Report: Analyzing the Impact of GDP on the Australian Stock Market

This report delves into the intricate relationship between Gross Domestic Product (GDP) and the stock market, specifically within the Australian context. It begins by defining GDP and the stock market, highlighting their interconnectedness. The literature review examines how GDP influences stock m...Read More

Impact to GDP on Stock Market/
Investment

ABSTRACT
That has reflected an attractive investment opportunities surplus in excess of capacity of Australia to fund
those through domestic savings. This extrinsic funding has been utilised to develop productive assets,
therefore supporting productivity and employment and as an outcome, the economy of Australia has
benefited. The long importing capital history means that Australian economy has net liability to rest of the
globe. In recent years, the net capital inflows have declined as well as even reversed.

Contents
ABSTRACT.....................................................................................................................................2
TITLE - “Impact of GDP on Stock market/ investment”................................................................4
INTRODUCTION...........................................................................................................................4
LITERATURE REVIEW................................................................................................................4
Impact of GDP on Stock market/ investment.........................................................................4
Research methods............................................................................................................................8
Autoregressive Methods.........................................................................................................8
Conclusion.......................................................................................................................................2
REFERENCES................................................................................................................................3

TITLE - “Impact of GDP on Stock market/ investment”
INTRODUCTION
Gross Domestic Product (GDP) is defined as the total market or monetary value of finished goods as well
as services produced within the borders of the nation in a particular period of time. It administers an economic
snapshot of nation, utilise to estimate economy size and growth rate. There are three ways by which gross
domestic product can be calculated, using incomes, production or expenditures. On the other hand, stock
market is the collection of markets and exchanges where consistent activities of issuance, buying and selling
of shares a publicly held organisations takes place (Paramati, Ummalla, & Apergis, (2016). These financial
activities are performed by over the counter market places or institutionalized formal exchanges that operate
under some defined set of regulations. It is a platform where investors connect in order to buy and sell
investments. Investment contributes to current demand of goods, therefore increase the domestic expenditure.
In order to measure gross domestic product each quarter, Australian Bureau of Statistics gathers data
from government agencies, organisations and households. Then, calculate GDP in three distinct ways, seeing
separately at data or information regarding income, expenditure and production. The gross domestic product
in context of production is referred as the total value added from products and services produced. In relation
to income, GDP is that total income generated through businesses and employees (Alda, (2017). And in
relation to expenditure, GDP is the total value of expenditure through governments, businesses and consumers
on final products and services. In Australia, economists and Australian Bureau of Statistics generally
emphasize on average of these three measures. In the terms of economy, investment is the spending through
households and businesses which maximize the capacity of economy to develop goods and services.
Gross domestic product highly impacts on stock market/ investment. When GDP increases, corporate
earning maximize, that makes it bullish for stocks/ equities. The inverse happens when GDP falls, it leads to
less spending by consumers and businesses, that drives the markets lower. But, whether it is bear market or
bull market, the equity or stock market has some influence albeit indirectly on gross domestic product as well
as the economy as a whole. The stock market influence GDP primarily through impacting consumer
confidence and financial conditions (Roe, (2018). When stocks are in increasing trend, there tends to be great
optimism deal surrounding an economy as well as the prospects of different stocks. If an organisation issues
new stock shares to raise capital, they can utilise those funds in order to expand their operations, hire more
workers and invest in new project. All these activities of organisation increase the gross domestic product of
country. This report is based on the title "Impact of GDP on Stock market/ investment". It intends to assess to
answer the study question about “How Gross Domestic Product impacts on stock market/ investments in
Australian market?”

