imports, exports, dollar exposures, and stock returns

Imports, Exports, Dollar Exposures,and Stock Returns

Suparna Chakrabortyaa, Yi Tangb, Liuren Wua

aBaruch College and bFordham University

April 20, 2012

The Fifth Annual Triple Crown Finance Conference

Liuren Wu (Baruch) Imports, Exports, Dollar Exposures, and Stock Returns 4/20/2012 1 / 19

Overview

We explore three related questions:

1 Are stock returns exposed to exchange rate risk?

How do the exposures differ for different companies?

2 Can we trace the exchange rate exposure to firm fundamentals?

What firm fundamental characteristics explain the cross-sectional differencein exchange rate exposures?

3 Does the exchange rate exposure induce a risk premium for stocks?

Can the linkage with firm fundamental characteristics help enhance theidentification of the risk premium?

by examining the exposures of U.S. industries on a dollar index.


1. Are stock returns exposed to exchange rate risk?

The literature:

Jorion (1990): 287 U.S. multinationals — 15% w. significant exposure.

Amihud (1994): 32 large U.S. exporting firms — not much.

Allayabbis and Ihirg (2001): The exposures on 4 of 18 U.S. industriesare significant.

Our thinking:

Identifying the percentage of companies with “significant” exposure isnot that meaningful.

With dollar appreciates, some companies benefit, some companiessuffer, and some others do not care.

Instead of examining how many companies have “significant” exposureestimates, we examine how the exposure estimates differ acrossdifferent companies.

Are these different exposure estimates purely driven by random errors, or canthey be linked systematically to firm fundamental characteristics?


2. Can we trace the exposure estimates back to firmfundamental characteristics?

The literature: Firms with more international trades should have moresignificant exposures.

Jorion (1990) uses the share of foreign sales in total sales as a measureof openness.

Dominguez and Tesar (2006) uses the aggregate bilateral trade flowswith the U.S. as a measure of openness.

Our thinking: “Openness” is only half the story.

We agree that a company with no international trade shall have littlecurrency exposure.

But a company with balanced imports and exports activities may nothave much exposure, either.

Dollar appreciation may hurt exports, but can benefit imports.

It is not the aggregate openness (imports + exports) that matters, but ratherit is the imbalance (imports - exports) that generates the currency exposure,... when the imbalance is left unhedged.


3. Does the dollar exposure induce a risk premium?

The literature considers two main approaches:

1 The “ICAMP”approach: Time-series regression of returns oncovariance estimates

The identification relies on the time-variation of the covariance term.Issue 1: Individual companies can have strong currency exposures evenif the market portfolio does not.Issue 2: The business of a company (and hence its currency exposure)should not vary that much from month to month, even though thebusiness/exposure can be quite different from company to company.

2 The Fama-MacBeth approach: cross-sectional regression of stockreturns on exposure (beta) estimates.

The good: The regression relies on the cross-sectional difference inexposures, which is larger than the time-series variation.The bad: Rolling-window beta estimates tend to be very, very noisy.

Our approach: We start with the FM approach, but enhance theidentification of the dollar exposures by exploiting its link to firmcharacteristics.


Results overview

1 Regress the stock returns for each of 404 U.S. industries against returns ona broadly defined dollar index, and other risk factors...

The dollar exposure estimates vary widely from industry to industry.

2 Regress cross-sectionally dollar exposure estimates against imports andexports of each industry.

The estimate on total trade (imports+exports) is not significant.

The estimate is highly positive on imports, and negative on exports.⇒ Import-driven companies benefit from dollar appreciation whereasexport-driven companies suffer from it.

3 Regress cross-sectionally future stock returns on dollar exposure estimates.

The average risk premium on the dollar exposure is negative.

The estimate is not significant when the dollar exposure is estimatedpurely from stock return regressions, but becomes significant when weenhance the dollar exposure identification with information fromimports and exports.


Data collection

Our analysis involves data from several sources:

1 The dollar index: Federal Reserve Statistical Release

2 Stock returns: CRSP

3 Stock market risk factors: Kenneth French online data library

4 Imports and exports: Annual U.S. imports and exports data by 4-digit SICcoded industries.

1972-1988: Feenstra, Center for International data at UC, Davis1989-2007: United States International Trade Commission.


