Problem Statement & Rationale of the Study

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Problem Statement & Rationale of the Study

Introduction

This study as explained above aims at understanding the stock price movement of the United States stock market on the basis of different economic drivers mentioned above. The stock market fluctuations look random but are derived and controlled by a system of variables as understood from the literature and research studies. This study aims at understanding the impact of such key economic drives by performing statistical research on the Stock market index of the United States, Standard & Poor 500 and the economic variables discussed above. Multi variable regression model has been adopted to understand the relationship between the stock market index and the economic indicators. With the help of different departments of the United States and their online data, the historical data for the 30 years period from 1980 to 2011 has been obtained for the index as well as the economic variables. MS Excel has been used as the statistical tool for performing the statistical analysis of the data to analyze the relationship between the stock market index value and the economic drivers so that market movements can be well explained and understood.

Literature Review

As explained above, various research studies and literature has been explored to understand the stock price movements. The studied have been performed since 1960s and till now there have been huge literature on the stock price movements and fluctuations. Various models have been proposed based on the periodical trend movements of the market, different variables, economic factors, and so on.

Karl Borch has written a research paper in 1963 explaining his observations on the price movements in the Stock market. He had performed a detailed analysis on day to day movements for a four year period on the stocks listed on the New York Stock Exchange and have concluded based on his research study that the prices do not move independently but move in unison in one direction. (Borch, 1963)

In his research paper, Karl has referred to many old studied from 1930 where in researchers have tried to verify if the stock markets fluctuations are a random walk process but have concluded stating that there is a system that the stock market adopts by which it is controlled and this system is driven by various factors both at micro and macroeconomic level.

Najeb Masoud in 2014 has advanced the research study on the stock markets by publishing a paper on predicting the stock market indices using Artificial Neural Networks. He had conducted a detailed study on the Libyan Financial market and has adopted an advanced study to predict the movement. He also had identified various factors based on which his model has been designed to predict the market. (Masoud, 2014) In 1991, Hertz and his team had given introduction to the theory of neural computation in to the stock market predictions and it has been extended by Masoud extensively to predict the behavior (Hertz, Krough, & Palmer, 1991).

In 2003, Chen, Leung and Daouk have extensively studied the trading over Taiwan Stock Market index and means to forecast and predict the trading on this index. Thus there have been many studies and researches that have been happening in this domain to predict and forecast the stock market prices. (Chen & Leung, 2003)

All these research studies and literature have pumped us to identify certain economic indicators and assess their impact in the stock market index. Thus we have chosen the United States market

for our study and have considered mentioned economic variables to conduct a statistical study and analyze the impact.

Research Design

This research is structured so as to identify the impact of the various economic indicators on the stock market index value. The data required for the study has been obtained from various sources. Yahoo Finance has helped to obtain the stock market index values. Monthly data has been obtained for the period from 1980 to 2011 and their annual average has been calculated for the study.

From the different departments of the United States, the historical data related to Consumer Price Index, Housing Price Index, Producer Price Index, Interest Rate and GDP growth has been obtained. In the first model, the GDP of Spain and Germany has been considered to verify their impact whose results are explained in next section. It is noted that the GDP growth of Spain has no significant impact on stock market movement and similarly the producer price index factor. Thus another model has been created with Unemployment rate and GDP growth of China as the factors. Both the model results are explained below in next section.

Analysis

As mentioned above, two regression models have been formed considering economic variables to identify the movement in the United States Stock Market index, Standard & Poor 500. The model 1 has considered, Consumer Price Index, House Price Index, Producer Price Index, Interest Rate, GDP growth of US, Spain and Germany.

The results of the model 1 are as below:
Regression Statistics
Multiple R 0.69
R Square 0.48
Adjusted R Square 0.32
Standard Error 0.12
Observations 32.00
ANOVA
df SS MS F
Regression 7 0.31 0.04 3.12

 

Residual 24 0.34 0.01
Total 31 0.64
Coefficients Standard Error t Stat P-value
Intercept 0.26 0.48 0.54 0.59
CPI 0.00 0.00 0.16 0.87
PPI -0.01 0.01 -0.91 0.37
HPI 0.00 0.00 -1.09 0.29
Interest Rate -3.90 2.45 -1.59 0.13
% change of GDP US 4.04 1.21 3.34 0.00
% change of GDP Spain -0.22 0.99 -0.22 0.83
% change of GDP Germany 1.53 1.15 1.33 0.20

The model has been formed with % change in the closing price of S&P 500 as the dependent variable and the mentioned factors as the independent variables. The model based on the results is as below:

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)

The model after results is:

S&P500 (% Change) = 0.26 + 0.00 * (Annual CPI) -0.01 * (Annual Average PPI) + 0.00 * (Annual Average House Price Index) – 3.90 * (Annual Average Interest Rate) + 4.04 * (% Change of Annual Average GDP of US) – 0.22 * (% Change of Annual Average GDP of Spain) + 1.53 * (% Change of Annual Average GDP of Germany)