LITERATURE REVIEW
Impact of GDP on Stock market/ investment
The economy of Australia put in an exceptional performance in year 2019 - 2020, in spite of many
challenges. Even for the spread of covid-19 pandemic, the economy of Australia hit major headwinds. The
agriculture, resources and energy exports are kept growing in the nation. Fiscal stimulus equivalent to around
18% of gross domestic product assisted sustain businesses and households. Decisive actions assisted the
economy of Australia to outperform as per the Global comparisons. The gross domestic product of Australia
was 2.4 percent low in year 2020 then in year 2019. In financial terms, the nation remains rock solid and the
debt ratio of public sector will be just 49 percent of gross domestic product by the end of year 2021 (Demir,
(2019). As per the article published by Reserve Bank of Australia, each element of aggregate demand
including investment, exports, government spending, consumption, imports etc. contribute to the growth of
gross domestic product (How is GDP Measured? 2021). The size of growth contribution is determined
through size of element as well as its growth rate. If consumption is more than half of gross domestic product
and tends to grow steadily, it makes high contribution to the growth of GDP. Mining investment is an
example that accounts for small share of gross domestic product, however has recorded large swings over past
decades related to resources boom as well as its slow down. Constrained-coefficient systems for perspectives
of 12 to thirty-six days or weeks, and a Variance decomposition to classify variables that affect FX effect in
the short assessing current the long term.
That used a Frenkel-Bilson methods of providing model, a Dornbusch-Frankel moist financial model,
and a Hooper-Morton sticky-price resource prototype to conduct rolling correlation projections for this out of
test result, they discovered that such systems start to always have increased dominance over the moving
average framework with time periods larger beyond 12 months. Given the nature of the foreign exchange
market, this seems wise to investigate this problem utilizing intraday data. Since the foreign exchange market
is extremely liquid, with trades taking place 24 hours a day, and because we now live in a digital age in which
even retail consumers have almost unrestricted access to information including data, this author thought it
would be worthwhile to revisit these issues from a western perspective. Intraday data studies usually
concentrate on a particular event or variable, including such financial regulation.
As per the article published by reserve Bank of Australia in year 2019, the economy of Australia is
closely integrated with global capital markets and this integration provides benefit to economy. Australians
are free to invest and borrow in financial assets abroad. Since the economy of nation was opened, the capital
markets globally have grown substantially. It has long raised funding abroad and the economy of the nation
has been net importer of financial capital for its modern history. In year 2019, net capital outflows have been
there for the very first time since 1970 - i.e., current account surplus (Tsaurai, (2018). This shift reflects end
of mining investment boom and reduction in net borrowing through government sector. Mining boom saw
higher investment level which drew upon maximized funding from abroad. As investment has decreased as a

share of gross domestic product in recent years, the net need of economy for extrinsic capital has also fallen.
This resulted into decline in the net foreign liabilities to the lowest level since early 2000s.
Figure 1: Saving and Investment percent of GDP, Australia, 2019
(Source: Australia's Integration with Global Capital Markets, 2019)
Whereas, the net capital inflow has decreased markedly since end of mining boom, Australian economy
has consistently to become much integrated with financial system at global level by growing stock of gross
foreign liabilities and assets. The gross liabilities over the past decade that Australian people owe to rest of the
world as well as growth asset which they own abroad have increased by around 50 percent points of gross
domestic product (How Do Global Financial Conditions Affect Australia? 2019). There are numerous reasons
for which this integration is beneficial. It has been reflected from the foreign liabilities that Australians
trapping into deep International capital pool in order to acquire funding at favourable rates. For instance: the
banks in Australia find it advantageous to raise a debt portion in offshore markets to access wider investor
base as compared to investors available at domestic nation. At the similar time, the investors of Australia,
involving growing and large superannuation sector of nation, can purchase foreign assets in order to diversify
their portfolio.
The financial linkages of Australia remain strongest with the advanced economies. The other advance
economics have more than 90% of foreign liabilities of Australia and a same share of foreign assets. In turn,
the direct financial links of Australia with emerging nations remain modest, despite having grown.

