CESSA WP 2018-01

Determinants of firm exchange rate predictions:Empirical evidence from survey data of Japanese firms

Thi-Ngoc Anh NGUYEN
Yokohama National University
Japan Society for the Promotion of Science
May 2018

Center for Economic and Social Studies in Asia (CESSA) Working Paper
Downloadable from:http://www.econ.ynu.ac.jp/cessa/publication/workingpaper.html

Center for Economic and Social Studies in Asia, Department of Economics
Yokohama National UniversityDeterminants of firm exchange rate predictions:Empirical evidence from survey data of Japanese firms

Thi-Ngoc Anh NGUYEN *†May 2018

Abstract
This letter uses Bank of Japan’s Tankan survey data covering 10 manufacturing industries
to investigate the characteristics as well as the determinants of firms’ exchange rate predictions.
Evidence of asymmetry is found regarding the way firms adjust their exchange rate predictions
in response to different currency regimes. Japanese exporting firms are more likely to update
their prediction of future exchange rates in response to unexpected yen appreciation than tounexpected yen depreciation. Furthermore, firm exchange rate prediction updating decisions
are affected by other firm characteristics such as export dependency and profitability.
JEL classification: D22, D84, F31, G13
Keywords: Exchange rate pass-through (ERPT), pricing-to-market (PTM), exchange rate
expectation, Japanese export, prediction adjustment speed, Tankan data*

This study is financially supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI
Grant Number 16J11988.
† Graduate School of International Social Sciences, Yokohama National University and Research Fellow of
the Japan Society for the Promotion of Science (JSPS). Email: nguyen.thingocanh208@gmail.com
01. Introduction
Exchange rate expectations have long been an important research topic in international
macroeconomics; yet, the question of how market participants predict future exchange ratelevels remains unresolved. A few studies only, such as those by Ito (1989) and Takagi (1991),
have used survey data to analyze this question empirically. Accordingly, the limited data
availability on expected future exchange rates has prevented further rigorous examination ofthis unresolved research question.
Regularly, the Bank of Japan (BOJ) publishes firm predictions of the yen/ US dollar
bilateral normal exchange rate when export by firm size and by industry, which they obtain byconducting large-scale surveys of Japanese firms. 1 Figure 1 presents the exchange rate
prediction error for both large-size and small-size firms as well as the actual (quarterly
averaged) yen/ US dollar bilateral nominal exchange rate, where the prediction error is definedas the differences between the actual and predicted exchange rate. It can be observed that themagnitude of the prediction error is larger with a longer duration in shaded yen depreciation
periods than in non-shaded yen appreciation periods. 2 In addition, in most periods, the graphof large-size firms (black x marked line) is above that of small-size firms (red dotted line)
implying that prediction errors by large-size firms are larger (smaller) than those of small-size
firms during yen depreciation (appreciation) periods. Thus, questions are raised about why the
degree of prediction errors differs (1) between yen appreciation and depreciation periods, and(2) depending on firm size.
This letter aims to use the BOJ’s Tankan survey data to reveal the characteristic as well as
the determinants of firm-level exchange rate predictions. The analysis revealed that, first,
Japanese firms adjust their exchange rate prediction errors more quickly (slowly) in yen
appreciation (depreciation) period. Second, the adjustment speed of exchange rate prediction
is positively correlated with a firm’s degree of export dependency in the yen depreciation
period. Third, in contrast, the adjustment speed is found to have negative relationship with afirm’s profitability performance in the yen depreciation period, with small-size firms tending
to increase the adjustment speed in response to an unanticipated depreciation of the yen.
The letter is organized as follows. Section 2 investigates the characteristics of exchange
rate prediction and adjustment speed. The relationship between the adjustment speed and firm

1

BOJ’s Short-Term Economic Survey of Enterprises in Japan (TANKAN) is published in every quarter. As
of March 2018, for example, the BOJ sent out questionnaires to 10,020 firms and presents the averaged
results by three types of firm size (large, medium-sized, and small firms) and by industry (17 manufacturing
and 14 non-manufacturing industries).
2 In this letter, the criteria of dividing currency regimes are based on prediction errors of exchange rates, i.e.,
the period is counted as yen depreciation period if the yen depreciates more than firms’ expectation(prediction error >0) and yen appreciation otherwise. In fact, this currency regime specification is reasonable
when comparing with the movement of actual yen/ US dollar exchange rate (blue solid line) in Figure 1.
1characteristics is analyzed empirically in Section 3. Section 4 concludes.
2. Adjustment speed of exchange rate prediction
The adjustment speed ( α t ) of exchange rate prediction is calculated by dividing the change
in firm’s predicted exchange rate by the exchange rate prediction error in the previous period:
αt = (Et st+1 -Et-1 st )⁄(st -Et-1 st ), where st is the natural log of bilateral nominal exchange rate

at t period, and Et is an expectation operator conditional on all available information at t
period. Although firms use all the available information to predict the future exchange rate, it
is impossible to establish perfect expectations. Thus, the hypothesis proposed here is that firms
tend to determine the predicted exchange rate conservatively when making an export plan. The
additional hypothesis is that the magnitude of prediction errors depends on firm characteristics
such as export dependency and profitability.
To test the above hypothesis, the adjustment speed, α t , which measures how quickly firms
update their predicted exchange rate by observing current (realized) exchange rate, is estimated.
To allow for possible asymmetric prediction of future exchange rates, the following thresholdregression model is established:

Et st +1 − Et −1 st = I × α1 (st − Et −1 st ) + (1 − I ) × α 2 (st − Et −1 st ) + vt +1(1)

where the dummy I takes the value of 1 (yen depreciation regime) if the prediction error ispositive, and takes 0 (yen appreciation regime) otherwise.
Quarterly data from 1997Q2 to 2016Q1 for three firm sizes in 10 manufacturing industries
are used for the above estimation. 3 Table 1 presents the results of α1 and α 2 as well as theresults for the Wald-test for the null hypothesis α1 = α 2 . An asymmetric pattern of adjustment
speeds can be observed among almost all large and medium enterprises and in four out of 10
industries for small enterprises, where the adjustment speed is higher in the yen appreciation
regime than in the yen depreciation regime.
One explanation for regime-dependent adjustment speed could be that exporting firms
might need to revise their export plan quickly in response to an unexpected appreciation of the3

Following Nguyen and Sato (2017), this letter constructs the dataset from BOJ’s Tankan data. Althoughdata on 17 manufacturing sectors are available, as footnote 1 shows, we finally chose “all manufacturing”
and nine manufacturing industries. See the footnote of Table 1 for industry information. Actual yen/ US
dollar bilateral nominal exchange rate (quarterly average) st is taken from the IFS.
2

yen, especially when they use foreign currency invoicing in their exports. 4 In contrast, during
the yen depreciation regime, Japanese firms could enjoy unexpected exchange gains, which
provides less incentive to change their export plan and, hence, to revise their predictedexchange rate.
3. Determinants of adjustment speed
Next, we investigate what factors determine the adjustment speed. The BOJ provides
notable data of forecasted (planned) export amounts, total sales, and ordinary profits asobtained from their Tankan surveys. These data are used to calculate the “forecasted export
ratio” as well as the “forecasted profit ratio”. 5 To investigate the effect of firm size on
adjustment speed, firm-size dummies and their interactions with explanatory variables areincluded in the following fixed-effect model estimation:
spd ijt = c + ∑ Si + α ⋅ exp_ ratioijt −1 + ∑ α i ⋅ Si × exp_ ratioijt −1 +ii

+ β ⋅ profit _ ratioijt −1 + ∑ β i ⋅ Si × profit _ ratioijt −1 + γ ij + ηt + ε ijt(2)iwhere spdijt , exp_ ratioijt , and profit _ ratioijt denote the adjustment speed, export ratio, andprofit ratio of i firm-size-level in industry j at time t, respectively. Si denotes dummies for large
or small enterprises. Fixed effects of industry-firm-size and time are captured by γ ij and ηt ,respectively. Prediction error, ert , is used to distinguish currency regimes. The sample period
ranges from 1999Q2 to 2016Q1.
A firm’s export ratio has two conflicting effects on its adjustment speed. One effect is an“exposure effect” that occurs when the export ratio is treated as a proxy for firms’ exchange
rate exposure. Given the assumption of a positive relationship between the adjustment speed
and ERPT, the sign on α is expected to be positive. 6 The other effect is a “market share

