Multiple Regression Model

Project Multiple infantile fixation mystify Introduction Todays short letter market place offers as many opportwholeies for investors to raise money as jeopardies to lose it because market depends on different factors, such as boilers suit observed countrys performance, foreign countries performance, and unexpected events. One of the most important melodic phrasetaking market great poweres is Standard & Poors 500 (S 500) as it comprises the 500 largest Ameri give the axe companies across different industries and sectors. Many great deal put their money into the market to get reaping on investing.Investors ask themselves questions like how to make money on the agate line market and is there a focussing to signal in slightly degree how the bank line market will be fetch? There atomic anatomy 18 lots and lots of inconstants involved in how the rip market behaves at a specific time. The broth market is in a substance an information agency. Based on new information, whether good or bragging(a) regarding almost everything from political issues to please rates and inflation, the stock market deal 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 500 is considered to be the most reliable benchmarkfor the overall U. S. stock market, we decided to study what factor has the most shock absorber on it. We created two atavism posers and included the economic indicators, such as Consumer charge Index, Producer Price Index, House Price index, Inte breathe Rate, Unemployment Rate, and Gross home(prenominal) Product of some countries. Model Specification and Data How accurately undersurface 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 hav e a very long and conglomerate 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 gross domestic product. With this enquiry we argon hoping to find a statistically portentous model that would recognize what affects the stock market. We used the average annual data from 1980 to 2011 to track the bias on the US market. Our data is a time-series data.It is very kindleing since in spite of appearance these 31 years there were a lot of changes in the countries economies, fiscal regulations and policies. At the very beginning, we yieldd that the following factors may have watch on stock market S (Percentage Change) = ? 0 + ? 1*(yearly CPI) + ? 2*( one-year norm PPI) + ? 3*(Annual Average House Price Index) + ? 4*(Annual Average Interest Rate) + ? 5*(Percentage Change of Annual Average gross domestic product of US) + ? 6*(Percentage Change of Annual Average gross domestic pro duct of Spain) + ? 7*(Percentage Change of Annual Average gross domestic product of Germany) 1 Consumer Price Index reflects the state of inflation in the countrys 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 sop up money for further development of their handicraftes. This entire situation may spite the stock prices of the companies and thats why we wanted to see how big the squeeze is. We assume that this variable is going affect the dependent variable a lot. ?2 Producer Price Index indicates early state of inflation.Therefore, if investors manage that the PPI heralds a strong economy with no increase in an interest rate, so they feel confident to invest in the businesses what means change magnitude positive activity in the market. We assume that this variable is going to have some extend to on the dependent variable however it is non going to be c rucial. ?3 House Price Index is an analytic as welll for estimating changes in the rates of mortgages. If mortgage rates are high, so housing market is weak because demand for houses drops due to expensive loans, wherefore HPI drops.In 2008 mortgage default affected stock market very badly because before that period house prices went down because people couldnt net income their mortgage payments and banks collapsed. Decrease in house prices is one of the possible contributors to respite 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 shouldnt move in the uniform direction thereby we dont take into consideration the tortuous scenario of 2008. ?4 10-Year Treasury Constant matureness Rate impacts on the number of issued bond and is used as risk free rate to head the excess return on the investment. It also has an influence on the stock market. ?5 Gross Domestic Product of the US is important for business profit and this can drive the stock prices up. Investing in the stock market seems reasonable when the economy is doing well.If the economy is growing fast hence the stock market should be affected positively, the investors are more approbative well-nigh the future and they put more money into market more. This variable is crucial for the dependent one. ?6 Gross Domestic Product of Spain. Since europium is currently in a recession, we wanted to include the gross domestic product of Spain, as one of the weakest economies in Europe now, to check if there is any blood between Spains economy and the US stock market performance. real small percentage of US investments goes to Spain.Compared to Germany, which is the fifth country the USA invests into, Spain is the thirty-first country on the list. There should not be any coefficient of correlation coefficient between these two variables, so we included Spains gross domestic product into our regression to check our hypothesis. ?7 Gross Domestic Product of Germany is an indicator of Germany is the 5th largest economy in the world and is the largest European trade and investment cooperator of the US. Germany is the largest economy in Europe and almost 1/5 of GDP of the European Union is that of Germany alone. We assume that this variable has to have an impact on the US stock market.The second regression model is the following S (Annual Average) = ? 0 + ? 1*(Annual CPI) + ? 2*(Annual Average House Price Index) + ? 3*(Annual Average Interest Rate) + ? 4*(Average Annual Unemployment Rate) + ? 