Figure 2: Financial and Trade Linkages of Australia, 2019
(Source: Financial and Trade Linkages of Australia, 2019)
In context of China, capital has flowed freely across the borders of China over past decade, at times
make up material share of gross and net capital flows to nation. However, stock of these investments accounts
for 2 percent of foreign investment in the nation, i. e., Australia and around 5% involving investment from
Hong Kong. It stands in contrast to trade relationships of nation, which are proximally tied to emerging
economics. While, emerging nations make up growing and large share of global output, they are less
integrated with capital markets globally than advance economics (Jareño, & Negrut, (2016). The emerging
nations are more possibly to have maintained constraints on flow of capital as well as are often considered to
present higher risk to foreign investors. As per a study, standard valuation model posits three variations
sources in stock returns - shocks to discount rates; shocks to expected cash flow; and predictable return
variation because of variation through time in discount rate which price expected flows of cash. It has been
found that the variables which are proxy for expected as well as expected return shocks captured around 30%
of variance of early NYSE value weighted returns. Stock market Is a sentiment indicator as well as can
influence gross domestic product. As stock market falls and rises, so too, happen sentiment in the economy.
As the sentiment alters, so does the spending of individuals that ultimately drives growth in gross
domestic product. However, the impact of stock market on gross domestic product can be positive and
negative both. The author discovered that the circumstances below which marginal and actual rates obey
normal distribution are very strict, yet figures have been unable to differentiate between the two options,
implying that although the conditional variance hypothesis cannot be dismissed, this should not be given
significant weight. Information for the US dollar and the British pound in a concurrent, reasonable empirical
models with practical partnerships for short or long speculators, brief & medium hedge funds, and exchange
rates formulas. They discovered that this method outperforms normal distribution in out-of-sample
forecasting; however, one apparent drawback of this model though is that it uses one underlying asset for
higher results. Ignoring the fact that somehow this research did not produce that FX rate consistency

surpassed normal distribution, it was a significant step forward in assessing FX rate predictive ability through
equity market data, as well as the authors point out using implied volatility data helps researchers toward
more precisely monitor for causal inference and variables that affect rates. Based on order flow details and the
online sales of the US sector, their analysis reveals predictable trends in implied volatility data. They agree
that research based on intraday information are starting to challenge long-held beliefs.
Research methods
Data collection
The information used was long term average gains on six marginal currency pair currency fluctuations
from November 1, 2018 towards October 31, 2019. By each nation's currency, the simulation is validated
twice: and used monthly data (modifications in daily closing), and then again through implied volatility data
during specific prices during the market period. Both currency are dependent on the Australian dollar for the
time span, as well as the 6 financial instruments are divided into two groups: set A as well as set B. The US
dollar (USD), Japanese yen (JPY), as well as Chinese yuan (CNY) make up Set A. (CNY). For such
2018/2019 financial year, these 3 economies mark Australia's top trading partners (both inflows and outflows)
(Department of Foreign Affairs and Trade, 2019). In respect of incoming visitors to Australia, such
3 countries are also important (Tourism Australia, 2019). The South Africa Rand (ZAR), Philippine Peso
(PHP), as well as Swiss Franc (CHF) make up Set B. (CHF). These nations account for less that 1% of either
the AUD Exchange Measure (RBA, n.d.). In addition, the regions are not major tourism collaborators for
Australian visitors, both incoming and outgoing. The comparison between such three separate was made to
account for the disparities in production and consumption, as well as liquidity ratios.
Autoregressive Methods
An autoregressive method of data analysis with one period has been used to search for an association
between values at points 1 and 2 – 1. In particular, Rit = + iRit–1 +, where Rit represents the difference in the
currency rate I at time t. I is indeed the approximate slope function for currency rate I which is a uniformly
distributed random measurement error with such a mean of 0. Rit–1 represents the one-period tends to lag
transition of currency exchange i. (i.i.d.). The AR (1) model has a drift.
And seeing if a clear gap for transmitting information can be found, a two-period lag vector auto regression
method was used. The autoregressive process is a form of regression that occurs when a variable is i1Rit =
i1Rit = i1Rit = i1Rit = i1Rit = Rit–1 + i2 = Rit–1 + i2 = Rit–1 + i2 = Rit–1 + The difference in the currency
rate I at period interval is represented by Rit–2 +, where Rit denotes the impact of the exchange rate i. The
adjustment in the currency rate I at and one 2 records, however, is denoted by Rit–1 and Rit–2. i1 and i2 are
also the market price i's curve to determine coefficient values premised on and 1 or 2 tends to lag,
respectively, as well as being a binomial random random error with such a normal distribution of 0. (i.i.d.).
The AR(1) model has a drift. The findings using regular data are consistent with the pioneering work, which
found no statistically relevant figures that better describe or illustrate foreign currency trends than normal
distribution.

