Introduction/Background
The market index of any country can be determined with the assistance of various and different indicators. S&P 500 is the most reliable benchmark for knowing the investment trends of the US stock markets. For the prediction of a stock market, various variables are involved in knowing the market behaviors, but it is also a fact that real market trends cannot be anticipated. In this report, only those factors or variables are used that have the most impact on the economy and stock market.
In this analysis report, two regression models are being adopted, and every model is used and evaluated individually so this report is based on a comparative analysis study of these adopted regressions models. The economic indicators that have been used for this analysis report are.
- Interest Rate
- House Price Index (HPI)
- Product Price Index (PPI)
- Unemployment Rate
- Consumer Price Index (CPI)
- Gross Domestic Product (GDP) of major countries
Literature Review
For the analysis of any data, the statistical method, which is, used known as Regression Analysis. It is very helpful to identify and characterize the relationship between 2 or among multiple factors. Regression analysis makes possible three things for statistical evaluation like:
Description: A relationship among independent and dependent variables is described statistically with the help of regression analysis.
Estimation: When values of some dependent variables are projected by observing the values of some independent variables by using regression analysis, it is called estimation.
Prognostication: Risk factors which have an impact on the results and are identified by determining the individual prognoses (Gallo, 2015).
Regression analysis uses a model, which explains a relationship between independent variable and dependent variable in a simple mathematical form. Regression analysis uses a model that expresses a relationship between the independent variables and the dependent variables in a plain mathematical shape. There are many situations where in some cases the contribution of one independent variable is not enough for explaining the dependent variable. Therefore, in such conditions, we can perform or use multivariable linear regression for studying the impact of various or multiple variables (Schneider, Hommel, & Blettner, 2010).
Hypothesis/Problem Statement
H0a: Market index is not influenced by CPI
H0b: Market index is not influenced by PPI
H0c: Market index is not influenced by HPI
H0d: Market index is not influenced by Interest Rate
H0e: Market index is not influenced by US GDP
H0f: Market index is not influenced by Spain GDP
H0g: Market index is not influenced by Germany GDP
H1a: Market index is influenced by CPI
H1b: Market index is influenced by PPI
H1c: Market index is influenced by HPI
H1d: Market index is influenced by Interest Rate
H1e: Market index is influenced by US GDP
H1f: Market index is influenced by Spain GDP
H1g: Market index is influenced by Germany GDP
Regression Model 1
Analysis
Before we determine this model, it is known that R square is 0.20 and adjusted R square is 0.002. It is noticed that much of this model is unused and the whole point is saying that very little of this model is being used now.
The second thing, which is discussed, is Difference in F, its stats, and its implications. Both R and F contribute to this regression model and are of great importance.
The third thing that is to be taken into consideration is the coefficient of P. These are of great importance to this model. We know coefficients are of no importance, and this phoneme is applied the all the coefficients it is observed that US GDP is significant at a smaller level.
The fourth part of this study is the study of coefficients. It shows Consumer Index Price is inversely related to Market Index’s performance, which shows when Price Index increases the Market Index falls. It is known that coefficient is positive in the case of PPI. So, when PPI has increased Market index increases as well. There is a direct relationship. On further studies, It is concluded that House Price Index is also inversely related to Market Index. It proves when Index increases the return of market index reduces. Market Index is negatively influenced by the Interest rate. It is clearly seen that on reducing the interest rate, the market index dramatically increases. Now the only important variable is to be discussed.
It is well known that GDP and market Index of US has a direct relationship, which means an increase in GDP will result in increased performance of S & P Index. Further discussion is in Germany and Spain, and their GDP. GDP of Spain and Germany has an inverse relationship with the market index, but GDP of Spain is less than that of Germany. Therefore, if GDP of both the countries decrease at similar proportion, GDP of Germany will flourish than that of Spain in a market.
Regression Model 2
Analysis
There are many considerations to the regression model. Most prominent and important ones are to be discussed here. Here R square is 0.24 and adjusted R square is 0.05, and there is not such a huge difference that could be significant. Another important factor is the importance of the model. This model does not conclude that independent variables affect dependent variables in a very distinctive manner.
The third important factor is Importance of this model and its elements. The independent variables are quite low in this model of regression. Variables are not significant; The US GDP though is significant to some extent in this model. Therefore, it is concluded that variables are derived in a way that has a uniform impact on Market Index.
The fourth component is the explanation of the Coefficients; Consumer Price Index has an inverse relationship with Market Index; Housing Price index also has an inverse relationship with S&P. Market interest has an inverse relationship with Market Index.
The market index has a direct relationship with US GDP and unemployment. China and Germany both inversely affect US market index. S&P is 500 in this particular case. When Housing price index and Consumer price index is increased, the Market Index reduces which results in decreasing Interest rates and when the Index increases, there is an increase in unemployment rate and US GDP resulting in the market index to increase. It is observed that on increasing GDP of China & Germany, US market index S&P reduces.
