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REGIONAL ECONOMIC ISSUES
Working Paper Series
The M idw est Stock Price Index— Leading Indicator
o f Regional Econom ic A ctivity
William A. Strauss
F E D E R A L R ESERVE B A N K
O F C H IC A G O
WP- 1993/9
The Midwest Stock Price Index—Leading
Indicator of Regional Economic Activity
William A. Strauss
I. Introduction
Accurate forecasting of economic growth, whether nationally or regionally, is
an important objective for strategic planners and policy makers. Being able
to properly estimate the future direction of the economy leads to better
economic decisions. A constant challenge to analysts has been how best to
approach the problem of predicting the future course of a sector. Analysts use
many techniques to find the best forecast, varying from econometric models
incorporating existing data to combining data to form an indicator that can be
used for forecasting.1 The Standard & Poor’s 500 Stock Price Index (S & P
500 Stock Price Index) is an example of a series created by combining existing
data to form a composite series that can be used for forecasting. Indeed, the
S & P 500 Stock Price Index is a widely used leading economic indicator for
the U.S. economy. While national indicators are often used to predict regional
activity, it is more appropriate to work with indicators that are attuned to
regional conditions. These regional indicators should have the ability to detect
factors particular to the region that the national indicators would not capture.
The purpose of this working paper is to develop a regionally based tool to
forecast regional activity and then analyze and compare its performance as a
forecasting tool with the S & P 500 Stock Price Index. The results indicate
that this new regional stock price index tool can out-perform the S & P 500
Stock Price Index in regional forecasting.
II. Stock price movements as economic indicators
Because of its national scope, however, the S & P 500 Stock Price Index
might not be the best indicator of regional economic performance. Regional
economies vary dramatically in industrial structure and often move somewhat
independently of other regions. For example, the S & P 500 Stock Price Index
declined due to the government cutbacks in defense spending, that reduction
in spending had a dramatic impact on California, while in the Midwest the
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effect of those cutbacks was hardly noticeable. If the S & P 500 Stock Price
Index was used to explain Midwest activity, that particular downturn in the
index would lead to erroneous results. A Midwest based stock price index
should provide more region-specific information about the Midwest’s economic
performance. With this in mind, this paper is organized in three parts: 1) To
construct a Midwest Stock Price Index, 2) to compare its regional economic
explanatory properties with the S & P 500 Stock Price Index, 3) to examine
and compare its forecasting ability of the regional economy with the S & P
500 Stock Price Index.
III. Data sources
Stock price data for companies headquartered in the five states that comprise
the Seventh Federal Reserve District (Illinois, Indiana, Iowa, Michigan, and
Wisconsin) were acquired from the Compustat database, covering the period
January 1988 through June 1992. Of the 632 Midwest companies on the
Compustat database, 232 companies were eliminated because they did not have
continuous stock price information over the entire period. For the companies
that were missing employment information on the Compustat data base, the
Standard & Poor’s Register for 1991 provided the missing values. Table 1
presents the industry distribution of the 400 companies in the Midwest Stock
Price Index as compared with the Midwest distribution of all companies for
1989. The distribution of companies in the Midwest Stock Price Index are
quite different than the actual distribution of companies in the Midwest. The
Midwest Stock Price Index has a greater percentage of Mining, Manufacturing,
Transportation and Public Utilities, and Finance, Insurance, and Real Estate.
This is because only companies that have publicly traded stock, which tend to
be large companies, comprise the Midwest Stock Price Index. The industries
that are over-represented in the Midwest Stock Price Index tend to be
industries that have a large number of employees per company. For example,
while the number of construction companies made up nine percent of the total
number of companies in the Midwest, the average construction company
employed only eight people. It is hard to imagine having a publicly traded
stock for a company with only eight employees. In order to make the stock
price index more representative of the regional economy it will be necessary
to adjust the index accordingly. This issue will be dealt with later in the
paper.
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Table 1
Midwest industry distribution
Industry classification
Midwest
companies
Ag.f forestry, & fishing
Mining
Construction
Manufacturing
Trans. & pub. util.
