Carry Trades by Nonfinancial Firms

Many nonfinancial firms receive revenues in multiple currencies, and control assets in multiple countries.  Foreign exchange risk is thus present on these firms’ income statements and also on their balance sheets, both through the income effects and because the balance sheets of foreign subsidiaries must be translated to the parent company’s home currency in the consolidated balance sheet.

If an arbitrageur observes that risk-free rates in the Euro Area are lower than in the U.S. and wants to engage in a one-year carry trade, he borrows 1€ with maturity of one year, exchanges it into dollars at the spot rate s, which is the value of the Euro in dollars, and contracts to purchase enough Euros to repay his debt in the future, which costs f*(1+rfeur).  He then invests the proceeds at the risk-free U.S. rate for one year by purchasing a Treasury note or a CD.  At the end of one year, he receives s*(1+rfusd) and pays f*(1+rfeur) to settle his futures position. Covered interest parity holds when there is no arbitrage opportunity, or where

f/s = (1+rfusd) / (1+rfeur)

For a corporation that already is exposed to Euro revenues and assets and is trying to decide how to hedge, the choice is between hedging in the forward market and hedging by borrowing:

f/s   versus   (1+r*) / (1+rfeur+spreadeur)

  1. If the firm has no borrowing constraints in the U.S., the only benefit of having the cash translated from the Euro borrowing proceeds is that firm can borrow less in dollars and still maintain its target leverage. In this case, r* = 1+rfusd+spreadusd
      1. If rfeur<rfusd, then adding the spreads lowers the ratio and makes it preferable to hedge using forward contracts, UNLESS the EUR spread is much lower than the USD spread. This can happen in segmented markets, as in Gordon Liao (2017).
      2. If rfeur>rfusd, then adding the spreads raises the ratio and makes it preferable to hedge using debt, UNLESS the EUR spread is much higher than the USD spread.
  2. If the firm is unable to borrow in the U.S., then r* is the rate on the marginal investment, which would not have occurred without the Euro borrowing.
  3. A firm with existing Euro exposure through its revenues and/or assets has another advantage over pure arbitrageurs in that it can avoid some transactions costs.  An arbitrageur has to pay transactions costs both on (1) borrowing and exchanging at the spot rate, and (2) the forward contract.  The firm in this example has to pay transaction costs for (1) or (2), but not both.

Finally, there are reasons why firms might borrow in another currency even if no arbitrage is possible.

  1. Firms might be willing to take on foreign exchange risk in order to access lower borrowing costs.  This is most likely to be true for financially constrained firms.  If the size of the loan (controlling for the firm’s capacity to repay) is correlated with the risk of the loan and if the risk of the loan is correlated with the interest rate charged, then firms can access more capital by borrowing in currencies where the funding costs are lower.
  2. The firm has operational hedges by which it benefits from changes in exchange rates.  For example, a U.S. exporter that competes with European firms in world markets will see higher sales growth if the Euro strengthens.  This at least partially offsets the losses that would occur if the firm borrowed Euros and did not cover its position either with derivatives or with expected future Euro revenues.

Predictions:

  1. When rfeur<rfusd, lower-rated firms are less responsive to changes in interest rates, and higher-rated firms are responsive only if the EUR spread is much lower than the USD spread
  2. When rfeur>rfusd, lower-rated firms are more responsive to changes in interest rates, and higher-rated firms are responsive unless the EUR spread is much higher than the USD spread
  3. Larger firms are more responsive to changes in interest rates, if the cost of hedging using forwards is a convex function of the contract size
  4. Firms with tight financial constraints and good investment opportunities, such as young and small firms with high Tobin’s Q, or firms with high Q and lots of short-term debt, are more responsive to changes in rates.
  5. Firms that compete in world markets are more willing to borrow in the currencies of its competitors, and so are more sensitive to rate changes in those currencies.

Summer Plan 1.0

Brauning and Ivashina (2017a) – U.S. Monetary Policy and Emerging Market Credit Cycles

This paper argues for the following chain of events:

  1. Federal Reserve lowers interest rates (loosens monetary policy)
  2. Global banks’ dollar funding costs fall
  3. Global banks charge lower interest rates on dollar loans
  4. Large firms in emerging markets, who borrow a lot of dollars, borrow more

Therefore, U.S. monetary policy sets credit conditions for foreign firms.