The dollar index

The dollar index Monthly returns on the dollar index

71 76 82 87 93 98 04 0930

40

50

60

70

80

90

100

110

120

130

Th

e d

olla

r in

de

x

71 76 82 87 93 98 04 09−5

−4

−3

−2

−1

0

1

2

3

4

5

Mon

thly

retu

rns

on th

e do

llar i

ndex

, %

The dollar index is a weighted average of foreign exchange rate values of thedollar for a large group of major U.S. trading partners.

Industry specific dollar index (Goldberg)Statistical driven dollar index

We use the dollar index to measure the strength of the dollar against thebasket of other currencies.

The index shows two sustained periods of dollar appreciation, followed bytwo periods of depreciation.

Monthly returns on the dollar can be very volatile (±4%).


Cross-sectional v. time-series variation

ln(IM) ln(EX) ln( IMME ) ln( EX

ME ) ER

A. Cross-sectional statistics of time-series averagesMean 5.2 4.9 -0.7 -0.9 1.1Std 1.8 1.8 2.2 1.8 0.8

B. Time-series statistics of cross-sectional averagesMean 5.5 5.2 -1.0 -1.1 0.9Std 1.0 1.0 0.4 0.4 5.8

Imports and exports activities show much larger cross-sectional (fromindustry to industry) variation than time-series (from year to year) variation.

⇒ Firm fundamental variations are better captured cross-sectionally thanover time.

The excess stock returns show much larger time-series variation thancross-sectional variation.

⇒ Large randomness in stock return realization can overwhelm thecross-sectional differences in risk premiums.


Risk exposure estimates

ER it = βi0 + βfx

i ER fxt + βmkt

i ERmktt + βsmb

i SMBt + βhmli HMLt + e i

t ,

βfx βmkt βsmb βhml R2

Mean 0.046 0.970 0.971 0.333 0.391Std 0.667 0.277 0.536 0.472 0.158Minimum -3.557 -0.337 -0.362 -1.629 0.043Maximum 3.095 2.134 3.352 2.350 0.876

The cross-sectional average of the dollar exposure estimates is small,

But the estimates show large cross-sectional variations across 404 industries.

The cross-sectional variation of the dollar exposure is larger than thecross-sectional variation of market, size, book-to-market exposures.


The cross-sectional variation of dollar-exposure estimates

Dollar exposure estimates t-statisics

−4 −3 −2 −1 0 1 2 3 40

50

100

150

200

250

Dollar exposure estimates, βfx

Num

ber

of obse

rvatio

ns

−4 −3 −2 −1 0 1 2 3 40

20

40

60

80

100

120

Dollar exposure t−statistics

Nu

mb

er

of

ob

serv

atio

ns

There is definite cross-sectional variation in the dollar exposure estimates...

Negative for ”steel investment foundaries,” “space vehicle equipment”Positive for “men’s underwear,” “electronic resistors,” “refrigerator”

Are these variations driven purely by random noise ...or do they contain some information about the actual fundamentaldifferences across these industries?


Linking dollar exposures to imports and exports

Cross-sectional regression of full-sample dollar exposure estimates against averageimports and exports of each industry.

Intercept ln( IM+EXME ) ln( IM

ME ) ln( EXME ) ln( IM

EX ) Adj. R2

A. 0.042 0.065 -0.056 1.49%(1.29) (2.84) (-2.11)

B. 0.042 0.020 0.07%(1.29) (1.13)

C 0.042 0.048 0.69%(1.29) (1.94)

The dollar exposure is positively related to imports and negatively related toexports. The relations are highly significant.

The exposure does not have a strong link with the total trade, but has asignificant link to import/export imbalance.

The low R-squared suggests that the return-regression generated dollarexposure estimates are very noisy. The significant link to imports/exportssuggests that they are not all noise.Liuren Wu (Baruch) Imports, Exports, Dollar Exposures, and Stock Returns 4/20/2012 12 / 19

Time-variation in the cross-sectional linkage

Regress 10-yr rolling dollar exposure estimates against 10-yr rolling average ofimports and exports:

84 87 90 93 95 98 01 04 06−0.08

−0.06

−0.04

−0.02

0

0.02

0.04

0.06

0.08

0.1

Dolla

r exp

osur

e dep

ende

nce o

n imp

orts

and e

xpor

ts

ln(IM/ME)

ln(EX/ME)

The link with imports is positive and the link with exports is negative.— The relation is reasonably stable over time.