Wholesale trade
Retail trade
Fin., ins., real estate
Services
9,124
2,152
68,299
55,447
30,781
63,190
200,632
64,804
254,027
Total
748,456
Midwest
percent
1.2
0.3
9.1
7.4
4.1
8.4
26.8
8.7
33.9
Avg.
no.
empl
Stock
index
companies
6
19
8
62
22
14
14
14
14
0
4
3
232
41
15
29
49
27
17
400
Stock
index
percent
0.0
1.0
0.8
58.0
10.3
3.8
7.3
12.3
6.8
There are a several issues about the construction of the index that need to be
discussed. First, each company in the Compustat database is assigned one
Standard Industrial Classification (SIC) code based on the company’s primary
line of business. This SIC code is typically a four-digit code, but several
companies are assigned either three or two-digit codes. Only when the
company is involved in more than one two digit industry does the problem of
having only one SIC assigned exist. Given the number of possible instances
of this happening it is not considered to be a major problem. Second, the
location of the company was based on the location of the headquarters even
though there is a chance that the company may do the bulk of their production
outside of the Midwest. The fact that many of the companies operate outside
the geographic boundaries of the Midwest, the impact locally of factors that
affect these companies is still considered to be quite large. The same type of
issue is at play when using the S & P 500 to forecast the national economy.
Many things outside the U.S. will have an affect on those companies in the
U.S. Third, companies may not have been in the Midwest over the entire time
period studied. Only those companies that were located in the Midwest in
June 1992 were included. If a company moved to the Midwest in any period
prior to June 1992 they are considered to be a Midwest company. If a
Midwestern company moved to a non-Midwestem state prior to June 1992 it
was not included in the index. This problem is not considered to be
widespread enough to cause any major problems. Finally, the technique of
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weighing the individual stock prices needs to be addressed. The S & P 500
Stock Price Index is constructed by weighing the stock prices of the companies
in the index by their respective market value, thus allowing the index to be
representative of the market value of the overall stock market. The Midwest
Stock Price Index is intended to be representative of the Midwest economy and
not of the stock market. Therefore, a different weighing scheme, one that
would reflect the regional economy, needs to be used. One good proxy for the
distribution of industries in a region, that is readily available with quite a bit
of detail, is state employment by SIC code. So by weighing the companies in
the Midwest Stock Price Index by their respective share of employment in the
region would yield an index that reflects the midwest economy.
IV. Construction of the Midwest Stock Price Index
Equation 1 illustrates how stock price indexes were generated for each four
digit SIC code with January 1988 equalling 100. If a particular four-digit SIC
contained only one company, the indexed stock price for this SIC code was
created using the stock prices for this one company. If more than one
company was in a particular SIC code, an employment weighted stock price
index was calculated. The stock price for each company in that SIC code was
multiplied by their relative company employment share between the different
companies in that SIC code.
E
where wij
Ut
i j JAN88
Nu
x
( 1)
100
=i
E*„
sL t = Stock price index for four-digit SIC j at time period t.
w.j = Weight for individual company i in four-digit SIC j.
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N .j = Em ploym ent f o r com pany i in fo u r-d ig it SIC j.
P.jt = Stock price for company i in four-digit SIC j at time period t.
r = The number o f companies in four-digit SIC j.
SI, = £
{% sij . }
(2)
p
where ^ vv. =
1
7=1
SIt = Midwest Stock Price Index at time period t.
Wj = Weight for four-digit SIC j.
p = The number of four-digit SIC groups in the index.
Equation 2 demonstrates the way the four-digit stock price indexes were
combined into one overall stock price index. Weights were chosen to reflect
Midwest employment at the two-digit level. In the most extreme example, if
a two-digit category contained only one four-digit industry, the entire
employment for that two-digit industry would be assigned to the four-digit
industry. As more four-digit industries comprise a two-digit industry, the
respective employment in each four-digit industry in the Midwest would
determine its share of the two-digit Midwest employment weight. The weights
were then multiplied by the stock price indexes for each four-digit SIC code
and the products of those calculations were then summed to create the Midwest
Stock Price Index.
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V. Comparison of the Midwest and S & P 5 0 0 Stock Price
Indexes
A comparison between the Midwest Stock Price Index and the S & P 500
Stock Price Index yields some interesting differences. Figure 1 graphs both
the Midwest Stock Price Index and the S & P 500 Stock Price Index. The
correlation between S & P 500 Stock Price Index and the Midwest Stock Price
Index indicate that they are somewhat similar in their monthly pattern. The
correlation between the levels and the percentage changes of the two indexes
is 0 .8 6 8 and 0 .6 8 6 , respectively.
