The document analyzes the relationship between stock market performance and economic growth in the U.S. from 1980-2011. It finds a strong positive correlation between changes in the Dow Jones Industrial Average and nominal GDP. Regression analysis shows stock market fluctuations explained about 87% of the variation in GDP. The results suggest stock prices can influence economic activity by affecting business confidence, financing, and household wealth. Therefore, large declines in stock prices may precede and prolong economic downturns.

1. Relationship between the
STOCK MARKETS and
ECONOMIC GROWTH
Experience from International Financial Markets-A Case
Study on U.S.A.
RESEARCH PAPER
2012-2013
B.A.(HONS.) ECONOMICS-2ND YEAR
Abstract
A causal inspection of stock market prices and GDP in
developed market economies reveal that these tend to move together over time.
This raises the question as to what is the reason for such a relationship. Our
objective is to assess the relationship between Nominal GDP (A measure of
economic growth) and the Stock market fluctuations. This paper examines the
question whether financial development has a profound relationship with
economic growth. The focus of the paper is on long-term trends and the
empirical results suggest the existence of a stable long-run relationship between
stock market prices and Nominal GDP and hence proving that stock market
indices act as a barometer of the economic growth in a country.

2. 2
INTRODUCTION
The behaviour of aggregate stock prices is a subject of enduring
fascination to investors, policymakers, and economists alike. A causal
inspection of stock market prices and GDP in developed market
economies reveals that these tend to move together.
Two of the longest periods of economic weakness observed in the
industrialised world during the twentieth century namely, the Great
Depression in the US and the ‘lost decade’ of the 1990s in Japan, are
often identified with the asset-price busts that preceded them. In both
circumstances, rapidly falling prices marked the beginning of
painfully long periods of economic setbacks.
There is a broad consensus that stock market performance impacts the
economy and this influence has been explained by the following
effects.
1.Confidence effects: Persistent stock market declines can be
interpreted as the harbinger of economic slowdown, lowering
consumer confidence and business outlook which inturn typically
leads to lower consumption and investment spending.
2.Financing effect in the corporate sector is another factor. The more
companies rely on the stock markets on financing, the more they are
held back by the bear markets.
3.Wealth effects i.e. the rising (falling) stock prices can
increase(decrease) the sense of financial wealth among private
households. A bull market can boost and a bear market can depress
private consumption- and with it the economy as a whole.

3. 3
MACROECONOMIC THEORY-
The Link between Stock Prices and the Economy
Macro-economic theory suggests that there should be a strong link
between economic activity and security prices, given that the stock
price is the discounted present value of the firm’s payout. If this
payout is ultimately a function of real activity, such a link should
prevail. The standard discounted-cash-flow model implies that stock
prices lead real economic activity if investors’ expectations about
firms’ future payouts are correct on average. This is one theoretical
argument as to how stocks and economic output may be related. If we
have a look at the tables T.1 and T.2 given below, we can clearly
make out the level of correlation between the stock market
capitalisation and economic wellbeing of a country.
Top Ten Biggest Stock Markets in the
World
No. Market
1 United States
2 Japan
3 United Kingdom
4 France
5 Canada
6 Germany
7 Switzerland
8 Italy
9 Australia
10 Korea
Source-wikipedia Table- T.1
Top Ten Biggest Economies in the
World
Rank Country/Region 2010 Nominal GDP
(Million of US$)
( World ) (62,633,783)
1 United States 14,447,100
2 China 5,739,358
3 Japan 5,458,873
4 Germany 3,280,334
5 France 2,559,850
6 United Kingdom 2,253,552
7 Brazil 2,088,966
8 Italy 2,051,290
9 India 1,722,328
10 Canada 1,577,040
Source- Wikipedia Table-T.2
TABLE- T.2