Import-driven companies benefit from dollar appreciation whereasexport-driven companies suffer from it.

The result makes intuitive economic sense, and suggests that firms do notfully hedge their trade-induced currency exposures.Liuren Wu (Baruch) Imports, Exports, Dollar Exposures, and Stock Returns 4/20/2012 13 / 19

Identifying the dollar exposure and dollar risk premium

SFM. ER it+1 = η0t + ηfxt β

fxit + control + e i

t+1,EFM. ER i

t+1 = η0t + ηfxt(βfxit + λIM ln( IM

ME )it + λEX ln( EXME )it

)+ control + e i

t+1

(control) = ηmktt βmkt

it + ηsmbt βsmb

it + ηhmlt βhml

it

Fama-MacBeth η0 ηfx ηmkt ηsmb ηhml

Standard 0.484 -0.009 0.379 -0.085 0.016(1.38) (-0.09) (1.14) (-0.37) (0.07)

Enhanced 0.485 -0.066 0.351 -0.062 0.038(1.36) (-2.02) (1.04) (-0.27) (0.15)

The dollar exposure induces a negative risk premium on average.

The estimate is not significant when the dollar exposure is estimated purelyfrom return regressions, potentially because the estimates are too noisy.

Adding imports and exports to enhance the exposure identification leads toa significantly negative risk premium estimate on the dollar exposure.


Time-varying dollar risk premium

Exponential-weighted moving average of the monthly risk premium estimates (ηfxt )

82 84 87 90 93 95 98 01 04 06 09−0.1

−0.09

−0.08

−0.07

−0.06

−0.05

−0.04

−0.03

−0.02

−0.01

Curre

ncy

risk

prem

ium

The dollar risk premium becomes increasingly negative over time, potentiallybecause increasing aggregate currency exposure and/or increasing investorawareness of the risk inherent in the currency exposure.

The risk premium also becomes more negative during the two recessions(early 90s and early 2000).


The economic implications

of a negative risk premium on the dollar exposure

Stocks for import-driven companies tend to co-move positively with thedollar index, whereas export-driven companies tend to negatively co-movewith the dollar index.

Positive dollar exposure generates a negative risk premium.— Import-driven companies tend to generate lower average returns thanexport-driven companies.

Dollar appreciation is regarded as a “bad state” for the overall economy.

Companies that positively co-move with dollar can serve as a hedge ofthe bad state and hence investors ask for a lower return for suchcompanies.

The absolute magnitude of the currency risk premium increases duringrecessions, either due to increased risk, or due to increased risk aversion, orboth.


The practical implications

on risk exposure and risk premium identification

The Fama-MacBeth approach is good in exploiting the information in thecross-sectional variation of risk exposures.

The issue is that the risk exposure (beta) estimates from return regressionstend to be very noisy.

The regression suffers from severe errors-in-variable problems.

Textbooks propose various remedies to reduce the noise in market beta:

Averaging within industries, bottom-up methods, etc.

Practitioners (e.g. BARRA) move away from beta estimates to firmcharacteristics, as they can be measured more accurately and rely less ontime-series regressions.

Academic studies (Daniel & Titman (1997), Ang, Hodrick, Xing, Zhang(2009)) also find that it is the characteristics (e.g. size and book to marketratio), not the beta estimates on the SMB and HML portfolios, that predictstock returns.

We propose to combine the information in both...


Concluding remarks

Different U.S. firms have different exposures to the dollar movement.

The exposure difference can be traced back to differences in firmfundamental characteristics.

Stock returns on import-driven firms move with the dollar.

Stock returns on export-driven firms move against the dollar.

These companies do not fully hedge their business risk exposure.

Investors ask for different returns for different dollar exposures.

Investors ask for lower expected returns for companies with positivedollar exposures.

Export-driven companies tend to generate higher returns thanimport-driven companies.

The risk premium becomes more negative during recessions.


What is next?

Combining return regression beta estimates with firm characteristics:

Given the highly noisy nature of beta estimation, should we follow thepractitioners lead in switching to firm characteristics from betaestimation?

(How) can the idea of combining beta estimates with firmcharacteristics be applied to other risk factors?

Why is dollar appreciation bad for the U.S. economy?

Is it specific to the U.S. economy?

Is it a proxy for something more fundamental?

Why export-driven companies generate higher average returns?


imports, exports, dollar exposures, and stock returns

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