Figure 1
Midwest and S & P 500 stock price indexes
There are some major differences between the two indexes in terms of their
construction and what they attempt to measure. First, the S & P 500 Stock
Price Index includes 500 stocks chosen with the aim of achieving a distribution
by broad industry groupings that approximates the distribution of these
groupings in the New York Stock Exchange common stock population. The
S & P 500 Stock Price Index represents 78 percent of the total market
capitalization of all domestic stocks. However, the S & P 500 Stock Price
Index is heavily dominated by large-capitalized stocks with over 50 percent of
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its total market value accounted for by the 50 largest stocks. While, the
Midwest Stock Price Index uses the stock prices of 400 companies located in
the five states that make up the Seventh Federal Reserve District regardless of
industry distribution or capitalization. Second, the S & P 500 Stock Price
Index includes dividends paid by their companies. The Midwest Stock Price
Index uses only stock prices in its construction due to the more timely nature
of stock prices compared with dividends that are paid quarterly and tend to lag
contemporaneous stock prices. Third, the weighing of the S & P 500 Stock
Price Index uses the market value of the company calculated by multiplying
the number of shares outstanding times the price of the stock. The Midwest
Stock Price Index uses the employment of each company to determine a four
digit SIC stock index and then employment in the Midwest for each SIC to
sum to the total stock index value.
The two indexes also differ in their primary objective. While the S & P 500
Stock Price Index exists to represent the pattern of common stock price
movement, the purpose of the Midwest Stock Price Index is to be a better
indicator of future performance of the regional economy, for example to aid
in predicting growth in employment. The difference in the weighing schemes
is due to this different focus on what each index hopes to measure.
If the Midwest Stock Price Index is to be used to anticipate future economic
activity in the Midwest, it should be representative of the structure of the
Midwest economy. Table 2 below shows the distribution of employment in
the Midwest and the distribution of employment weights used in the index.
The weights are close to the percentage in the population which indicates that
the employment represented by the companies included in the index are close
to the distribution of actual employment.
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Table 2
Midwest industry employment percent and employment weights
Industry classification
SIC
codes
Actual
Midwest
percent
Stock
index
weight
Ag., forestry, & fishing
Mining
Construction
Manufacturing
Trans. & pub. util.
Wholesale trade
Retail trade
Fin., ins., real estate
Services
00-09
10-14
15-19
20-39
40-49
50-51
52-59
60-69
70-89
0.4
0.3
4.4
26.8
5.3
6.7
21.3
7.0
27.7
0.0
0.2
1.6
30.0
5.9
7.8
22.3
8.5
23.7
VI. Analysis of midwest employment using stock price indexes
Regression models with percent differences from trend employment as the
dependent variable and percent differences from trend stock price indexes as
the independent variables—estimated through December 1991, reserving the
1992 values for out of sample testing. The models were estimated by
eliminating insignificant independent variables one at a time until all the
remaining independent variables had t values that were at least below the ten
percent probability level. Table 3 presents the initial estimated coefficients for
both indexes.
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Table 3
Regression models with percent differences from trend employment
as the dependent variable and percent differences from trend stock
price indexes as the independent variables—
estimated through December 1991
Variable
Intercept
Lag 2
Lag 3
Lag 4
Lag 5
Lag 11
Lag 12
Lag 13
Lag 14
Lag 17
Lag 18
r-square
Root MSE
S & P 500
coefficients
t-value
-0.011016
0.027301
-0.021528
-0.525
2.810
-2.391
-
-
0.019747
0.020325
0.031379
-0.051768
0.035087
0.043647
-0.048942
2.544
2.199
2.123
-3.390
3.536
3.662
-3.735
0.6736
0.1102
Midwest
stock index
coefficients
t-value
0.022105
0.016029
-0.021704
0.021427
0.014335
1.013
3.443
-3.377
4.159
3.526
-
-
-0.012622
0.011102
-2.504
2.261
--
-
--
0.5040
0.1241
The regression coefficients represent the percentage changes above trend that
would result to employment in the Midwest from a one percent change above
trend in stock prices. For example, if the S & P 500 Stock Price Index grew
by one percent, the sum of the coefficients indicates that employment in the
Midwest would be .044 percent above trend. For the Midwest Stock Price
Index, a one percent growth rate above its trend would generate a .051 percent
above-trend growth rate for employment. Both models do a very good job of
explaining movements in employment. By having an R-square value of .67 it
appears that the S & P 500 Stock Price Index does a better job at explaining
variations in employment than the Midwest Stock Price Index which has an Rsquare value of .50. The root mean square errors for both models are very
similar with the S & P 500 Stock Price Index having a slightly lower value.
Figures 2 and 3 illustrate the fitted values for the regression equations.
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Figure 2
Midwest employment—regression using the midwest stock price index
Figure 3
Midwest employment—regression using the S & P 500 stock price index
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However, before concluding that the S & P 500 Stock Price Index model is a
significantly better model at explaining employment in the Midwest a couple
of other factors need to be examined. The S & P 500 Stock Price Index
model’s first lag is lag 2. This allows the model to forecast ahead by 2
months. For example, if the last actual index value available is December
1992 the S & P Stock Price Index model could forecast January and February
1993 because the most recent actual index value the January forecast would
require would be November 1992 and the February forecast would use
December 1992 actual. Since its first lag is lag 3, the Midwest Stock Price
Index can forecast ahead one extra month compared with the S & P 500 Stock
Price Index model. The Midwest Stock Price Index also has the advantage of
being a more parsimonious model, using six lagged variables, compared with
nine for the S & P 500 Stock Price Index model. One final check on the
quality of forecasting models is to check how good the models actually
forecast. Figures 4 and 5 contain the out-of-sample forecasts for the two
models.
