Stock Prices, News, and Economic Fluctuations

TLDR: The authors show that the joint behavior of stock prices and TFP favors a view of business cycles driven largely by a shock that does not affect productivity in the short run, but affects productivity with substantial delay, and therefore does not look like a monetary shock.

Abstract: We show that the joint behavior of stock prices and TFP favors a view of business cycles driven largely by a shock that does not affect productivity in the short run ? and therefore does not look like a standard technology shock ? but affects productivity with substantial delay ? and therefore does not look like a monetary shock. One structural interpretation for this shock is that it represents news about future technological opportunities which is first captured in stock prices. This shock causes a boom in consumption, investment, and hours worked that precedes productivity growth by a few years, and explains about 50 percent of business cycle fluctuations. (JEL G12, E32, E44)

Figures

Figure 18: 2 Against ̃1 in the (TFP, SP,C,H) VAR, without (left panel) or with (right panel) Adjusting TFP for Capacity Utilization

Figure 19: Share of the Forecast Error Variance of Consumption (C), Investment I, Output (C+I) and hours (H) attributable to 2 (left panel) and to ̃1 (right panel) in 4-variables VARs, with non adjusted TFP (top panels) or adjusted TFP (bottom panels)

Figure 5: Robustness to Cointegration: Impulse Responses to 2 (upper panels) and ̃1 (lower panels) in the (TFP, SP ) VAR

Figure 15: 2 Against ̃1 in the (TFP, SP,C) VAR, without (left panel) or with (right panel) Adjusting TFP for Capacity Utilization

Figure 9: 2 Against ̃1 in the (TFP, SP ) VAR, Using Basu, Fernald, and Kimball [2002] Measure of TFP (annual, 1949-1989)

Figure 20: Impulse Responses to 2 and ̃1 in the in the (TFP, SP,C, I) VAR, without (upper panels) or with (lower panels) Adjusting TFP for Capacity Utilization

Full text

NBER WORKING PAPER SERIES
STOCK PRICES, NEWS AND ECONOMIC FLUCTUATIONS
Paul Beaudry
Franck Portier
Working Paper 10548
http://www.nber.org/papers/w10548
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
June 2004
The authors thank Susanto Basu , Larry Christiano, Roger Farmer, Robert Hall, Richard Rogerson, Julio
Rotemberg and participants at seminars at CEPR ESSIM 2002, SED Paris 2003, Bank of Canada, Bank of
England, the Federal Reserve of Philadelphia, the National Bureau of Economic Research, University of
Berlin, Université du Québec à Montréal, Université de Toulouse and CREST for helpful comments.
The views expressed herein are those of the author(s) and not necessarily those of the National Bureau of
Economic Research.
©2004 by Paul Beaudry and Franck Portier. All rights reserved. Short sections of text, not to exceed two
paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given
to the source.

Stock Prices, News and Economic Fluctuations
Paul Beaudry and Franck Portier
NBER Working Paper No. 10547
June 2004
JEL No. E3
ABSTRACT
In this paper we show that the joint behavior of stock prices and TFP favors a view of business
cycles driven largely by a shock that does not affect productivity in the short run -- and therefore
does not look like a standard technology shock -- but affects productivity with substantial delay --
and therefore does not look like a monetary shock. One structural interpretation we suggest for this
shock is that it represents news about future technological opportunities which is first captured in
stock prices. We show that this shock causes a boom in consumption, investment and hours worked
that precede productivity growth by a few years. Moreover, we show that this shock explains about
50\% of business cycle fluctuations.
Paul Beaudry
Department of Economics
University of British Columbia
997-1873 East Mall
Vancouver, BC
Canada V6T 1Z1
and NBER
paulbe@interchange.ubc.ca
Franck Portier
Manufacture des Tabacs
Universite des Sciences Sociales de Toulouse
21 allee de Brienne
31000 Toulouse, France
fportier@cict.fr