Whenever the system is run with 30-minute information, though, there seems to be proof inside the
model that it can predict on a quite short time horizon (intraday). The large number of studies, especially
those that use quantitative dynamics as forecast factors, rely on less periodic results, which may be as long as
this year. Given that features of the foreign currency industry, this seems to be unacceptable. Given the vast
number of variables that influence prices, including reported and forecast economic indicators, political risks,
commerce and investment, and also transportation and inventory market forces, it is clear that no single model
would ever collect sufficient information on these variables. The findings in this paper are based on the
dissemination of knowledge rather than the data themselves. The fact that all 6 combinations loosely modeled
have substantially negative correlations means there is an exaggeration to facts, but that this outburst is
corrected within thirty minutes. Medium algorithm outperforms all macro as well as spontaneous walk
systems, according to Evans & Lyon (2005), so they can use market data recorded to provide non-public
knowledge that affects market-maker transactions, but now they have included everyday data. More research
into combining the suggested micro models through implied volatility data may provide analysis and
forecasting advantages including high; for example, developing a model that focuses on responses to
knowledge rather than just the knowledge itself can provide forecasting capability without needing investors
to buy additional data (such as order flows) or a big data can help that considered several macroeconomic
variables.
Table showing the data collected regarding the exchange rate 30-min and daily intervals
Set
A Set
B
JPN US
D CN
Y ZA
R PHP CH
F
30-min int
n 12 169 12 390 7 336 12 230 4201 12 137
Const -0.00001 -0.000003 -0.000003 -0.000001 -0.00002 -0.000004
𝛽
i
-0.02383*** -0.01828** -0.08451*** -0.06196*** -0.08324*** -0.02986***
R² 0.00056 0.00033 0.00714 0.00384 0.00693 0.00089
F-test 6.91045 4.14213 52.75171 47.12127 29.2965 10.83050
Daily int
n 259 259 259 259 259 259
Const -0.00034 -0.00016 -0.00010 0.00005 -0.00036 -0.00024
𝛽
i
0.00629 0.00648 -0.03170 0.05794 -0.54444 -0.04728
R² 0.00004 0.00004 0.00101 0.00338 0.00301 0.00223
F-test 0.01009 0.01078 0.25910 0.86996 0.77576 0.57405
Notes: *, **, *** denotes the coefficient is statistically significant at the 90%, 95% and 99%
level respectively.
Table 2: AR (2) Tests on exchange rates in 30-min and daily intervals
Set
A Set
B
JPN US
D CN
Y ZA
R PHP CH
F
30-min int
n 12 169 12 390 7 336 12 230 4201 12 137
Const -0.00001 -0.000003 -0.000003 -0.000001 -0.00002 -0.000004
𝛽i1 -0.23896*** -0.01840** -0.08392*** -0.06258*** -0.08141*** -0.03017***

𝛽i2 -0.00297 -0.00664 0.00700 -0.01001 0.02195 -0.01041
R² 0.00058 0.00038 0.00719 0.00394 0.00741 0.00100
F-test 3.50858** 2.34428* 26.55295**
* 24.1737*** 15.6640*** 6.07342***
Daily int
n 259 259 259 259 259 259
Const -0.00034 -0.00017 -0.00009 0.00004 -0.00035 -0.00023
𝛽i1 0.00596 0.00655 -0.03139 0.06048 -0.05275 -0.04551
𝛽i2 0.01870 -0.37786 0.00829 -0.04944 0.03796 0.03789
R² 0.00040 0.00156 0.00108 0.00564 0.00448 0.00369
F-test 0.05077 0.19936 0.13799 0.72582 0.57581 0.47427
Notes: *, **, *** denotes the coefficient is statistically significant at the 90%, 95% and 99%
level respectively.