I have a slightly richer story in mind:

  1. Federal Reserve lowers interest rates
  2. Interest rates on dollar loans fall
  3. Non-U.S. firms borrow more in dollars and less in other currencies
  4. The drop in demand reduces interest rates on non-dollar loans

Firms’ percentage of foreign assets, percentage of foreign revenue, and percentage of foreign-currency debt do not match up, leaving residual foreign-currency exposure that firms need to hedge.  For example, Amgen’s 2017 annual report states that the firm has residual foreign-exchange exposure to euros, since it has more euro-denominated revenues than debts. Firms can and do hedge this exposure using derivatives, but they can also do so by taking a short position in the currency through a method: borrowing in the currency.  The latter hedge involves assuming risk (albeit a risk negatively correlated with exiting firm risk), and so should be less expensive than the latter hedge, which involves selling risk.  Nonfinancial firms, then, are in good position to arbitrage nominal interest-rate differences across currencies.

  • compare firms’ % foreign assets to % foreign revenues to % foreign-currency debt
    • by country, by industry, by size
  • systematically explore firms’ use of foreign currency
    • by country, by industry, by size, by currency, by MNC-vs-standalone

If firms do change currency preferences after nominal rate changes, this propagates monetary policy from one country to another.  This channel is different from, but complements, the channel of Brauning and Ivashina (2017a).  It would also predict that U.S. monetary loosening does not only boost the investment of EME firms that rely on dollar funding, but also boosts the investment of EME firms in countries where other firms choose to access dollar funding, even if the firm in question does not.

  • Replicate Brauning and Ivashina (2017a) figure 1,
    • use multiple currencies on the horizontal axis
    • use foreign-currency debt, investment, and employment on the vertical axis
    • repeat for different countries, industries, and firm characteristics
      • size
      • financial constraint
      • mismatch between foreign assets or revenues and foreign-currency debt
      • bond-issuers

 

Foreign Currency Choice

Ilzetzki, Reinhart, and Rogoff (2017) – Exchange Arrangements Entering the 21st Century: Which Anchor Will Hold?

Monetary policy in the country of the “anchor” currency matters more.  Non-anchor countries may wish to stabilize their exchange rates vis-a-vis the anchor country, meaning non-anchor monetary policy is determined by policy in the anchor country.

Countries may choose an anchor currency based on trade ties or on inflation concerns.  They give the example of Argentina choosing the U.S. dollar over the Brazilian real despite Brazil being Argentina’s largest trading partner by far.

The variables these authors look at are

  • invoicing currency
  • currency of foreign reserves
  • explicit currency pegs
  • currency of public debts

They do not directly consider the currency of individual firms’ debts, which is important if the local central bank considers these firms’ needs in addition to the bank’s or the government’s needs.  This matters more if the currency of firms’ debts and the currency of foreign reserves and government debts are not the same.

Opportunity Zones

The recently-passed Tax Cuts and Jobs Act calls for states to establish, and private funds to invest in, low-income “Opportunity Zones” throughout the country.  The plan offers tax incentives for such investment.

This sort of incentive distorts capital allocation by giving preference to investments in less-efficient projects in “opportunity zones”  over investments in more-efficient projects elsewhere, at the margin.  The belief must be that externalities produced by opportunity-zone investment are more positive than externalities created by investments elsewhere, and that the difference is big enough to overcome the effect of the distortions that prevent capital from flowing to its most profitable use.

Potential externalities

  • Real estate values: investment in low-income areas lifts real estate values by a greater amount than investment in higher-income areas.
    • This would help homeowners, but may actually make renters worse off as they are priced even further out of the housing market[1]
    • A short-term boost to a long-lived asset is not a clear boon to the area. In order to capture gains, either the gains would have to be permanent or else homeowners would have to sell, putting downward pressure on home prices and detracting from the long-term economic viability of the region as people move away.
    • For home price increases to be permanent, the investment would have to raise the long-term economic prospects of the region. What are the determinants of long-term economic success?  Can 5- to 10-year investments create these?  I’m thinking about natural resources (minerals, rainfall, dairy herds, etc.), infrastructure, educated population, and some other things I’m probably missing.  If 5- to 10-year investments fail to create these, then the boost to real-estate prices will not be permanent.
    • If new investments temporarily create more demand for housing but the supply curve also quickly shifts out (new construction), then:
      • House prices may not rise much
      • The long-term situation might be even worse than otherwise if the investments don’t create permanent economic growth. When the effect wears off, people will move away again and leave an over-supply of housing.
    • Infrastructure: new investment might spur new infrastructure (roads, airports, sewer systems, etc.) that makes the area more attractive to future business even after the tax incentives under the Opportunity Zone program disappear in 2026.
    • Human capital: new jobs teach workers new skills, which potentially make them better off in the long run whether or not the job is permanent.
      • An investment that creates a new entry-level computer programming job in Silicon Valley is not likely to teach new skills to someone living in Palo Alto – that person probably already has appropriate skills.
      • An investment that creates a new computer programming job in Buhl, Idaho, may confer new skills on someone who would otherwise work in retail or agriculture. If the skills learned are useful in other industries, they might be valuable and this might contribute to the success of the Opportunity Zone program.