1 Introduction
There is a huge literature suggesting that sto ck prices movements reflect the market’s expectation
of future developments in the economy. As a test of standard valuation models, Fama [1990] shows
that monthly, quarterly and annual stock returns are highly correlated with future production
growth rates for 1953-1987. This result is confirmed on a extended sample (1889-1988) by Schwert
[1990]. Both authors argue that the relation between current stock returns and future production
growth reflects information about future cash flows that is impounded in stock prices. On the other
hand, not all stock prices movements are informative, as Shiller [1981] noted that stock prices move
too much to be justified by subsequent changes in dividends, such an evidence being confirmed
by Flavin [1983] and Mankiw, Romer, and Shapiro [1985]. There is also a huge literature, and a
long tradition in macroeconomics (from Pigou [1926] and Keynes [1936] to the survey of Benhabib
and Farmer [1999]), suggesting that changes in expectation may be an important element driving
economic fluctuations.
Given this, it is surprising that the empirical macro literature – es pecially the VAR based
literature – rarely exploits stock prices movements to expand our understanding of the role of
expectations in business cycle fluctuations. In this paper, we take a step in this direction by
showing how stock prices movements, in conjunction with movements in total factor productivity
(TFP), can be fruitfully used to help shed new light on the forces driving business cycle fluctuation.
The empirical strategy we adopt in this paper is to perform two different orthogonalization
schemes as a means of identifying properties of the data that can then be used to evaluate theories
of business cycles. Let us be clear that our empirical strategy is a purely descriptive device which
becomes of interest only w hen its implications are compared with those of structural models. The
two orthogonalization schemes we use are based on imposing sequentially, not simultaneously, either
impact or long run res trictions on the orthogonalized moving average representation of the data.
The primary system of variables that interests us is one composed of an index of stock market
value (SP) and measured total factor productivity (TFP). Our inte rest in focusing on stock market
3

information is motivated by the view that stock prices are likely a good variable for capturing any
changes in agents expectations about future economic conditions.
The two disturbances we isolate with our procedure are first, a disturbance which represents
innovations in stock prices which are orthogonal to innovations in TFP and sec ond, a disturbance
that drives long run movements in TFP. The main intriguing observation we uncover is that these
two disturbances– when isolate separately without imposing orthogonality – are found to be almost
perfectly co-linear and to induce the same dynamics. We also show that these co-linear shock
series causes standard business cycle co-movements (i.e., induces positive co-movement between
consumption and investment) and explains a large fraction of business cycle fluctuations. Moreover,
when we use measures of TFP which control for variable rates of factor utilization, as for example
when we use the series constructed by Basu, Fernald, and Kimball [2002], we find that our shock
series anticipate TFP growth by several years.
In order to interpret the result from our empirical exercise, we b e gin by presenting a simple
model where fluctuations are driven by surprise changes in productivity as well as a temporary
disturbance– which in our example is a monetary shock. This example allows us to clarify the
extent to which the data on TFP and stock prices have prop erties that run counter to those implied
by models where surprise changes in productive capacity are a central part of fluctuations. We also
present a model where technological innovations only affect productive capacity with delay, and
show how such a model can explain quite easily the patterns observed in the data. In particular,
our evidence suggests that business cycles may be driven to a large extent by TFP growth that is
heavily anticipated by economic agents; thereby leading to what might be called expectation driven
booms. In effect, the original burst in economic activity associated with the shock we identify, using
either the impact or the long run restriction, looks like a business cycle fluctuations which preempts
future growth in productivity. Hence, our empirical results suggests that an important faction of
business cycles fluctuations may be driven by changes in expectations – as is often suggested in
the macro literature – but where these changes in e xpectations may well be based on fundamentals
since they anticipate future changes in pro ductivity.
4

