Assessment Answers | Quantitative Research Design: FIN6C4

Research Design
Of the three research designs –qualitative, quantitative and mixed-method design, this
study used quantitative research. Ther …

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Research Design
Of the three research designs –qualitative, quantitative and mixed-method design, this
study used quantitative research. Therefore, numerical data was used in the analysis to provide
insights that helped in achieving the objective of the research (Bloomfield and Fisher 27-30).
Also, the research employed deductive reasoning research design whereby general ideas were
narrowed down to specific ideas about the research objectives in order to make conclusions
(Alam et al. 175). The quantitative research design was appropriate because through Excel
analysis, the numerical data provided useful insights that are easily interpreted to gain an
understanding of how UK stock markets affects UK real estate /house prices index.
Collection of Data
Major sources of data for most researches are primary and secondary sources. Primary
sources of data collection enable collection of data for the first time hence provides first
information to the researcher (Proenca et al. 256-263). Some primary sources of data are
interviews, experiments, observations, focus groups, surveys (Proenca et al. 256-263). Secondary
sources of data provide aready-made data whereby aresearcher collects data that has already
been collected to be used in research (Johnston 619-626). Some sources of secondary data are
internet, websites, journals, newspapers, published books, government records (Johnston 619-
626). This paper used secondary data sources from Fred Economics St. Louis Federation website.
Secondary data was appropriate because ithelped in saving time and costs of data collection.
Our largest database for the real estate Index (REI), which was used in this study from
January 1990 to January 2016. The data source is U.K Central Bank. 7QE-20 index represents
the United Kingdom stock exchange market performance, which measures twenty and the most
liquid share listed in the U. K stock exchange. The UKE-20 index and macroeconomic index
factor were collected from the data stream. We also utilized the historical data for the credit
policies for collateral and lending interest rates by the banks and other institutions throughout the
same times.
Variables
The data set consisted of five variables namely year, real estate index, stock market index,
wealth index and credit index. The year represented time period from 1990 to 2016, the real
estate index described the UK house price index, stock market index indicated the total share
prices index for all stocks in UK, the wealth index described the CPI of residents in UK and
credit index represented the corporate borrowing rate on loans from UK Banks.
Data Preparation
This involved pre-processing processes performed on the data set to make itready and
suitable for analysis. For instance, itentails data cleaning processes to remove missing
observations, NA entries or redundancy (Alasadi and Bhaya 4102-4107). The data set in this
research ranged from 1990 to 2016 atotal of 25 years. The lengths of the data sets were not equal
because bank credit index was quarterly, share price index was monthly, real estate index /house
price index was annually and wealth index was monthly. Annual averages were performed on
the monthly and quarterly entries in order to have auniform data length for an effective analysis.
To convert the quarterly and monthly data sets into annual data set, pivot function was used in
Excel.
Models used to analyze the data
Before performing predictive analytics which involved running the models, descriptive
statistics were performed first. Here, the study performed descriptive summary analysis of the
data sets collected in order to gain overall or summary understanding of the data sets (Kaur et al.
60). Some of descriptive statistics measures are mean, mode, median, standard deviation,
variance, skewness and this case study, we considered mean and standard deviation measures. In
addition, under descriptive analytics we performed exploratory analysis to visually explore and
represent out data sets (Kaur et al. 60). For exploratory analysis, line graphs were used and this
helped in determining the trends of real estate index, stock market index, wealth index and credit
index (Kaur et al. 60). Scatter plots were also used to determine if outliers were present in the
data sets.
Though correlation analysis is not apredictive analytic, itis an important step to perform
before doing the real predictive analytics. Correlation measures the degree of association
between two variables (Schober et al. 1763-1768). It also helps in selecting variables to be
included in the model hence itis avariable selection process (Schober et al. 1763-1768). If two
independent variables are strongly correlated, only one variable is included in the model to avoid
multicollinearity effect (Daoud 012009). Correlation analysis was performed to determine the
relationship among the variables real estate index, stock market index, wealth index and credit
index.
For predictive analytics, we used both asimple linear regression model and amulti
variable regression model in order to analyze and understand the data. Regression analysis is a
set of statistical methods used for the estimation of relationships between adependent variable
and one or more independent variables (Sarstedt and Mooi 209-256). It can be utilized to assess
the strength of the relationship between variables and for modeling the future relationship
between them (Finance, Corporate 2021). This is abasic linear regression model;
y= a+bx +É›
where
y= Real estate prices
a= intercept
x= stock prices
b= coefficient of x
É›=error term
The research also used multiple linear regression model to further analyze other factors of the
economy.
y= a+b1+c2+ 3+É›
where
y= Real estate prices
a= intercept
1=stock prices, 2=lending/collateral rates and 3=interest rates
b= coefficient of 1,c= coefficient of 2and c= coefficient of 3
É›=error term
Diagnostic Check
To confirm for the accuracy and reliability of the regression model, the research used
Mean Absolute Percentage Error (MAPE) technique. The decision criterion was that aMAPE
value below zero or avalue approaching zero was agood indication that the model is accurate
(Myttenaere et al. 38-48).
MAPE =100%