Finally, the things everyone likes to talk about–job creation and wage increases–are not the right outcome variables. One region’s additional jobs and wages are another region’s (marginal) losses.  Program success has to be about externalities–real estate values, infrastructure, or skills.

Finally finally, how do we know it’s better to move the businesses to the people instead of moving the people to the businesses?  Maybe the program should invest in helping poor people move from Buhl, ID, to Seattle, WA, where job growth is already happening.

 

[1] The City Council of Redwood City, CA, is worried about exactly this.  See “City wants out of ‘opportunity zone’ designation”, The Daily Journal, April 16, 2018.

Parsing Text in Company Filings

I sat down with Sergey Chernenko in August to ask how to do this, and he walked me through the basics of what he has done before.

  1. EDGAR contains links to “index” files — look for the ones called “master.zip”
  2. Collect the links to the 10-Ks that you want
  3. Write a program that follows the link to download the text file.
  4. Search for key words

Sergey uses Perl, I believe, and uses the “grab” command in a Unix shell.  When doing a search on key words or phrases–especially long words or multi-word phrases–one thing to consider is the possibility that your word or phrase is broken across lines.  If your program reads one line at a time during the word/phrase search, then it will not handle line breaks appropriately.

Problems with Tobin’s Q

Tobin’s Q remains the most-used proxy of “investment opportunities” in financial economics.  Economists typically assume that firms face constant returns to scale, in which case marginal Q and average Q are equal.  Average Q is calculated as the ratio of a firms’ market value to the book value of its assets.

Problem #1: Book value is a poor proxy for replacement cost.  What Q really tries to measure is how much additional future cash flow each additional $1 of investment will produce.  Consider two firms whose only asset is a building, and who each have expected discounted future cash flows (market value) of $1 million.  Firm X bought its building 40 years ago for $1,000, while Firm Y bought its building (which is identical to X’s building) last year for $10,000.  If each firm invests another $1,000, what will happen to their market value?

  • Tobin’s Q calculates that Firm X tends to produce $1,000 of market value per dollar of assets, and so it assumes that Firm X’s market value will increase by $1 million (double its current value).  It implicitly assumes that X can get another building for just $1,000.
  • Likewise, Q calculates that Firm Y will only get one-tenth of a new building, so its market value will only rise by $100,000.
  • This is a well-known problem with Q.  Most researchers either ignore it completely, assuming insignificant differences between firms in asset age, or else try to control for industry and age, assuming that all firms of a certain age within a certain industry group have close-to-identical assets.

Problem #2: market value of the firm (not just of the equity)

  • Another well-known problem surfaces in calculating the numerator of Tobin’s Q, or the market value of the firm.  The market value of a public firm’s equity is a simple matter, but the market value of a firm’s debt is more complicated.  Few firms have publicly-traded debt.
  • About the best anybody does is to simply use the book value of the company’s debt and assume that measurement errors are not correlated with anything important.

Problem #3: forward-looking asset prices

  • The numerator of Tobin’s Q includes the market value of a firm’s equity, which is a forward-looking number that is partially based on investor’s expectations regarding the firm’s future investment.
  • This is mostly a problem when attempting to establish a strong link between Tobin’s Q (“investment opportunities”) and actual investment.
  • Consider two otherwise identical firms in separate parts of the country.  Firm L operates in a labor market that is not expected to have any growth in wages in the future, while Firm K operates in a labor market that is expected to have high wage growth.  Investors expect that Firm L will maximize future revenues by only investing (capx) what is needed to replace worn-out machinery.  Investors expect that Firm K will maximize revenues by replacing worn-out machinery and also replacing labor with capital as wages rise relative to rental rates.
  • The firms will differ in their realized and even in their predicted investment, while Tobin’s Q will not necessarily differ between them.

Why do companies delay payments to their suppliers?

The primary rationale for demanding trade credit (loans from suppliers in the form of buy-now-pay-later schemes), has to do with limited availability of financing.  If you run a small firm, your local may charge you a high rate on loans because it doesn’t know and/or trust you.  You may be too risky.  However, your supplier may have several reasons to give you a better deal: (1) it knows you better than your bank, (2) it worries about the lost sales that would result in your bankruptcy (suppose your small business is fundamentally sound in the long run, but cash is a little tight at the moment), and (3) your supplier hopes you will grow and wants to develop a good relationship now.

