Economic Activity and McDonalds' Stock

8/3/2019 Economic Activity and McDonalds' Stock

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Economic Activity And McDonald’s Stock PricesWagner, Wasserman, Smith and Carpenter

The efficient markets hypothesis states that all stock prices represent their fundamental value at

each point in time. Further, it maintains that the stock market runs perfectly informed. If this

hypothesis were true, then any new information which may substantially affect the value of a

company’s stock is already seen in its price. It also must be true that at any given time the price of a

company’s stock represents the overall value of the company. Building on this hypothesis, we want to

use the price of stock for the McDonalds Corporation to evaluate the impact of economic fluctuations

have on the value of McDonalds.

Our original model controlled for the national savings level, the consumer price index, the

political affiliation of the current president, and the level of the S&P 500. We have also decided to test

the impact the movie Super Size Me had on the stock price of McDonalds. To do this we declared an

event dummy for all dates after the year 2004, setting the value to 0 prior to 2004 and to 1 after 2004.

We have also decided to add the unemployment rate to measure the effect fluctuations in income had

on the stock prices. Lastly, we added Kroger's stock price because of the assumed substitution between

groceries and going out to eat. That is to say, people go out to eat more often when their disposable

income increases.

Testing Our Model

Using the Ramsey reset test, we reject the hypothesis that all nonlinearities are accounted for

with 99.99% confidence. We would also suspect that there are important omitted variables. The

Davidson Mackinnon test was performed by creating a second model with quadratics for the CPI,

unemployment and savings rate. As the level of these variables increase, we would suspect that the

level of fast food consumption will increase because of a decrease in hours of leisure or some other



pertinent effect on real wages. The test’s results are conducive to our suspicions with the original model

being rejected with 99.99% confidence.

Because of an issue with larger than expected standard errors on our initial regression, we

found that our data could be defunct. But, instead of abandoning the project we decided to use the

Davidson-Mackinnon test to determine whether or not the data was salvageable. After reviewing the

findings, we decided that a new model using the natural logs of real GDP, savings, and unemployment

would be our preferred model. The model we will use will be:

MCD = ß0 + ß1(S&P500) + ω + ß6 (KROGERSTK) + ß7 (TRENDVAR) + δ0 (POL) + δ1 (SUPRSIZEME) + u

ω = [ß2 (LNSVG) +ß3 (LNUNEMP) + ß4 (CPI) + ß5 (LNRGDP)]

There are possible issues concerning endogeneity; that if we were to address it in our model,

could bolster its precision. Specifically, but not limitedly: a measure for a change in transfer payments,

such as unemployment insurance extensions during economic downturns, the average hours that each

person in the labor force is working each month – this would control for the amount of leisure that is

available in the workforce, and the consumer confidence level – this would control for changes in

discretionary spending due to a household’s view of current economic health. Another control that

might lead to more accurate depiction of the behavior would be to move the GDP data ahead to

determine if the information has on the general public.

The Breusch-Pagan test for heteroskedasticity showed that our model is, in fact,

heteroskedastic. To account for this problem, we will use robust standard errors to maintain a relevant

level of statistical significance. Also, we converted some of our variables into logs to help alleviate the

heteroskedasticity.





Understanding these key variables shows us the effect that key economic factors have on the

price of McDonald’s stock. We saw that almost all the variables had a significant effect on the price of

McDonald’s stock, but a few statistics not accounted for in the beginning could explain this. For

example, we did not look at the variability of the stock of McDonalds, not did we look at the standard

deviation of the restaurants stock data. A high variability can lead to what could be perceived as

significant changes; also the standard deviation could give us more insight into the idea of economic

significance. All in all, the stock prices generally showed a tendency to perform well when the economy

is spending money or just beginning to cut back because of an increase in unemployment. More study is

of course needed, but due to the fact that our data and findings still have room for analysis, this

regression is inconclusive.



APPENDIX

. regress

MCD sp500 lnSVGS lnRGDP POL SUPRSZME lnunempl SSE > KROGERSTK CPI var14

Source | SS df MS Number of obs = 36

-------------+------------------------------ F( 12, 23) = 47.41

Model | 5004.13922 12 417.011602 Prob > F = 0.0000

Residual | 202.287494 23 8.79510845 R-squared = 0.9611

-------------+------------------------------ Adj R-squared = 0.9409

Total | 5206.42672 35 148.755049 Root MSE = 2.9657

------------------------------------------------------------------------------

MCD | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

sp500 | .0580555 .014651 3.96 0.001 .0277476 .0883635lnSVGS | -139.4984 36.08208 -3.87 0.001 -214.1399 -64.85692

lnRGDP | 99.54638 197.9328 0.50 0.620 -309.9088 509.0015

POL | -1.190918 3.711364 -0.32 0.751 -8.86846 6.486623

SUPRSZME | 6.664474 3.135973 2.13 0.045 .177219 13.15173

lnunempl | 65.70522 13.91697 4.72 0.000 36.91577 94.49467

KROGERSTK | -.0935521 .319795 -0.29 0.772 -.7550984 .5679942

CPI | -.3920476 .609201 -0.64 0.526 -1.652276 .8681806

var14 | 3.517009 1.323342 2.66 0.014 .7794684 6.25455

_cons | -201.2886 1599.537 -0.13 0.901 -3510.184 3107.606

------------------------------------------------------------------------------

. predict yhat

(option xb assumed; fitted values)

(276 missing values generated)

. regress

MCD sp500 SVGS RGDP SVGSsq RGDPsq POL SUPRSZME UN

> EMPL unemplsq SSE KROGERSTK CPI var14 yhat


-------------+------------------------------ F( 16, 19) = 77.95

Model | 5128.29826 16 320.518641 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.9724

