Multiple Regression Model

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Project: Multiple Regression Model

Introduction

    Today’s stock market offers as many opportunities for investors to raise money as jeopardies to lose it because market depends on different factors, such as overall observed country’s performance, foreign countries’ performance, and unexpected events. 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. 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 proactively, 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, 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.

Model Specification and Data
         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. 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 2011 to track the influence on the US market. Our data is a time-series data. It is very interesting since within these 31 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 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...
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