F ig ure 4
Midwest employment-regression using the midwest stock price index
I-—Actual
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---Insamplefit ---•Outofsamplefit
11
Figure 5
Midwest employment—regression using the S & P 500 stock price index
-- Actuai
---Insamplefit
Outofsamplefit1
Clearly, the S & P 500 Stock Price Index does a much poorer job in
forecasting the data than the Midwest Stock Price Index. In fact, the root
mean square error for the S & P 500 Stock Price Index model was over twice
as large as the Midwest Stock Price Index 0.3304 versus 0.1414. For the
Midwest Stock Price Index, the root mean square error for the out-of-sample
forecasts was much closer to the root mean square error for the in-sample fits.
So the Midwest Stock Price Index model is a more stable model than the S &
P 500 Stock Price Index model.
The models were reestimated using the same lag specification that had been
established in the 1988-1991 specification but adding the additional five
months of data for 1992. Table 4 presents the results for the reestimated
models.
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Table 4
Regression models with percent differences from trend employment
as the dependent variable and percent differences from trend stock price
indexes as the independent variables—estimated through June 1992
Variable
Intercept
Lag 2
Lag 3
Lag 4
Lag 5
Lag 11
Lag 12
Lag 13
Lag 14
Lag 17
Lag 18
r-square
Root MSE
S & P 500
coefficients
t-value
0.000480
0.020034
-0.015966
0.021
1.941
-1.606
-
-
0.023831
0.031095
0.005641
-0.025970
0.013548
0.021545
-0.022260
2.971
3.113
0.472
-2.146
1.504
2.045
-2.117
0.4493
0.1344
Midwest
stock index
coefficients
0.024527
t-value
1.212
-
~
0.014189
-0.020887
0.021449
0.015591
3.180
-3.423
4.346
3.998
-
-
-0.012236
0.009718
-2.483
2.021
-
-
~
0.4466
0.1257
For the Midwest Stock Price Index model the model changed very little from
the previously estimated model. The coefficients and their significance levels
were approximately the same, demonstrating the stability of the model.
Previously, a one percent above trend value for the Midwest Stock Price Index
correlated with a 0.050 percent above trend employment level in the Midwest,
in the revised model the value is 0.052. The R-square value fell by about six
percentage points, while the root mean square error rose by 0.0016. The S &
P 500 Stock Price Index model changed dramatically by adding the six
additional observations. The coefficient values changed quite a bit with lags
3, 12, and 14 becoming insignificant. In the earlier model a one percent above
trend value for the S & P Stock Price Index would have translated into a 0.045
percent above trend level employment in the Midwest, the new model’s value
is 0.052. The R-square value fell by 22 percentage points and the root mean
square error increased by 0.0242. In fact, while in the earlier model the S &
P 500 Stock Price Index model had a lower root mean square error than the
Midwest Stock Price Index model, in the new model the root mean square
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error advantage is reversed. Figures
values for the reestimated models.
6
and 7 present the actual and fitted
Figure 6
Midwest employment—regression using the midwest stock price index
F ig u re 7
Midwest employment—regression using the S & P 500 stock price index
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VII. Conclusions
This research set out to accomplish three things: 1) to construct a Midwest
Stock Price Index, 2) compare its regional explanatory power with the S & P
500 Stock Price Index, and 3) to examine and compare the forecasting abilities
of the Midwest Stock Price Index with the S & P 500 Stock Price Index. The
Midwest Stock Price Index was constructed using both regionally
headquartered company stock prices and a weighing scheme utilizing regional
employment that would generate an indicator that would be more reflective of
Midwest economy. The Midwest Stock Price Index appears to be able to add
substantially to our ability to anticipate movements in employment in the
Midwest. While at first glance it appeared that the S & P 500 Stock Price
Index would do a better job of explaining variations in Midwest employment,
judging by the out-of-sample forecasts the Midwest Stock Price Index captured
more of the regional influences that affect employment than the S & P 500
Stock Price Index.
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Footnotes
'The models used by Wharton Econometric Forecasting Associates and Data Resources Inc. are
examples o f large econometric forecasting models using existing data to generate forecasts. The
Index o f Leading Economic Indicators is an example o f a technique tool using existing data to
generate a series to be used for forecasting.
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