4

As shown by Ito et al. (2018), Japanese firms tend to invoice exports in the destination currency (mainly
in US dollars) when exporting products to advanced countries. Even for exports to Asian countries, the shareof US-dollar-invoiced exports is higher than the share of yen-invoiced exports. Given such a large share offoreign currency invoicing in exports, Japanese firms shoulder foreign exchange loss when the yen
appreciates unexpectedly.
5 Export (profit) ratio is calculated as the ratio of firm’s export (ordinary profit) amount compared with total
sales amount. The two ratios’ correlation coefficient is 0.33.
6 Floden et al. (2008) shows that higher exchange rate exposure leads to a higher ERPT level.
3effect” that arises when the export ratio is considered as a proxy for firms’ sales dependency
on the international market. The fact that firms depend heavily on overseas sales might givethem an incentive to maintain (boost) their market share by decreasing (increasing) the level of
ERPT and hence to slowly (quickly) revise exchange rate predictions when the yen appreciates(depreciates). 7 Therefore, taken together, α is expected to be positive when the yen
depreciates, but remains ambiguous when the yen appreciates.
The profit ratio is used to proxy for firms’ business performance and is highly related to afirm’s mark-up level. Depending on that firm’s export competitiveness, the relationshipbetween firm profitability and adjustment speed can be either positive or negative. 8
Columns (4)–(6) in Table 2 reports the results of regression (2). 9 The coefficients of boththe export and profit ratios are found to be significant in the regime-free as well as yen
depreciation regime, but not in the yen appreciation regime.
In the yen depreciation regime, the coefficient of the export ratio is significantly positive
as expected, with 1% increase in the export ratio leading to a 11% higher adjustment speed.
However, firm size itself does not have a significant effect on adjustment speed, indicating alinear effect of the export ratio on adjustment speed among all types of enterprises. Meanwhile,
the profit ratio shows a strongly significant nonlinear relationship with the adjustment speed in
the yen depreciation regime. For benchmark medium-size enterprises, profitability negatively
affects adjustment speed, whereas the interaction between firm size and profitability issignificantly positive for small-size enterprises, making the point estimate 34.004. Thus, thesmaller the firm size, the more likely it is that firms with high profitability will increase theiradjustment speed in the yen depreciation regime.
As discussed earlier, firms with high profits tend not to revise their predictions of futureexchange rates quickly in response to an unexpected depreciation of the yen. However, thesmall-size firms, often regarded as “less competitive,” are likely to have a stronger incentive
to update their predicted exchange rate to decrease their export price, thereby improving theirexport price competitiveness during the yen depreciation regime. This finding is consistent
with the empirical results of Berman et al. (2012) and Nguyen and Sato (2017), which
demonstrated an asymmetric relationship between firm business performance and ERPT.
4. Concluding remarks

7

Krugman (1987) and Knetter (1993) discuss possible ERPT asymmetry due to market power.
Berman et al. (2012), Nguyen and Sato (2017) suggest a nonlinear relationship between firm performance(or industry competitiveness) and ERPT.
9
For reference, the results of the model without firm-size dummies (model (3)) are reported in column(1)–(3) of Table 2. Only the export ratio coefficients are found to be significant in regime-free and yen
depreciation regime. spd ijt = c + α ⋅ exp_ ratioijt −1 + β ⋅ profit _ ratioijt −1 + γ ij + η t + ε ijt (3)84Using the BOJ’s Tankan data, we have investigated empirically the characteristic anddeterminants of firm exchange rate predictions. The novel findings are three-fold. First,
Japanese exporting firms are more likely to update their exchange rate predictions in response
to unexpected yen appreciation than in response to unexpected yen depreciation. Second, theperformed panel estimation demonstrated that a firm’s export dependency, as well as its
profitability, has significant effect on the speed of updating predicted exchange rates in
response to an unexpected yen depreciation. Finally, unlike the negative effect of profitability
on prediction updating found among large- and medium-size firms, small-size firms tend to
update their prediction of future exchange rates in response to unexpected yen depreciation,
which suggests that firm size, often associated with firm export competitiveness, does matterin the prediction of future exchange rates. These empirical findings will motivate further
research on firm-level predictions of future exchange rates.

5References
Berman. N., Martin, P., Mayer, T., 2012. How do different exporters react to exchange ratechange? Q. J. Econ., 127, 437–492.
Flodén, M., Simbanegavi, W., Wilander, F. 2008. When is a lower exchange pass-throughassociated with greater exchange rate exposure. J. Int. Money Finance, 27, 124–139.
Ito, T., 1990. Foreign exchange rate expectations: micro survey data. Am. Econ. Rev., 80, 434–
449
Ito, T., Koibuchi, S., Sato, K., Shimizu, J. 2018. Managing Currency Risk: How Japanese Firms
Choose Invoicing Currency, UK: Edward Elgar, in press.
Knetter, M. M., 1993. International comparisons of price-to-market behavior. The Am. Econ.
Rev., 83, 473–486
Krugman, P., 1987. Pricing to market when the exchange rate changes, in: Arndt S.W.
Richardson J.D. (Eds.), Real-Financial Linkages among Open Economies, Cambridge:
MIT Press.
Nguyen, A. T. N., Sato, K., 2017. Firms’ predicted exchange rates and nonlinearities in pricingto-market presented at 2017 Asian Meeting of Econometric Society, The Chinese
University of Hong Kong, Hong Kong, June 3–5.
Takagi, S., 1991. Exchange rate expectations: A survey of survey studies. IMF Economic
Review Staff Papers, 38, 156–183.