Total | 5206.42672 35 148.755049 Root MSE = 2.0278

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

sp500 | .2934654 .1389335 2.11 0.048 .0026742 .5842567



SVGS | -.6065174 .2410394 -2.52 0.021 -1.111019 -.102016

RGDP | -.0572657 .1146206 -0.50 0.623 -.2971694 .1826381

SVGSsq | .0000647 .0000225 2.87 0.010 .0000176 .0001119

RGDPsq | 4.47e-06 4.59e-06 0.97 0.343 -5.14e-06 .0000141

POL | -8.690724 3.972973 -2.19 0.041 -17.00625 -.3751946

SUPRSZME | 32.13149 15.63001 2.06 0.054 -.5825094 64.84548

UNEMPL | 122.1611 54.30682 2.25 0.037 8.495589 235.8265

unemplsq | -5.356118 2.723134 -1.97 0.064 -11.0557 .3434676

KROGERSTK | .0033529 .4160976 0.01 0.994 -.8675493 .8742552

CPI | -3.344627 1.145695 -2.92 0.009 -5.742594 -.9466603

var14 | 17.62712 8.316956 2.12 0.047 .2195332 35.03471

yhat | -4.77735 2.376021 -2.01 0.059 -9.75042 .1957195

_cons | -331.1084 962.6046 -0.34 0.735 -2345.863 1683.646

------------------------------------------------------------------------------

. regress

MCD sp500 lnSVGS lnRGDP POL SUPRSZME lnunempl

> KROGERSTK CPI var14


-------------+------------------------------ F( 12, 23) = 47.41

Model | 5004.13922 12 417.011602 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.9409

Total | 5206.42672 35 148.755049 Root MSE = 2.9657

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------sp500 | .0580555 .014651 3.96 0.001 .0277476 .0883635

lnSVGS | -139.4984 36.08208 -3.87 0.001 -214.1399 -64.85692

lnRGDP | 99.54638 197.9328 0.50 0.620 -309.9088 509.0015

POL | -1.190918 3.711364 -0.32 0.751 -8.86846 6.486623

SUPRSZME | 6.664474 3.135973 2.13 0.045 .177219 13.15173

nikki225 | .0009418 .0005102 1.85 0.078 -.0001137 .0019973

euronex100 | -.0089089 .0041389 -2.15 0.042 -.0174708 -.000347

lnunempl | 65.70522 13.91697 4.72 0.000 36.91577 94.49467

SSE | -.0001147 .0009208 -0.12 0.902 -.0020195 .0017902

KROGERSTK | -.0935521 .319795 -0.29 0.772 -.7550984 .5679942

CPI | -.3920476 .609201 -0.64 0.526 -1.652276 .8681806

var14 | 3.517009 1.323342 2.66 0.014 .7794684 6.25455

_cons | -201.2886 1599.537 -0.13 0.901 -3510.184 3107.606

------------------------------------------------------------------------------

. ovtest

Ramsey RESET test using powers of the fitted values of MCD

Ho: model has no omitted variables



F(3, 20) = 3.45

Prob > F = 0.0362

. tsset var14

time variable: var14, 1 to 116

delta: 1 unit

. dfuller MCD

Dickey-Fuller test for unit root Number of obs = 115

---------- Interpolated Dickey-Fuller ---------

Test 1% Critical 5% Critical 10% Critical

Statistic Value Value Value

------------------------------------------------------------------------------

Z(t) -2.875 -3.505 -2.889 -2.579

------------------------------------------------------------------------------

MacKinnon approximate p-value for Z(t) = 0.0483

. dfuller MCD ,trend

Dickey-Fuller test for unit root Number of obs = 115

---------- Interpolated Dickey-Fuller ---------

Test 1% Critical 5% Critical 10% Critical

Statistic Value Value Value

------------------------------------------------------------------------------

Z(t) -3.299 -4.035 -3.448 -3.148

------------------------------------------------------------------------------MacKinnon approximate p-value for Z(t) = 0.0664

. regress

MCD sp500 lnSVGS lnRGDP POL SUPRSZME nikki225 euronex100 lnunempl SSE

> KROGERSTK CPI var14


-------------+------------------------------ F( 12, 23) = 47.41

Model | 5004.13922 12 417.011602 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.9409

Total | 5206.42672 35 148.755049 Root MSE = 2.9657

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

sp500 | .0580555 .014651 3.96 0.001 .0277476 .0883635

lnSVGS | -139.4984 36.08208 -3.87 0.001 -214.1399 -64.85692

lnRGDP | 99.54638 197.9328 0.50 0.620 -309.9088 509.0015



POL | -1.190918 3.711364 -0.32 0.751 -8.86846 6.486623

SUPRSZME | 6.664474 3.135973 2.13 0.045 .177219 13.15173

lnunempl | 65.70522 13.91697 4.72 0.000 36.91577 94.49467

KROGERSTK | -.0935521 .319795 -0.29 0.772 -.7550984 .5679942

CPI | -.3920476 .609201 -0.64 0.526 -1.652276 .8681806

var14 | 3.517009 1.323342 2.66 0.014 .7794684 6.25455

_cons | -201.2886 1599.537 -0.13 0.901 -3510.184 3107.606

------------------------------------------------------------------------------

. durbina

Durbin's alternative test for autocorrelation

---------------------------------------------------------------------------

lags(p) | chi2 df Prob > chi2

-------------+-------------------------------------------------------------

1 | 2.274 1 0.1315

---------------------------------------------------------------------------

H0: no serial correlation

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Economic Activity and McDonalds' Stock