Introduction
There are several indicators that can help determine the market index of a country, but these cannot be particularly highlighted in a specific trend, in this report, there are two such models that have been taken into consideration, each of the models has been evaluated separately, and hence it can be said that this report is a comparative study of the two models.
The following is the comparative study of each model and as each model studies the impact of various variables on the economy, these variables pertain to the macroeconomic features of a nation. The country that studies in this report is the US, and the market index that has been taken into consideration is S&P 500 (Us inflation calculator, 2017).
Problem Statement/Hypothesis
The GDPs of other countries have a positive or negative impact on the US stock market index.
Regression Model 1
While the first model takes into consideration, it can be studied that the R square is 0.201 and adjusted R square is 0.0022. This alone indicates that too much of the model has been sent into residuals and the overall perspective states that it denotes a little proportion of the market index that is under consideration.
The second aspect of the variation includes the F stats and its significance; both of these measures look out for the significance of the overall model. On observing this, it can be denoted that the model in an overall context is not significant.
The Third portion of the analysis depicts the P values of the coefficients, these are the key indicators of the significances of the model, it can be seen here that the coefficients are not significant, this applies to all of the coefficients, it can further be seen that the variable, US GDP is only partially significant.
The Fourth component of this analysis is the coefficients. These indicate that the Consumer price index (-0.003) is negatively related to the market index’s performance, indicating that when the Price index increases the market index falls, in case of PPI (0.0053), the coefficient is positive, it can, therefore, be concluded that the variable of market returns is positively influenced by PPI, an increasing PPI it can be denoted that the Market index too increases (Statista.com, 2017). On further analysis, the Housing Price Index (-0.0005) also seems to be negatively related to a market index and thus, therefore, makes it evident that as the index increases the returns of the market index decrease. Interest rate (-03.4693) to have a negative impact on Market index’s performance, as it can be seen that on decreasing the interest rate the market index has historically made it quite evident that the returns would increase. Now the only, partially significant variable of the series comes into focus; it is seen that GDP of US (2.7622) has a positive relationship with a market index of US, indicating that a rise in GDP would give a rise in performance of S&P index (Data.oecd.org, 2017). It is further analyzed that the GDP of Spain (-0.4469) and Germany (-0.8223) are interpreted, and in this interpretation, it can be seen that both of them have a negative relationship with the market index, while it is evident, it can also be seen that the impact on GDP of Germany is more than that of Spain. It means, that if the German’s and Spain’s GDP are decreased by a similar proportion, German’s GDP lets the market index groom more than that of Spain (Multpl.com, 2017).
Regression Model 2
There are several aspects of a regression model; however, the most prominent four proponents would be discussed in this report. The first is that while the R square is 0.24, the adjusted R square is 0.051; this does indicate that there is a huge difference owing to residuals.
The second component of this regression analysis is the significance of the model. While it is seen that both the F stats and the P stats denote that this model is not significant. It can be seen that the overall model does not significantly prove that the independent variables affect the dependent variable in a significant manner.
The third component is the significance of the model; it can be seen that the potential significance of the model and its components, the independent variables, is very low. So here, it can be said that all the variables are insignificant, except, US GDP, which is partially significant in this case. This indicates that the variables in this distribution are devised in a manner that is not found to have a uniform impact on the market index (Fred.stlouisfed.org, 2017).
The fourth component of this distribution is the interpretation of Coefficients. It can be seen that Consumer Price index (-0.0004) has a negative relationship with marker index and Housing Price Index (-0.0006) has a negative relationship with S&P index. It can be observed that the Interest rate (-2.3752) has a negative relationship with the market Index as well. Here it can be seen that both unemployment (1.7628) and US GDP (2.9040) has a positive relationship with the market index. In the country’s comparison part, it is seen that Germany GDP (-1.0834) and China GDP (-0.9598) both negatively affect the index of the market of US, in this case, S&P 500 (Data.bls.gov, 2017). It indicates that on the increasing consumer price index and housing price index, the market index would decrease, with decreasing the interest rates the index would increase and on increasing the rate of unemployment and US GDP, the market index too would increase. It can further be seen that an increasing the GDP of China and Germany the index of US Markets S&P 500 falls (fred.stlouisfed, 2017).
Conclusion
It can overall be concluded that though both the models are insignificant with all their coefficients, except US GDP, insignificant. It can be seen that the second regression model is less insignificant and manages to predict the dependent variable, market index, more than model 1. The key difference of the two models is that model 1 includes PPI (0.0053) and Spanish GDP (-0.4469), whereas model 2 replaces it with Chinese GDP (-0.9598) and the unemployment rate (1.7628). Therefore, it can be said that Chinese GDP and Interest rate (-2.3752) contribute more to the US market index than Spanish GDP (-0.4469) and PPI (0.0053). Therefore, it is obvious from the above analysis that GDP of some countries like Spain has a positive impact and GDP of other countries like China and Germany has a negative impact on the US stock markets.
Recommendation
On an overall perspective, it can be seen that model 2 overall is better than the model. Therefore, it is at this moment recommended that the second model should be used if a closer estimate of the index of the US market must be developed.