|−
|
where At =Actual value and Ft =Forecasted value.
Difficulties
A great challenge was faced in finding the correct websites and sources to collect the data
sets. The most famous financial and economic data websites such as yahoo finance and kaggle
had no our data sets of interest. Also, collecting data from UK Central bank website was not
possible. To solve the solve the problem, we had to evaluate alternative terms or names for real
estate index, wealth effect index, stock market index and credit index. We came to understand
that real estate index is same to house price index, wealth effect can be measured by Consumer
Price Index (CPI), stock market index could be measured by market share prices for all stocks
and then credit index could be measured by corporate borrowing rate on loans from UK Banks.
After gaining athorough understanding of our data sets, itwas now easy to find these data sets
from Fred Economics St. Louis Federation website.
Ethic Consideration
The research did not involve human subjects hence no much ethical considerations were
required as stipulated in IRB approvals. It was asecondary data source research. However, still
ethics considerations had to be considered whereby the research used the collected data sets only
for the purposes of research and not for other ill or malicious intentions.
Results and Discussion
Descriptive Analytics
Descriptive Summary Analysis
Variable M SD
Real Estate Index 64.71 28.73
Stock Market Index 74.30 21.04
Wealth Index 78.79 13.39
Credit Index 6.68 3.36
Table 1: Descriptive summary statistics
From table 1, real estate index (M= 64.71, SD=28.73), stock market index (M= 74.30,
SD= 21.04), wealth index (M= 78.79, SD= 13.39) and credit index (M= 6.68, SD= 3.36). The
standard deviation of real estate index seems to be significantly large in relation to the mean
value and this is an indication that there is high fluctuation in the mean house prices or real estate
indices in UK. This high volatility in the real estate index becomes abarrier to investors because
itimplies the future house prices is highly uncertain hence the need for amodel to help in
predicting real estate indices in the future. The standard deviation for stock market index is also
significantly large in relation to the stock market value again indicating high rate of fluctuations
in the UK stock market. This is expected because as we know, stock market prices are always
highly volatile and not stable for most countries. This is because especially in the modern world
technology, stock prices are highly determined and affected by tweets from famous and rich
CEOs like Elon Musk, Jeff Bezos. They are also affected current trends and pandemics like
coronavirus. Therefore, itis rare for stock markets to be stable and hence they also require
modelling to help in prediction. The standard deviation for wealth index is not significantly large
and hence an indication that the CPI indices of UK economy is not volatile and does not
experience high fluctuation. This is an indication of agood and stable economy and itindicates a
good consumer spending. The stable CPI index indicates that economists can easily predict
consumer spending in UK. Lastly, the standard deviation for the credit index is significantly
large in relation to the mean value and this shows high rate of fluctuation in the credit index in
UK. The rate of borrowing in UK is not predictable and this might be due to the bank interest
rates. If bank interest rates are high, then itwill discourage investors and when the interests are
low itwill encourage investors. The credit index volatility is an indication that UK bank interest
rates are highly volatile which directly will affect investments.
Exploratory Analysis
The study used line graphs and scatter plot. Line graph was used to determine the trend
while scatter plot was used to determine if we have presence of outliers in our data set. Outliers
are abnormal or extreme values that deviates from the rest of group values of the data.
Fig 1a: Real estate index line graph
Fig 1b: Real estate index scatter plot
From fig 1a, the line graph shows an increasing trend in real estate index from 1990 to
2006 and this is economically healthy to investors. However, from 2007 to 2009 we see adecline
in real estate index and this is attributed to the 2007-2008 economic depression or financial crisis
(Reddy et al. 257-281). During this crisis, the housing industry or the real estate industry was
severely affected that the Federal Reserve Bank had come in and issue economic stimulus
packages (Reddy et al. 257-281). The line graph then shows an increasing trend from 2009 to
2016. The scatter plot in fig 1b indicates that there are no outliers in our data set.
Fig 2a: stock market index line graph
Fig 2b: Stock market index scatter plot
From fig 2a, we an increasing trend in stock prices from 1990 to 1999 after which we
start experiencing significant fluctuations in the stock market prices. This reflects the summary
statistics that stock market prices are highly volatile. Again, we see adecline in stock market
prices between 2007 and 2008 and as itis known, this is due to the 2007-2008 economic
depression (Reddy et al. 257-281). The scatter plot in fig 2b indicates that there is not presence
of outliers in our data set.
Fig 3a: Wealth Index Line graph
Fig 3b: Wealth Index Scatter plot
The wealth index plot in fig 3a shows aconsistent upward trend and this indicates that
consumer spending in UK has been increasing from 1990. The consumer spending in UK was
not even affected by the 2007-2008 economic crisis (Reddy et al. 257-281). Generally, this is a
sign of agood economy and we can this line plot result reflect the summary statistics result that
consumer spending in UK is not volatile. The scatter plot in fig 3b indicates that there is no
presence of outliers in our data set.
Fig 4a: Credit Index line graph
Fig 4b: Credit Index scatter plot
From fig 4a, itcan be seen that we have been having adecreasing trend in the credit
index. This implies adecreasing trend in corporate borrowing rate on loans and this is an
indication of agood economy because decreasing the borrowing rate on loans will encourage
investors to borrow more and make investments. Fig 4b indicates that there are no outliers in the
data set.
Correlation Analysis
It was used to determine the relationship among the variables. The correlation ranges
between -1 to 1indicating that two variables can be positively or negatively be related. A
correlation coefficient approaching 1or -1 shows astrong positive or strong negative
relationship respectively (Schober et al. 1763-1768). Correlation analysis was used to detect
presence of multicollinearity in our independent variables. The decision criterion was that two
independent variables will result in multicollinearity if they have astrong correlation coefficient
value of r> 0.7 or r> -0.7. In modelling, strong correlation between predictor variables is not
good because itinduces multicollinearity effect in the model distorting the results (Daoud
012009). On the other hand, strong correlation between predictor variables and response
variables is highly encouraged. The predictor variables in this case study were stock market
index, credit index and wealth index while the dependent variable was real estate index. The
figure below shows our correlation matrix in this case study.
Real Estate
Index
Stock Market
Index Credit Index
Wealth
Index
Real Estate Index 1
Stock Market
Index 0.715208103 1
Credit Index -0.770129946 -0.71687699 1
Wealth Index 0.924027905 0.794870319