4. 4
A case study of U.S.A.
Moving our focus to U.S.A., as we know it has the largest market
capitalization and is also the largest economy in terms of nominal
GDP. Stock markets are a vital component for economic
development for U.S.A as they provide listed companies with a
platform to raise long-term capital and also provide investors with a
forum for investing their surplus funds. U.S Stock market therefore
encourages investors with surplus funds to invest them in additional
financial instruments that better matches their liquidity preferences
and risk appetite. Better savings mobilization may increase the
savings rate, and which in turn spurs investments and earns
investment income to the owners of those funds. As the U.S.
economy has grown, more funds are needed to meet the rapid
development and the stock markets serve as a veritable tool in the
mobilization and allocation of savings among competing uses which
are critical to the growth and efficiency of the country.
Objective
The objective of this research paper is to analyse the effect of the
changes in stock prices (Dow Jones Industrial Average) on the
Nominal GDP (billion dollars) of USA from 1980-2011(July YOY).
This will hereby let us comprehend that how the stock prices act as
an indicator of the economic growth and how they play an important
role in business cycles in the economy.

5. 5
Methodology
We will perform our econometric analysis in the following
manner-
 Step 1: We take time series data for the two variables involved
in our model from the underlying data source.
Here we take Dow Jones index as the explanatory variable (Xt
variable), and Nominal GDP(as a measure of economic growth)
as our explained variable(Yt).
 Step2: Here we will define our Population Regression function.
E(Yt) = B1 + B2Xt (Non-stochastic)
Or
Yt = B1 + B2Xt + Ut (Stochastic)
where Ut represents the Stochastic error term.
B1 and B2 are the regression coefficients.
 Step 3: Here we define our Sample regression
function(Considering the basic assumptions of classical linear
regression model to be true)
Yt = b1 + b2Xt + et
Where b1 and b2 are the ordinary least square estimators of B1
and B2
 Step 4: Here we perform the hypothesis testing on our OLS
estimators b1 and b2 and test them for significance. We will also
provide required tests for the ‘goodness of fit’ and comprehend
the ANOVA for the model defined.

6. 6
Data
Source of the two time series data in the model-
1. Nominal GDP yearly data (in billion dollars) was obtained from the
IMF International Financial Statistics (IFS) for the first financial
quarter of every fiscal year- i.e. Beginning of July data.
2. For the US, the stock market index used in the analysis was the
Dow Jones Industrial Average obtained on closing basis YoY every
July from the official website of New York Stock Exchange i.e.
Www.Nyse.com.
Empirical Work
Our objective is to provide an analysis of the relation between the
explained variable Nominal GDP (Yt) and the explanatory variable
Dow Jones Index (Xt)
The following figure shows the scatter diagram between the two-
FIGURE: On the X axis lies the Dow Jones Index and on the Y axis lies the Nominal GDP
0
2000
4000
6000
8000
10000
12000
14000
16000
0.00 5000.00 10000.00 15000.00
Nominal GDP

7. 7
Figure: This figure depicts the changes in the two variables w.r.t. time
The scatter plot shows a strong Correlation between the two
variables.
Considering the Sample regression function-
Yt = b1 + b2Xt + et
Analysis of our data gives us the following result [See Appendix] -
Hence, Y(cap) = 3018.81 + 0.86Xt
{Y(cap) denotes the Estimator of Yt } Standard error (444.52) Standard error (0.059)
t-statistic (6.79) t- statistic
(14.38)
P-value (1.5*10-7
) P-value (5.32*10-
15
)
0.00
5000.00
10000.00
15000.00
20000.00
25000.00
30000.00
Nominal GDP
Dow Jones

8. 8
Figure: Fitted regression line using the method of least squares
To see how significant our data is we perform the ‘Goodness of fit’
test on the variables and the model itself. The results of our test is as
follows-
R2
= 0.873
Adjusted R2
=0.869
The high values of R2
and adjusted R2
shows that our data is
very significant in empirical terms. (i.e. null hypothesis that the
slope regression coefficient= 0 is rejected and we get a strong
empirical correlation between the X and the Y variables).
Also we have provided the ANOVA table for our model which clearly
shows that F=206.884 which is a quite significant value, suggesting
that the variation in Y( Nominal GPD) is quite significantly explained
by our X variable ( Dow Jones Index ).
y = 0.862x + 3018.8
R² = 0.8734
0.0
2,000.0
4,000.0
6,000.0
8,000.0
10,000.0
12,000.0
14,000.0
16,000.0
0.00 5000.00 10000.00 15000.00
NominalGDP(BillionDollars)
Dow Jones Index
Fitted Regression Line
Nominal GDP
Linear (Nominal GDP)