For such a “credit transfer” to make sense, the supplier must have lower costs of credit than the customer, and the customer must be financially constrained.

Other theories of trade credit revolve around signals or product and firm quality:

  1. A business may advertise its confidence in the quality of its products by offering to ship weeks or even months before requiring payment, in order to give the customer a zero-risk trial period.
  2. A bank may want to see accounts payable on a loan applicant’s balance sheet as evidence that another informed party is also willing to extend credit.
  3. In addition to Suppliers observe customers’ amounts and frequencies of purchases, and supplier-creditors also observe any changes in the speed of payment.  As a related party with a financial interest in the fortunes of a customer (much like an equity holder), suppliers may be willing to extend attractive credit terms to their customers–even if the supplier bears the cost–in order to gain this information.

None of these theories are able to predict a more recent pattern of increased trade credit borrowing on the part of large customers, who have good credit quality and do not appear to be financially constrained.  Another reason a firm may borrow from its suppliers (begin paying them later) is because it is suffering from late payments from its customers.  A rise in trade credit may be a firm’s way of passing the buck up the supply chain.

To look at more fundamental reasons for an expansion of trade credit, I help mitigate this concern by looking at a sample of 731 firms in industries at the end of the supply chain.  Firms in these industries sell mostly to households (mostly final goods firms), which typically have low bargaining power, and they are also more likely to sell in cash transactions.  From these firms, I identify 29 instance of changes in a firm’s supplier-payment policy, indicated by a dramatic increase in days payables outstanding (DPO).  DPO is the ratio of accounts payable to cost of goods sold multiplied by the number of days in the reporting period, and it roughly captures the average number of days between the transaction in goods/services and the financial transaction.

For each policy change, I measure the firm’s average financial situation for the four quarters preceding the policy change, for each of the 1, 2, 3, 4, and 5 years after the change, and for the entire 5-year post-change period.  I also measure the firm’s percentile within its industry during each of these period, where industry is defined as 4-digit SIC code.  A fragment of Table 10 (below) illustrates the basic findings of this exercise.  By construction, all percentiles fall between zero and one.  Low (high) percentiles are highlighted in red (green).

table10_fragment

A few striking patterns emerge:

  1. Just before instituting a major change in supplier payment policy, these firms tend to pay much faster than their peers, with DPO at the 31st percentile.  This may put them at a disadvantage to their peers who have more working capital financed by their own suppliers.  This may also be a major reason behind the change.  Post-change, these firms pay slower than most of their peers, with DPO at the 70th percentile.
    1. If these firms intentionally overshoot their median peers, it suggests a ratchet effect in trade credit, where no competitor wants to be below average.
  2. A major cross-sectional shift also occurs in the amount of credit these firms extend to their customers.  Before the policy changes, the firms’ DSO (days sales outstanding, or the ratio or accounts receivable to cost of goods sold) is at the 30th percentile, on average.  After the change, it is at the 70th percentile.
    1. Even while the new, slower payment policy may be bad for the firm’s suppliers, it may also be good for the firm’s customers.  If these are households, then this would suggest a wealth transfer from these firms’ suppliers to households.
  3. There is no evidence that these firms are financially constrained before the policy change.  The average policy-changing firm has higher-than-median cash, cash flow, and profitability.  It has relatively low leverage and a very high interest coverage ratio (the ratio of EBITDA to total interest payment).  Modified interest coverage adds the total of short-term debt to the denominator of the coverage ratio, as a sort of “stress test” for situations in which short-term credit become temporarily unavailable.  These policy-changing firms also tend to be much larger than the average or median Compustat firms.  In the years 2008-2016, the average (median) Compustat firm had $2.8 billion ($504 million) in assets, while these firms have an average of $3.7 billion in assets just before the policy change.  This would put the average policy-changer in my sample just above the 80th percentile in Compustat in the past decade.

Debt Issuance During the Crisis

Despite the dramatic spike in credit costs, there was actually a considerable amount of dry powder in the corporate debt markets during the crisis.  According to statistics from Sifma, corporate debt issuance in 2008 (2009) was 36% (17%) lower than the high in 2007.  However, issuance in 2008 (2009) was only 5% lower (25% higher) than in 2005.

test table post

Example table 1
long description that explains everything in the table, including variable construction, differences between the panels, etc.
Panel A: specification 1 results
variable mean t-stat
Δβt-1 0.446*** (2.351)
capx 0.002*** (3.002)
Panel B: specification 2 results
variable mean t-stat
Δβt-1 0.035** (1.985)
capx 2.130** (1.994)