0.891894104 1
Table 2: Correlation Matrix
From table 2, correlation between real estate and stock market index is (r= 0.72), real
estate index and credit index (r=-0.77), real estate index and wealth index (r=0.92), stock market
index and credit index (r=-0.72), stock market index and wealth index (r=0.79) and lastly
correlation between credit index and wealth index is (r=0.89). Real estate index is negatively
related to credit index indicating that adecrease in credit index will lead to an increase in real
estate index. On the other hand, real estate index is positively related to stock market index and
wealth index hence increase in one leads to an increase in the other. It can be seen that all of the
correlation coefficients are greater than 0.7 /-0.7 and this shows astrong correlation between the
variables. For the independent variables this will bring problems in our model due
multicollinearity effects distorting the results. However, the study assumed the strong correlation
among the predictor variables.
Regression Modeling
Simple Linear Regression
This is an analysis used to determine the effect of independent variable(s) on the
dependent variable. In this case study real estate index was the dependent variable while stock
market index was the predictor variable.
Fig 5: Simple Linear Regression
R-Squared value was 51.15% indicating that the model explained 51.15% variability in
the response variable and this averagely shows agood model (Sarstedt and Mooi 209-256). An
R-Squared value approaching zero indicates apoor model while avalue approaching 1indicates
agood strong model. For the Fstatistic, F(27, 1) =26.18 and p0.01 indicated that the
credit index was not significant at explaining or predicting real estate index. Therefore, we reject
null hypothesis and accept alternative hypothesis to be true and hence conclude that credit index
weakly affects real estate index. The wealth index coefficient was 2.56 with p

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