Recommendations
From the above discussions and analysis, it is found that model 2 is better than model 1. So according to my decisions, I recommend that model 2 should be adopted for the near estimation of index for the United States market for development
Conclusion
It can be concluded that all the null hypotheses have been accepted and it it can be said there the variables in both the models are not influencing the market index significantly. At the end of this paper, through deep results and analysis, it is concluded that regression model 1 and regression model 2, both are very important including their all coefficients, but the only GDP of the United States is insignificant. It is observed that Model 1 is more insignificant as compared to model 2 for managing and prediction of market index dependent variables. The major difference in these two models is that model 2 has unemployment rate and Chinese GDP while model 1 has Spanish GDP and Product Price Index (PPI). In nutshell, PPI and GDP of Spain are contributing less to the United States stock market in while Interest rate and GDP of China are contributing more to the US economy and stock market.
References
Data.bls.gov. (2017, October 7). Bureau of Labor Statistics. Retrieved from https://www.bls.gov/data/
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
Gallo, A. (2015, November 4). A Refresher on Regression Analysis. Retrieved from https://hbr.org/2015/11/a-refresher-on-regression-analysis
Multpl.com. (2017, October 7). US GDP Growth Rate by Year. Retrieved from http://www.multpl.com/us-gdp-growth-rate/table/by-year
Schneider, A., Hommel, G., & Blettner, M. (2010). Linear Regression Analysis. Deutsches Ärzteblatt International, 107(44), 776–782.
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 | 0.4491 | |||||||||
| R Square | 0.2017 | |||||||||
| Adjusted R Square | 0.0022 | |||||||||
| Standard Error | 0.1622 | |||||||||
| Observations | 36.0000 | |||||||||
| ANOVA | ||||||||||
| df | SS | MS | F | Significance F | ||||||
| Regression | 7 | 0.1861 | 0.0266 | 1.0108 | 0.4449 | |||||
| Residual | 28 | 0.7365 | 0.0263 | |||||||
| Total | 35 | 0.9226 | ||||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |||
| Intercept | 0.4873 | 0.5777 | 0.8436 | 0.4061 | -0.6960 | 1.6707 | -0.6960 | 1.6707 | ||
| CPI | -0.0033 | 0.0068 | -0.4813 | 0.6341 | -0.0173 | 0.0107 | -0.0173 | 0.0107 | ||
| PPI | 0.0053 | 0.0111 | 0.4784 | 0.6361 | -0.0174 | 0.0281 | -0.0174 | 0.0281 | ||
| HPI | -0.0005 | 0.0014 | -0.3719 | 0.7128 | -0.0034 | 0.0024 | -0.0034 | 0.0024 | ||
| Interest Rate | -3.4694 | 3.3319 | -1.0413 | 0.3067 | -10.2945 | 3.3557 | -10.2945 | 3.3557 | ||
| US GDP | 2.7623 | 1.5217 | 1.8153 | 0.0802 | -0.3547 | 5.8792 | -0.3547 | 5.8792 | ||
| Spain GDP | -0.4469 | 1.6145 | -0.2768 | 0.7840 | -3.7540 | 2.8603 | -3.7540 | 2.8603 | ||
| Germany GDP | -0.8223 | 1.7178 | -0.4787 | 0.6358 | -4.3410 | 2.6963 | -4.3410 | 2.6963 |
Regression Model 2
| SUMMARY OUTPUT | ||||||||
| Regression Statistics | ||||||||
| Multiple R | 0.4907 | |||||||
| R Square | 0.2408 | |||||||
| Adjusted R Square | 0.0510 | |||||||
| Standard Error | 0.1582 | |||||||
| Observations | 36.0000 | |||||||
| ANOVA | ||||||||
| df | SS | MS | F | Significance F | ||||
| Regression | 7 | 0.2222 | 0.0317 | 1.2689 | 0.3010 | |||
| Residual | 28 | 0.7004 | 0.0250 | |||||
| Total | 35 | 0.9226 | ||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 0.3424 | 0.4997 | 0.6852 | 0.4989 | -0.6812 | 1.3659 | -0.6812 | 1.3659 |
| CPI | -0.0004 | 0.0031 | -0.1189 | 0.9062 | -0.0067 | 0.0060 | -0.0067 | 0.0060 |
| HPI | -0.0006 | 0.0011 | -0.5102 | 0.6139 | -0.0028 | 0.0017 | -0.0028 | 0.0017 |
| Interest Rate | -2.3752 | 3.2409 | -0.7329 | 0.4697 | -9.0138 | 4.2634 | -9.0138 | 4.2634 |
| Unemployment Rate | 1.7628 | 1.9078 | 0.9240 | 0.3634 | -2.1452 | 5.6708 | -2.1452 | 5.6708 |
| US GDP | 2.9040 | 1.4874 | 1.9524 | 0.0610 | -0.1428 | 5.9509 | -0.1428 | 5.9509 |
| Germany GDP | -1.0834 | 1.3629 | -0.7949 | 0.4333 | -3.8752 | 1.7084 | -3.8752 | 1.7084 |
| China GDP | -0.9597 | 0.8475 | -1.1324 | 0.2671 | -2.6958 | 0.7764 | -2.6958 | 0.7764 |
