables and the stock market to lead each other can be found.
The result difference with prior research in the literature review section can also be
attributed to using other models, time periods, data, geographical areas and data
transformation. It could also be that the characteristics of the relationship changes
from time to time as Pradhan suggests, making a general conclusion of the
relationship hard to present. This can also explain why the findings in the literature
review differ.
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A common theme in this research is that it’s all about expectations. From our own
models we see that it is the expectations that drives change and reasons for that has
been presented by researchers such as Fama. The different pricing formulas show that
assets are being priced through future expectations. These expectations are then
altered based on the investors view on the future economic state which is constantly
changing. To work with expectations is hard due to the constant change and because
people’s expectations can be impacted on what their expectations of other people’s
expectations are.