References
Data.bls.gov. (2017, October 7). Bureau of Labor Statistics. Retrieved from https://data.bls.gov/cgi-bin/dbdown?Your+request+was+invalid+for+this+Data+Access+Service.+Please+attempt+other+data+requests.+Thank+you+for+using+LABSTAT.
Data.oecd.org. (2017, October 7). Gross domestic product (GDP). Retrieved from https://data.oecd.org/gdp/gross-domestic-product-gdp.htm
fred.stlouisfed. (2017, October 7). 10-Year Treasury Constant Maturity Rate (DGS10). Retrieved from https://fred.stlouisfed.org/series/DGS10
Fred.stlouisfed.org. (2017, October 7). All-Transactions House Price Index for the United States (USSTHPI). Retrieved from https://fred.stlouisfed.org/series/USSTHPI
Multpl.com. (2017, October 7). US GDP Growth Rate by Year. Retrieved from http://www.multpl.com/us-gdp-growth-rate/table/by-year
Statista.com. (2017, October 7). Annual changes of the Producer Price Index (PPI) for commodities in the United States from 1990 to 2016. Retrieved from https://www.statista.com/statistics/193966/annual-changes-of-the-producer-price-index-for-commodities-in-the-us-since-1990/
Us inflation calculator. (2017, October 7). Consumer Price Index Data from 1913 to 2017. Retrieved from http://www.usinflationcalculator.com/inflation/consumer-price-index-and-annual-percent-changes-from-1913-to-2008/
Appendix
Regression Model 1
| SUMMARY OUTPUT | ||||||||
| Regression Statistics | ||||||||
| Multiple R | 0.449143 | |||||||
| R Square | 0.20173 | |||||||
| Adjusted R Square | 0.002162 | |||||||
| Standard Error | 0.162185 | |||||||
| Observations | 36 | |||||||
| ANOVA | ||||||||
| df | SS | MS | F | Significance F | ||||
| Regression | 7 | 0.186123 | 0.026589 | 1.010835 | 0.444934 | |||
| Residual | 28 | 0.736512 | 0.026304 | |||||
| Total | 35 | 0.922635 | ||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 0.48733 | 0.577695 | 0.843577 | 0.406055 | -0.69602 | 1.670685 | -0.69602 | 1.670685 |
| CPI | -0.00329 | 0.006839 | -0.48127 | 0.634061 | -0.0173 | 0.010717 | -0.0173 | 0.010717 |
| PPI | 0.005312 | 0.011104 | 0.478389 | 0.636088 | -0.01743 | 0.028059 | -0.01743 | 0.028059 |
| HPI | -0.00053 | 0.001419 | -0.37186 | 0.712796 | -0.00343 | 0.002378 | -0.00343 | 0.002378 |
| Interest Rate | -3.46939 | 3.331916 | -1.04126 | 0.306662 | -10.2945 | 3.355726 | -10.2945 | 3.355726 |
| US GDP | 2.762256 | 1.52166 | 1.815291 | 0.080205 | -0.35472 | 5.879236 | -0.35472 | 5.879236 |
| Spain GDP | -0.44687 | 1.614488 | -0.27678 | 0.783978 | -3.75399 | 2.860263 | -3.75399 | 2.860263 |
| Germany GDP | -0.82235 | 1.717751 | -0.47873 | 0.635845 | -4.341 | 2.696308 | -4.341 | 2.696308 |
Regression Model 2
| SUMMARY OUTPUT | ||||||||
| Regression Statistics | ||||||||
| Multiple R | 0.490748 | |||||||
| R Square | 0.240834 | |||||||
| Adjusted R Square | 0.051042 | |||||||
| Standard Error | 0.158163 | |||||||
| Observations | 36 | |||||||
| ANOVA | ||||||||
| df | SS | MS | F | Significance F | ||||
| Regression | 7 | 0.222202 | 0.031743 | 1.268938 | 0.301016 | |||
| Residual | 28 | 0.700434 | 0.025015 | |||||
| Total | 35 | 0.922635 | ||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 0.342357 | 0.499671 | 0.685165 | 0.498873 | -0.68117 | 1.365888 | -0.68117 | 1.365888 |
| CPI | -0.00037 | 0.003106 | -0.11886 | 0.906233 | -0.00673 | 0.005992 | -0.00673 | 0.005992 |
| HPI | -0.00056 | 0.001107 | -0.51019 | 0.613915 | -0.00283 | 0.001702 | -0.00283 | 0.001702 |
| Interest Rate | -2.37518 | 3.24086 | -0.73289 | 0.46972 | -9.01378 | 4.263422 | -9.01378 | 4.263422 |
| Unemployment Rate | 1.762811 | 1.907838 | 0.923984 | 0.36339 | -2.14522 | 5.670839 | -2.14522 | 5.670839 |
| US GDP | 2.904046 | 1.487417 | 1.952409 | 0.060954 | -0.14279 | 5.950882 | -0.14279 | 5.950882 |
| Germany GDP | -1.08344 | 1.362909 | -0.79494 | 0.433334 | -3.87523 | 1.708357 | -3.87523 | 1.708357 |
| China GDP | -0.95972 | 0.847523 | -1.13238 | 0.267078 | -2.69579 | 0.776353 | -2.69579 | 0.776353 |
