Multiple Regression Model Analysis for Stock Market Behavior

Running head: MULTIPLE REGRESSION MODEL 1
Assignment One: Multiple Regression Model Analysis
Name
Institution

MULTIPLE REGRESSION MODEL 2
Assignment One: Multiple Regression Model Analysis
Introduction
Today’s stock market offers as many opportunities for investors to raise money as
jeopardizes to lose it because market depends on different factors, such as overall observed
country’s performance, foreign countries’ performance, and unexpected events (Friedberg,
2018). One of the most important stock market indexes is Standard & Poor's 500 (S&P 500) as it
comprises the 500 largest American companies across various industries and sectors. Many
people put their money into the market to get return on investment (Roberts, 2017). Investors ask
themselves questions like how to make money on the stock market and is there a way to predict
in some degree how the stock market will behave? There are lots and lots of variables involved
in how the stock market behaves at a specific time.
The stock market is in a way an information agency. Based on new information, whether
good or bad regarding almost everything from political issues to interest rates and inflation, the
stock market can go up or down. The market is anticipating economic occurrences pro-actively,
ignoring already occurred events that were predicted before. This way it is very hard to predict
how it is going to move in the future. As S&P 500 is considered to be the most reliable
benchmark for the overall U.S. stock market (Berger, 2017), we decided to study what factor has
the most impact on it. We created two regression models and included the economic indicators,
such as Consumer Price Index, Producer Price Index, House Price index, Interest Rate,
Unemployment Rate, and Gross Domestic Product of some countries.
First Regression Model Specifications and Data

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How accurately can we predict the stock market behavior?
People working in the finance industry have been trying to estimate or predict the
behavior of stock market for a long time, or maybe some of them already have a very long and
complex model of predicting the behavior of a stock market based on many factors and variables
(Martin, 2014). We decided to use the US economic indicators and the other countries’ GDP.
With this research we are hoping to find a statistically significant model that would describe
what affects the stock market.
We used the average annual data from 1980 to 2015 to track the influence on the US
market. Our data is a time-series data. It is very interesting since within these 36 years there were
a lot of changes in the countries’ economies, financial regulations, and policies. At the very
beginning, we assumed that the following factors may have influence on stock market: S&P500
(Percentage Change) = β0 + β1 * (Annual CPI) + β2 * (Annual Average PPI) + β3 * (Annual
Average House Price Index) + β4 * (Annual Average Interest Rate) + β5 * (Percentage Change
of Annual Average GDP of US) + β6 * (Percentage Change of Annual Average GDP of Spain) +
β7 * (Percentage Change of Annual Average GDP of Germany)
β1: Consumer Price Index reflects the state of inflation in the country’s economy. That indicator
is very important in the assessment of the stock market performance. If inflation grows, the
interest rate rises and this prevents the companies to borrow money for further development of
their businesses. This entire situation may hurt the stock prices of the companies and that’s why
we wanted to see how big the impact is. We assume that this variable is going affect the
dependent variable a lot. β2: Producer Price Index indicates early state of inflation. Therefore, if
investors know that the PPI heralds a strong economy with no increase in an interest rate, then

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they feel confident to invest in the businesses what means increased positive activity in the
market. We assume that this variable is going to have some impact on the dependent variable
however; it is not going to be crucial.
β3: House Price Index is an analytical tool for estimating changes in the rates of mortgages. If
mortgage rates are high, then housing market is weak because demand for houses drops due to
expensive loans, therefore HPI drops (Poff & Poff, 2013). In 2008 mortgage default affected
stock market very severely because before that period house prices went down because people
couldn’t pay their mortgage payments and banks collapsed. Decrease in house prices is one of
the possible contributors to recession because the home owners lose their equity in their houses.
Considering such recession scenario, the stock market always becomes bearish. Additionally,
house market is considered more stable investment than stock market. When stock market drops,
people are willing in the houses and HPI goes up. We assume that HPI and stock market
shouldn’t move in the same direction thereby we don’t take into consideration the complex
scenario of 2008.
β4: 10-Year Treasury Constant Maturity Rate impacts on the number of issued bond and is used
as risk free rate to calculate the excess return on the investment. It also has an influence on the
stock market.
β5: Gross Domestic Product of the US is important for business profit and this can drive the
stock prices up (Poff & Poff, 2013). Investing in the stock market seems reasonable when the
economy is doing well. If the economy is growing fast then the stock market should be affected
positively, the investors are more optimistic about the future and they put more money into