5*(Annual Average GDP of US) + ? 6*(Annual Average GDP of Germany) + ? 7*(Annual Average GDP of china) afterward we run the regression of the second model, it resulted in improving of our model accuracy. We stave offd PPI, GDP of Spain because it came bulge that these variables have no impact on the US stock market.Also, we added the unemployment rate and GDP of China because it is the largest US business partner. Here is the chronicle of the new variables Unemployment Rate is one of the most important factors of the economys performance. High unemployment rate decreases the buyer power of the consumers. 2/3 of the US economy is consumer based and it influences the stock market negatively. We assume that there is a relationship between these two variables. Gross Domestic Product of China affects the US economy because cheap export from China prevents inflation in the US.China is a huge buyer of the US Treasuries. It lowers the interest rate and companies seize money to invest in development hence, it directly affects the stock market. We assume that GDP of China and US stock market move in the same direction, meaning if China does well, it has money to buy US Treasuries. Additionally, the US s tock market increases because production of those US companies that is outsourced to China grows. Results The First Model pic face at this model, we see that only the interest rate and GDP of US are statistically significant because they have P-values lower than 0. 05.The rest variables do not correlate with S because their P-values are high. Our assumption about Spains economy affecting the US stock market was proved. The coefficient we got for GDP of Spain is statistically insignificant. Looking at the US and Spain investment relationships in the colossal aspect we see that Spains performance has no significant impact on the US stock market even considering its economic situation. PPI is a fraction of inflation and CPI also reflects inflation, so we decided to exclude one variable because two variables together cancelled each other out and we got defected result.As P-value is smaller compared to P-value of PPI, we decided to keep it in the second model. Looking at the familiari zed R square which is 26 %, we concluded that model is deficient and we have to change the variables. The Second Model pic Each estimated coefficients we can interpret as follows -For every 1 unit increase in Annual CPI, the S will go down by -25. 68 S points. When inflation goes up, it causes interest rate to go up, therefore companies are not willing to borrow money and invest. Hence the S index moves in the opposite direction to CPI.The P-value of 0. 000368 implies that the results are statistically significant and it coincides with our assumption. -For every 1 unit increase in House Price Index, the S goes down by -7. 97 units, which tell us that when the price of houses rises, the stock market moves in the opposite direction and it shrinks because people invest in the housing market. The P-value of 0. 000028 shows it is a statistically significant burden. -For every 1 unit increase in Annual Average 10-Year Treasury Constant Maturity Rate, the S index goes up by 27. 4 units. I t would imply that when interest rates go up then the stock market goes up as well, but the p-value of 0. 154 tells us the results are not statistically significant and we should not rely on this outcome. There is no correlation. -For every 1 unit increase in Annual Average Unemployment Rate in US, the S goes down by 40. 44 units. The p-value of 0. 043 shows what we consider a statistically significant correlation. We can conclude that unemployment rate has a reverse impact on the stock market.When more people have jobs, more people have money to spend and to invest, hence the economy speeds up and the stock market goes up. -For every 1 unit increase in Annual Average GDP of US, the S goes up by 0. 601 index units. The p-value of 0. 00000069 shows the outcome is statistically significant, and implies that when the GDP of US grows meaning that the economy is doing better, investors are more confident and invest more and stock market also goes up. -For every 1 unit increase in Annual Average GDP of Germany, the S goes up by 0. 224 units.We assumed that when Germany is producing more products and their economy is doing well, then the stock market in US does somewhat better too because Germany and US have an economic interaction. The P-value of 0. 155 tells us that the relationship is not statistical significant to conclude the Annual Average GDP of Germany has a positive relationship with S. -For every 1 unit increase in Annual Average GDP of China, the S goes down by . 154 units. US economy as we know is affected by Chinese economy. When US companies move production overseas, specifically to China, the stock market in US does poorly.The P-value of 0. 005 means that this results is statistically significant. We did not find any violations with SLR/MLR assumptions. There appears to be no problem with the data and all the results are relevant. Summary The adjusted R2 of . 96 means that our regression of 96% explains the changes in S. We ground out that the biggest correlation is observed between US GDP, CPI, HPI, and Chinas GDP. We found out that the GDP of Germany and Interest Rate has no significant correlation with S predicted performance.As we explained above in the result section, the investors should look at US economy performance as well as Chinas economic performance, CPI and HPI to try to predict the stock market behavior. References 1. http//www. infoplease. com/ipa/A0774473. hypertext markup language 2. Federal Housing Finance Agency Web Site 3. U. S. incision of Commerce Bureau of Economic Analysis Web Site 4. U. S. surgical incision of Labor Bureau of Labor Statistics Web Site 5. mhttp//research. stlouisfed. org/fred2/series/SP500/downloaddata? cid=32255