Table 3: Summary Statistics – 30-minute data
Summary Statistics – 30-
minute
JPN USD CNY ZAR PHP CHF
Mean -
0.0006%
-
0.0003%
-
0.0003%
-
0.0001%
-
0.0018%
-
0.0004%
Standard Error 0.000009 0.000007 0.000010 0.000011 0.000017 0.000007
Standard Deviation 0.0956% 0.0727% 0.0830% 0.1199% 0.1127% 0.0783%
Sample Variance 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001
Range 5.1523% 2.5807% 1.7857% 2.7609% 2.4382% 2.8948%
Minimum Change -
3.3588%
-
1.5316%
-
0.8721%
-
1.2385%
-
1.3145%
-
1.7384%
Maximum Change 1.7935% 1.0491% 0.9135% 1.5224% 1.1237% 1.1564%
Kurtosis 151.0923 35.5032 19.5066 9.7752 21.7167 36.2504
Skewness -2.9967 -0.5067 -0.0629 0.3660 -0.0127 -0.8380
n 12171 12392 7338 12232 4203 12139
Table 4: Summary Statistics – Daily data
Summary Statistics - Daily JPN USD CNY ZAR PHP CHF
Mean -
0.0269
%
-
0.0098%
-
0.0076%
-
0.0016%
-
0.0289%
-
0.0183%
Standard Error 0.0003
60
0.000291 0.000260 0.000431 0.000261 0.000311
Standard Deviation 0.5831
%
0.4697% 0.4196% 0.6936% 0.4219% 0.5025%
Sample Variance 0.0000
3
0.00002 0.00002 0.00005 0.00002 0.00003
Range 4.2503
%
3.6623% 2.7914% 5.3173% 3.0631% 3.1326%
Minimum Change -
1.9736
%
-
1.7993%
-
1.4541%
-
2.1848%
-
1.6058%
-
1.5539%
Maximum Change 2.2767
%
1.8629% 1.3373% 3.1325% 1.4574% 1.5787%
Kurtosis 1.7287 1.417 0.8080 1.6313 1.1632 1.0623
Skewness 0.0100 0.0966 -0.1073 0.3830 0.0587 -0.0597
n 261 261 261 261 261 261
Forecasting methods
In-sample predictions were done after finding a statistically relevant throughout the 30-minute results
at one-lag. ∆𝑅it = 𝛼 + 𝛽i ∆𝑅it–1 + 𝜀 with ∆𝑅it The difference in the rate of exchange I around time
interval is represented by Rit–1 +, where Rit denotes that impact of the exchange rate i. One such lagged shift
in exchange rate is denoted by Rit–1. i.i is the curve constants calculated from 2000 data sets for gold price I
which is a naturally random value measurement error with such a normal distribution of 0. (i.i.d.).
Forecasts were developed from data set 2001 through most of the conclusion within each analysis of 30-
minute information using only a rolled regression to calculate the interrupt and gradient factor from the
preceding 2000 data points.