9. 9
Conclusion
This paper examines the hypothesis that stock market development
leads to economic growth. Although the effect of the equity market
on economic growth varies per region and time periods. In our case
of U.S.A. it shows a positive effect on economic activity and
economic growth.
The observed relationship between GDP and stock prices implies that
the level of economic activity in a country can potentially depend on
the stock market amongst other variables. The observed
phenomenon hinted in the introduction, that long periods of
weaknesses such as the Great Depression and the ‘lost decade’ in
Japan are identified with the asset-price busts that preceded them,
could therefore be no mere coincidence. The significant contraction
in asset values triggered a subsequent contraction in consumption
and economic activity levels. Hence a large downfall in stock prices
caused a similar decrease in economic activity.
The empirical results of the econometric test, conducted here
support the view of presence of a causal link between stock market
performance and economic growth.
The findings therefore call for an effective and efficient regulatory
framework that prevents the occurrence of runaway prices in
domestic stock markets. It also calls for the need of in depth analysis
of financial markets in order to predict future business cycles and
make better fiscal and monetary policy decisions.

10. 10
References
 O.Blanchard (2006), Macroeconomics,4th
edition, Pearson
Education (India)
 R.Dornbusch and S.Fischer (2006), Macroeconomics,6th
edition, Tata McGraw Hill Publication
 Gujarati D.N. (2007) edition, Essentials of econometrics: Tata
McGraw Hills Publication
Websites:
 www.Wikipedia.org For some listed tables and information
 www.google.com/Finance For verification of statistics
 www.NYSE.com
 http://www.imf.org/external/data.htm IMF data and
statistics

11. 11
Appendix
Data used in our analysis-
Year
Dow Jones
Industrial
Average
Nominal
GDP
1980 891.13 2,788.1
1981 955.67 3,126.8
1982 814.12 3,253.2
1983 1207.23 3,534.6
1984 1122.57 3,930.9
1985 1334.45 4,217.5
1986 1821.43 4,460.1
1987 2455.99 4,736.4
1988 2106.15 5,100.4
1989 2487.86 5,482.1
1990 2904.95 5,800.5
1991 2932.47 5,992.1
1992 3330.56 6,342.3
1993 3521.06 6,667.4
1994 3709.14 7,085.2
1995 4702.73 7,414.7
1996 5588.14 7,838.5
1997 7921.82 8,332.4
1998 9105.74 8,793.5
1999 11193.70 9,353.5
2000 10635.98 9,951.5
2001 10252.68 10,286.2
2002 9379.50 10,642.3
2003 9119.59 11,142.2
2004 10213.22 11,853.3
2005 10449.14 12,623.0
2006 11090.67 13,377.2
2007 13611.68 14,028.7
2008 11100.54 14,291.5
2009 8280.74 13,973.7
2010 9686.48 14,498.9
2011 12582.77 15,075.7

12. 12
Basic formulae used in the calculation, and hypothesis of our
variables.
b1= E(Y) – b2E(X)
b2= [∑XiYi – nE(X)E(Y)]/ ∑Xi
2
- n E(Y)2
R2
= ESS/ TSS
Adjusted R2
= {1- (1- R2
) (n-1)}/(n-k)
Where, k is the total number of variables
F= {R2
/(k-1)}/{(1- R2
)/(n-k)}
The following table shows the results of our calculations:
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.934535046
R Square 0.873355752
Adjusted R Square 0.869134277
Standard Error 1410.325629
Observations 32
ANOVA
df SS MS F
Significance
F
Regression 1 411496141.7 4.11E+08 206.884 5.32E-15
Residual 30 59670551.41 1989018
Total 31 471166693.1
Coefficients
Standard
Error t Stat P-value Lower 95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 3018.812666 444.5235344 6.79112
1.57E-
07 2110.974 3926.651 2110.974 3926.651
X Variable 1 0.862004381 0.059930236 14.38346
5.32E-
15 0.739611 0.984398 0.739611 0.984398