6

Figure 1: Prediction error of large and small Japanese manufacturing exporters

Note: Prediction error is calculated as the difference between natural log of the actual exchange
rate and natural log of the predicted exchange rate, ert = ln st − ln Et −1 st , indicating thedeviation rate of the realized exchange rate from the expected one. “Error (Large Firms),”“Error (Small Firms)” are calculated using the expected exchange rate for large- and small-size
firms, respectively. The shaded (non-shaded) yen depreciation (appreciation) periods are
periods in which the prediction errors are larger (smaller) than 0.
Source: Bank of Japan, Short-Term Economic Survey of Enterprises in Japan (TANKAN); IMF,
International Financial Statistics.

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Table 1: Adjustment speed of exchange rate predictions to an error in the previous period
Firm-size
level

Large enterpriseMedium enterprise

Small enterprise

Industry

Depre.

Appre.

Symmetry

Depre.

Appre.

Symmetry

Depre.

Appre.

Symmetry

Manufacturing0.351***

0.632***

0.000***

0.357***

0.542***

0.000***

0.397***

0.480***

0.104(0.026)(0.052)(0.023)(0.037)(0.030)(0.041)0.431***

0.573***

0.442***

0.578***

0.509***

0.611***(0.038)(0.527)(0.031)(0.042)(0.046)(0.062)0.381***

0.573***

0.386***

0.565***

0.345***

0.510***(0.030)(0.049)(0.034)(0.053)(0.032)(0.058)0.478***

0.621***

0.354***

0.543***

0.480***

0.532***(0.045)(0.063)(0.040)(0.066)(0.053)(0.065)0.377***

0.590***

0.419***

0.613***

0.500***

0.586***(0.041)(0.071)(0.032)(0.049)(0.047)(0.056)0.370***

0.523***

0.278***

0.673***

0.256***

0.771***(0.039)(0.060)(0.031)(0.077)(0.047)(0.109)0.335***

0.656***

0.345***

0.573***

0.380***

0.628***(0.032)(0.069)(0.025)(0.046)(0.034)(0.063)0.251***

0.651***

0.312***

0.623***

0.396***

0.513***(0.049)(0.137)(0.027)(0.054)(0.031)(0.047)0.326***

0.699***

0.256***

0.536***

0.373***

0.352***(0.034)(0.076)(0.025)(0.051)(0.054)(0.052)0.367***

0.534***

0.410***

0.370***

0.328***

0.275***(0.034)(0.057)(0.041)(0.045)(0.058)(0.060)

TextileChemicalIron and steel
Non-ferrous metal
Processed metal
GeneralElectric
Transport
Other0.031**
0.001***
0.014**
0.011**
0.036**
0.000***
0.007***
0.000***
0.014**

0.011**
0.006***
0.017**
0.001***
0.000***
0.000***
0.000***
0.000***
0.518

0.187
0.015**
0.539
0.241
0.000***
0.001***
0.041**
0.784
0.527

Note: (a) “Manufacturing,” “General,” “Electric,” “Transport,” and “Other” denotes “Allmanufacturing industries,” “General machinery” (including the three sectors of “Generalpurpose machinery,” “Production machinery,” and “Business oriented machinery”), “Electricalmachinery,” “Transportation machinery,” and “Other manufacturing,” respectively. (b) “Depre.”and “Appre.” denote the yen depreciation and appreciation regimes, respectively. (c)“Symmetry” indicates p-values of the Wald test for the hypothesis of equality in adjustment
speed ( α1 = α 2 ) in the two regimes.

8

Table 2: Relationship of adjustment speed with other firm characteristics.
dependent var: adjustment
speed
exp_ratio
profit_ratio

no regime(1)

depreciation(2)

appreciation(3)

no regime(4)

depreciation(5)5.462**(2.543)9.532***-2.7886.955**

11.152***-1.590(2.982)(4.187)(3.472)(4.145)(5.657)-8.624-7.355-1.378-17.628**-25.715**-6.280(5.589)(6.503)(9.297)(8.252)(10.183)(12.942)large*exp_ratio
small*exp_ratio
large*profit_ratio
small*profit_ratio
Constant-1.592-1.325-3.406(5.098)(5.937)(8.463)-2.079

0.390

1.427

(6.858)(9.024)(10.011)10.38319.5387.562(9.694)(12.063)(15.004)

25.820**59.719***8.647(12.122)(16.058)(17.962)-0.590-1.482*

2.378

0.073

-0.5044.025(0.841)(0.899)(3.378)(0.840)(0.878)(3.417)large enterprisesmall enterprisetime-fixed effectcross-section fixed effectdegree of freedoms
R-squared
No. of obs

appreciation
(6)

YES
YES
1908
0.067
2006

YES
YES
1074
0.232
1159

YES
YES
771
0.128
847

-0.681-0.889-1.430(1.066)(1.211)(1.823)-1.099-1.440-3.061*(1.031)(1.294)(1.585)

YES
YES
1904
0.069
2006

YES
YES
1070
0.242
1159

YES
YES
767
0.129
847

Note: */**/*** denotes significance at 10%, 5% and 1%, respectively.

9