Table 5: Summary of Forecasts
Currency n Sum of Errors
Set A
JPN 10 169 7.39171%
USD 10 169 3.46014%
CNY 5 336 4.41912%
Set B
ZAR 10 169 3.32987%
PHP 2 201 1.04981%
CHF 10 137 3.98133%

Table 6: Summary Statistics – Forecasts
Forecast
Summary
JPN US
D
CN
Y
ZA
R
PHP CH
F
Mean -0.001118% -0.000601% -0.000538% -0.000101% -0.002560% -0.000618%
Median -0.001069% -0.000676% -0.000760% -0.000241% -0.002639% -0.000788%
Standard Deviation 0.005353% 0.002514% 0.008782% 0.008223% 0.008853% 0.003496%
Range 0.404133% 0.070211% 0.235980% 0.247033% 0.224039% 0.083665%
Minimum Forecast -0.310041% -0.043282% -0.138671% -0.120722% -0.146486% -0.034083%
Maximum
Forecast
0.094092% 0.002693% 0.097309% -1.026256% 0.077553% 0.049582%
The number of explanatory variables for all 6 currency is positive. These findings, when
paired with AR (1slightly)'s negative correlations and AR (2one-lag,)'s suggest that implied
volatility data could be used to predict shifts and generate additional revenues. Mean reversion
investing is one example of such a tactic. Using moving averages rotating averages, buying when
prices were below minimum and selling when levels are above average may be a lucrative tactic.
The autoregressive models have negative correlation, implying a real economy outburst with
adjustment, which is consistent with previous research. This, along with significant sum or
prediction errors, indicates that the data somehow doesn't obey a random walk. The results of
this study may be used to create a statistical template or trading approach by additional
simulations and experiments. The discrepancies between regular and intraday data may be
representative of trends overlooked by certain models due to low statistical features. This study's
findings reveal a strong correlation between implied volatility data and data collected less often.
Sequence flow data is used in other more improvement for brief prediction, but this analysis is
tough to obtain by and also requires the author to buy it. Every day, the Exchange rate receives a
massive amount of material, both internal and external (order flows). By its own essence, this
knowledge will still be a "bit surprising" towards the market. A template that relies on
transmitting information instead of data capture can provide a simpler, more straightforward
approach to finding lucrative trades. There are some drawbacks to the techniques used here. The
regular data obtained from Bloomberg represent final rates; it really isn't possible to ensure that
somehow this information is gathered at the very same time each day for every monetary system;
in other words, it's not really instantaneous. Since there has been proof of time-of-day trends
depending on the foundation country trade hours, this might contribute in any variability.
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Understanding exchange rate fluctuations is critical not just for analysts, but also for
governments, consumers, companies, and individuals. Exchange rate fluctuations have an effect
on international trade and capital flows, and also financial regulation. Since financial markets are
critical to global financial stability, there seems to be a lot of studies done and try to understand
and forecast exchange rate fluctuations. Businesses would have been able to decrease their
foreign exchange costs, governments would have been able to control monetary policies, and
investors would have been able to be using predictive models to find lucrative trading
opportunities. Businesses could become capable of reducing their foreign exchange costs,
policymakers would indeed be able to control monetary policies, and traders would be willing to
use prediction model to find lucrative market opportunities if they could forecast currency
fluctuations. FX prices are dictated throughout the shorter term by production and consumption
movements, according to international economic indicators, and rates can move throughout the
longer term to normalise spending power throughout currencies.
Conclusion
In the conclusion, it is stated that this study's findings reveal a strong correlation between
implied volatility data and data collected less often. Sequence flow data is used in other more
improvement for brief prediction, but this analysis is tough to obtain by and also requires the
author to buy it. Every day, the Exchange rate receives a massive amount of material, both
internal and external (order flows). By its own essence, this knowledge will still be a "bit
surprising" towards the market. A template that relies on transmitting information instead of data
capture can provide a simpler, more straightforward approach to finding lucrative trades. This
study's findings indicate that moving averages data provides detail that is not collected by
simulations that use less regular data. The currency market, which trades 24 hours per day and, is
the world's largest and most competitive market. Daily data, let alone weekly or monthly data
aligned with economic and financial announcements cannot accurately describe the massive
number of exchanges and knowledge coming into the market. Investor sentiment can be used to
identify sound investments, which would be an area that needs more research and investigation.
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