The remaining sections of the paper are structured as follows. In Section 2, we present our
empirical strategy and show how it can be used to shed light of the sources of economic fluctuation.
In Section 3, we present the data and in Section 4 we implement our strategy using post-war US
data. We present our empirical results in steps from a smaller dimensional system – compos ed only
of TFP and stock prices – to a larger system that includes alternatively or jointly consumption,
investm ent and hours. We begin by considering the bi-variate system for TFP and stock prices
since it offers the most straightforward way of highlighting an intriguing property of the data. In
a sec ond stage, we consider a tri-variate system composed of TFP, stock prices and consumption.
The advantage of the tri-variate system is that it allows us to easily embed a standard view about
the sources of fluctuations. We also report results based on a set a four-variable systems in order to
further document the robustness of our results. In Section 5, we discuss the strength and we aknesse s
of different models in explaining he observations presented in Section 4. Finally, Section 6 offers
some concluding comments.
2 Using Impact and Long-Run Restrictions Sequentially to Learn
About Macroeconomic Fluctuations
The object of this section is to present a new means of using orthogonalization techniques –i.e.
impact and long run restrictions – to learn about the nature of business cycle fluctuations. Our
idea is not to use these techniques simultaneously (as is now common in the literature), but is
instead to use them sequentially. In particular, we will want to apply this se quencing to describe
the joint behavior of stock prices (SP ) and measured total factor productivity (T F P
t
) in a manner
that can be easily mapped into structural models . The main characteristic of stock prices that we
want to exploit is that it be an unhindered jump variable, that is, a variable that can immediately
react to changes in information without lag.
5

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Nber working paper series stock prices, news and economic fluctuations" ?

The authors thank Susanto Basu, Larry Christiano, Roger Farmer, Robert Hall, Richard Rogerson, Julio Rotemberg and participants at seminars at CEPR ESSIM 2002, SED Paris 2003, Bank of Canada, Bank of England, the Federal Reserve of Philadelphia, the National Bureau of Economic Research, University of Berlin, Université du Québec à Montréal, Université de Toulouse and CREST for helpful comments. The views expressed herein are those of the author ( s ) and not necessarily those of the National Bureau of Economic Research. 

Q2. What have the authors stated for future works in "Nber working paper series stock prices, news and economic fluctuations" ?

In light of this possibility, the authors now want to go a step further and ask: The authors can focus here on the effect of only the shocks since, as they have shown, they are essentially mirror images of the ̃ shocks. Their first approach to this issue will therefore be to estimate the following truncated moving average representation for different variables J∑ j=0 φ2j 2, t−j + µt ( 17 ) where Z will either be consumption ( C ) or investment ( I ) and where µ a variable-specific disturbance 8Note that the variance decompositions are also very robust to choice of lag length or to estimating system in levels or in VECM form. 

Q3. What are the two series that interest us for their bi-variate analysis?

The two series that interest us for their bi-variate analysis are an index of stock market value (SP) and a measure of total factor productivity. 

Q4. Why does the embodied technological change model tend to produce a negative co-movement?

The reason being that in the phase prior to arrival on line of the more productive capital, the model tends to behave like a one sector neo-classical model subjected to a change in expectations and consequently, it tends to produce a negative co-movement between consumption and investment. 

Q5. What is the type of model that is needed to explain the observations?

In particular, the type of model that is needed to explain the observations is one where agents recognize changes in technological opportunities well in advance of their effect on productivity, and where the recognition itself leads to a boom in both consumption and investment which precedes the growth in productivity. 

Q6. What is the advantage of estimating in levels?

According to Hamilton, estimating in levels has the advantage that the parameters that describe the system’s dynamics are estimated consistently. 

Q7. How do the authors find out that the shock series predicts growth in the business cycle?

when the authors use measures of TFP which control for variable rates of factor utilization, as for example when the authors use the series constructed by Basu, Fernald, and Kimball [2002], the authors find that their shock series anticipate TFP growth by several years. 

Q8. What is the effect of a positive 2 on the economy?

As can be seen in the Figure, a positive 2 has an expansionary impact: investment and consumption increase on impact, and seem to reach a permanently higher level after 10 to 12 quarters. 

Q9. What does the paper show that causes a large fraction of business cycle fluctuations?

The authors also show that these co-linear shock series causes standard business cycle co-movements (i.e., induces positive co-movement between consumption and investment) and explains a large fraction of business cycle fluctuations. 

Q10. What is the lag between stock prices and the increase in TFP?

This long lag between stock prices increase and the increase in TFP is potentially consistent with a delayed impact of technological innovation on productivity, where the diffusion now appears quite20slow while it appeared to be rather quick with a less sophisticated measure of TFP.