1. Measuring FX Exposure Economic Exposure

2. Economic exposure (EE): EE measures how an unexpected change in S t affect the future cash flows of the firm. Economic exposure is: Subjective. Difficult to measure. Measuring CFs. We can use accounting data (changes in EAT) or financial/economic data (stock returns) to measure EE. Economists tend to like more non- accounting data measures. Note: Since S t is very difficult to forecast, the actual change in S t (e f,t ) can be considered “unexpected.”

3. Measuring Economic Exposure An Easy Measure of EE Based on Financial Data We can easily measure how  CF and  S t move together: correlation. Example: Kellogg’s and IBM’s EE. Using monthly stock returns for Kellogg’s (Kret t ) and monthly changes in S t (USD/EUR) from 1/1994-2/2008, we estimate ρ K,s (correlation between Kret t and s t ) = 0.154. It looks small, but away from zero. We do the same exercise for IBM, obtaining ρ IBM,s =0.056, small and close to zero. ¶ Better measure:1) Run a regression on  CF against (unexpected)  S t. 2) Check statistical significance of regression coeff’s.

4. Testing and Evaluating EE with a regression Steps: (1) Collect data on CF and S t (available from the firm's past) (2) Estimate the regression:  CF t =  + ß  S t +  t,  ß measures the sensitivity of  CF to changes in  S t.  the higher ß, the greater the impact of  S t on CF. (3) Test for EE  H 0 (no EE): β = 0 H 1 (EE): β ≠ 0 (4) Evaluation of this regression: t-statistic of ß and R 2. Rule: |t β = ß/SE(ß)| > 1.96 => ß is significantly different than zero at 5% level. For companies listed on exchanges, we can use stock prices instead of CFs & stock returns instead of changes in CFs.

5. Example: Kellogg’s EE. Now, using the data from the previous example, we run the regression: Kret t =  + ß e f,t +  t R 2 = 0.023717 Standard Error = 0.05944 Observations = 169 CoefficientsStandard Errort-Stat P-value Intercept (α) 0.0039910.0046370.8607560.390607 e f,t (β) 0.5510590.2735892.0141850.045595 Analysis: We reject H 0, since |t β = 2.01| > 1.96 (significantly different than zero). Note, however, that the R 2 is very low! (The variability of s t explains less than 2.4% of the variability of Kellogg’s returns.) ¶

6. Example: IBM’s EE. Now, using the IBM data, we run the regression: IBMret t =  + ß e f,t +  t R 2 = 0.003102 Standard Error = 0.09462 Observations = 169 CoefficientsStandard Error t-StatP-value Intercept (α)0.0162830.0072972.2314390.026983 e f,t (β) -0.203220.2819-0.720890.471986 Analysis: We cannot reject H 0, since |t β = -0.72| < 1.96 (not significantly different than zero). Again, the R 2 is very low. (The variability of s t explains less than 0.3% of the variability of IBM’s returns.) ¶

7. Sometimes the impact of  S t is not felt immediately by a firm.  contracts and short-run costs (short-term adjustment difficult). Example: For an exporting U.S. company a sudden appreciation of the USD increases CF in the short term. Run a modified regression:  CF t =  + ß 0  S t + ß 1  S t-1 + ß 2  S t-2 + ß 3  S t-3 +... +  t. The sum of the ßs measures the sensitivity of CF to  S t. Practical issue: Number of lags? Usual practice: Include at most two years of information.

8. Example: HAL runs the following regression.  CF t =.456 +.421  S t +.251  S t-1 +.052  S t-2.R 2 =.168. (.89)(2.79) (2.01) (0.77) HAL's HKD CF (in USD) sensitivity to  S t is 0.672 (.421+.251).  a 1% depreciation of the HKD will increase HKD CF (translated into USD) by 0.672%. ¶ Note on regressions to measure EE e t,t is not the only variable affecting CFs/returns of a company. A company grows, adds assets, then higher sales and EPS are expected. Also, the economy and the stock market grow over time. We need to “control” for these other variables, to isolate the effect of e f,t. A multivariate regression will work: Besides e t,t include other “control” independent variables (income growth, inflation, sales growth, etc.

9. We can also borrow from the investments literature and use the three popular Fama-French factors (Market, Size (SMB), Book-to-Market (HML)) as controls. Then, we run a multivariate regression: Stock Return t = α + β e f,t + δ 1 Market Return t + δ 2 HML t + δ 3 SMB t + ε t Evidence: The above regressions have been done repeatedly for firms around the world. (Without the FF factors, we have already done it for Kellogg and IBM.) On average, for large firms (MNCs) EE is small –i.e., β is small- and not significant at the 5% level. See recent paper by Ivanova (2014).

10. A Measure Based on Accounting Data It requires to estimate the net cash flows of the firm (EAT or EBT) under several FX scenarios. (Easy with an excel spreadsheet.) Example: IBM HK provides the following info: Sales and cost of goods are dependent on S t S t = 7 HKD/USDS t = 7.70 HKD/USD Sales (in HKD)300M400M Cost of goods (in HKD)150M200M Gross profits (in HKD)150M200M Interest expense (in HKD)20M20M EBT (in HKD)130M180M EBT (in USD at S t =7) : HKD 130M/7 HKD/USD = USD 18.57M EBT (in USD at S t = 7.7): HKD 180M/7.70 HKD/USD = USD 23.38M

11. Example (continuation): A 10% depreciation of the HKD, increases the HKD cash flows from HKD 130M to HKD 180M, and the USD cash flows from USD 18.57M to USD 23.38. Q: Is EE significant? A: We can calculate the (pseudo) elasticity of CF to changes in S t. For example, in USD, a 10% depreciation of the HKD produces a change of 25.9% in EBT. Quite significant. But you should note that the change in exposure is USD 4.81M. This amount might not be significant for IBM! (Judgment call needed.) ¶ Note: Obviously, firms will simulate many scenarios to gauge the sensitivity of EBT to changes in exchange rates.