The R square of this model is noted to be 48% only indicating that the goodness of this model is only to the extent of 48%. It also indicates that except for GDP growth of the United States, none of the variables have significant impact on the % change in the stock market. Thus, another model excluding the Producer Price Index value, GDP growth of Spain variables and including Unemployment Rate, GDP growth of the China as variables is formed. The model also had considered the annual average closing price of the index as the dependent variable instead of the percentage change. The model 2 results are as below:

Regression Statistics
Multiple R 0.96
R Square 0.93
Adjusted R Square 0.91
Standard Error 142.96
Observations 32
ANOVA
df SS MS F
Regression 7 6287853.04 898264.72 43.95
Residual 24 490467.55 20436.15
Total 31 6778320.59
Coefficients Standard Error t Stat P-value
Intercept -1560.56 539.74 -2.89 0.01
CPI 15.27 3.52 4.34 0.00
HPI 0.23 1.15 0.20 0.85
Interest Rate 7948.22 3207.89 2.48 0.02
Unemployment Rate -8442.88 1760.26 -4.80 0.00
% change of GDP US 2971.83 1411.94 2.10 0.05
% change of GDP Germany -1443.55 1215.25 -1.19 0.25
% change of GDP China -2024.60 1013.33 -2.00 0.06

This model has got a R-square of 93% indicating that this model has a goodness fit to the accuracy of 93% which is highly accepted. Also, from the model it is clear that with p-values less than 0.05, the factors of consumer price index, interest rate and % change in GDP of US have got significant impact on the stock market index price movement. Chinese economy GDP growth has got significant impact at 90% confidence level so is the house price index and Germany GDP growth.

The model as per this revised version is as below:

S&P500 (Annual Average) = -1560.56 + 15.27 * (Annual CPI) + 0.23 * (Annual Average House Price Index) + 7948.22 * (Annual Average Interest Rate) – 8442.88 * (Average Annual Unemployment Rate) + 2971.83 * (Annual Average GDP of US) – 1443.55 * (Annual Average GDP of Germany) – 2024.60 * (Annual Average GDP of China)

The significance of the drivers and their relationship with the stock market index values are also clearly understood from these results. It may be noted that the stock market index annual average value is directly related to the variables of consumer price index, average interest rate, GDP growth of US and House Price Index while it is negatively related with the variables Unemployment rate, GDP growth of China and Germany. It implies that increase in consumer price index, housing price index, interest rate and GDP growth of the United States will lead to positive movement in the stock market index while increase in unemployment rate or the GDP growth of Germany or China will lead to negative movement in the market. The impact of the movements are defined by the coefficients and with high goodness fit upto 96%, this model 2 is preferred and may be accepted.

Conclusion

Based on the above study and analysis, it may be understood that the stock market index price movement is not random and is controlled by a system of variables or drivers. From the study it is noted that the stock market movement is positive with respect to positive change in the consumer price index, house price index, risk free or interest rate, GDP growth of the United States while it is negative with respect to the unemployment rate, GDP growth of China and Germany. These factors including Producer Price Index and GDP growth of Spain were found to have no significant impact on the percentage change in the stock market index value while were observed to have significant impact on the stock market index value excluding producer price index and GDP growth of Spain. The main limitation of the study is that study does not take in consideration many other factors that can have impact on the stock market.

The study has also explored and reported various studies and literature which support with the statistical findings of this study. The study has finally arrived at the following model which is found to have a goodness fit of 93%.

S&P500 (Annual Average) = -1560.56 + 15.27 * (Annual CPI) + 0.23 * (Annual Average House Price Index) + 7948.22 * (Annual Average Interest Rate) – 8442.88 * (Average Annual Unemployment Rate) + 2971.83 * (Annual Average GDP of US) – 1443.55 * (Annual Average GDP of Germany) – 2024.60 * (Annual Average GDP of China)

References

  • Board of Governors of the Federal Reserve System (US). (n.d.). 10 year Treasury Maturity Rate. Retrieved from https://research.stlouisfed.org/fred2/series/DGS10/downloaddata
  • Borch, K. (1963). Price Movements in the Stock Market. Economic Research Programme, Princeton University.
  • Chen, A. S., & Leung, M. T. (2003). Forecasting and Trading on Taiwan Stock Market Index. Computers and Operations Research.
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  • Learning And Developing Strategy

    Learning And Developing Strategy

    The role of the learning and developmental strategies in the organization is to manage the talent pool in the organization while at the same time it helps in development of a culture towards the development in the organization. Also this helps to drive the commitments of the people working in the organizations as they feel valued through satisfaction of the developmental needs and career advancements. Thus various strategies that are used by Star Industries are:

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