The Mystery of Zero-Leverage Firms

Strebulaev and Yang (working paper, 2013)

A large percentage of publicly-owned U.S. firms (14% in 2000) have zero or almost-zero leverage, and this phenomenon is not confined to just a few years.  Zero leverage as a corporate policy appears to be persistent, and is not explained either by industry or by firm size.

In addition, many zero-leverage firms pay dividends, so it is not the case that this is driven by growth firms choosing zero leverage to avoid paying out earnings.

Compared to similar firms matched on size and industry, zero-leverage firms that pay dividends:

  • pay higher dividends
  • have higher cash balances

One potential explanation is an agency story where the manager prefers zero leverage, even if the shareholders may not.  This story finds support in the empirical findings that zero leverage is more likely in

  • firms with higher CEO ownership
  • firms with less independent or more CEO-friendly boards
  • family-owned firms.


How Stable Are Corporate Capital Structures?

Harry DeAngelo and Richard Roll (2008 JF)


  • Lemmon, Roberts, and Zender (2008) and others make the argument that the cross-section of corporate capital structure is quite stable over long horizons.  LRZ show that, among a selection of determinants that are believed to be linked to capital structure, firm fixed effects are by far the most powerful predictor.  They also sort firms into leverage quartiles, and show that the high- (low-) leverage portfolios at time t also have the highest (lowest) leverage as much as 20 years into the future.
  • One interpretation, bluntly stated, is that 50 years of capital structure research has been barking up the wrong tree.  Researchers should go “back to the beginning” and rethink their approach.
  • Another interpretation of these findings is that the cross-section is stable over time, and therefore not interesting.  Researchers should confine themselves to explaining within-time variation in capital structure.
  • DeAngelo and Roll (this paper) present compelling arguments that (1) LRZ’s analysis masks variation of the cross-section over time, and so is somewhat biased toward finding stability and (2) capital structure at the firm level is wildly unstable, and the evolution of the cross-section is at least as interesting a research topic as the snapshot at each point in time.

Methods and Findings

  • The sample consists of 15,096 CRSP/Compustat firms over 1950-2008.
  • The authors test for firm-level stability by measuring the length of each firm’s “stable leverage regimes” – i.e. a period of time where the firm’s leverage stays within a narrow band of 0.05, 0.1, or 0.2.
    • Among firms that are in the sample for at least 20 years, only 20% have stable (using the bandwidth definition of 0.05) leverage for a ten-year period, only 4% have stable leverage for a 20-year period, and the median length of firms’ “longest stable regime” is only 6 years.
    • Among firms that are in the sample for the entire 59-year period, 52% have stable leverage (again, bandwidth of 0.05) for some 10-year period, 0% have stable leverage for a 40-year period, and the median length of “longest stable regime” is only 10 years.
  • They then show that, during firm’s stable regimes, leverage tends to be quite low, often less than 0.1.
  • A significant portion of the paper is dedicated to explaining why the results in Lemmon, Roberts, and Zender (2008) and MacKay and Phillips (2005) are misleading.
    • Use a creative specification that allows firm-time fixed effects with firm-time observations.  A textbook application of this would result in one fixed effect for each observation and tells the researcher absolutely nothing.
      • Following Scheffé (1959), they get around this by imposing some additional structure. They assume that firm-time interaction effects are stable over longer periods – they arbitrarily choose 10 years – so that they run regressions with firm-decade fixed effects.
        • Using firm-decade interactions significantly improves R-squared over a specification with firm fixed effects only.
        • ANOVA reveals that
    • DeAngelo and Roll (this paper) use a longer sample than the papers they criticize.  This is important, because if firm leverage changes slowly (this can be represented by adjustment costs to leverage), then the power of firm fixed effects will be overstated in short samples.  The problem is similar if many firms only appear in the sample for a few years.
      • This is also important because, as these authors argue, the LRZ sample begins in 1970 – after economy-wide increases in leverage as firms took advantage of post-war investment opportunities.  Thus, the later sample misses crucial time-series variation in leverage.
  • In another set of analyses, DeAngelo and Roll show that the cross-section varies meaningfully over time.
    • They measure the correlation between several pairs of cross-sections – the pairs {t,t+1}, {t,t+2},…,{t,+40}.
      • The correlation falls quickly from 0.8 for {t,t+1} and approaches zero.
    • They show that firms in one leverage quartile at time t  moves across quartiles a lot in the ensuing years.  LRZ miss this because they look at average leverage of quartile groups.
  • The final part of the paper tests various theories of capital structure: (1) Miller’s (1977) random variation, (2) speed-of-adjustment (SOA) models, (3) flexible target ratio models, and (4) time-varying target (TVT) models.
    • They simulate a model that nests all these as special cases, and see which model(s) appear to best fit the data.
    • The TVT and flexible-target models seem to be the best, while Miller’s random leverage model is not supported.

The authors’ primary conclusion is that the cross-section is far from stable, and that within-firm variation of leverage over the time series is probably a response to the firm’s investment needs.


  • I liked this paper.  It documents new facts about the variability of firm-level debt ratios over time, and uses creative analysis.
  • The paper also leaves many questions unanswered.  It doesn’t explain why firms have such low leverage during stable regimes (though DeAngelo, Stulz, and Gonçalves currently have a working paper that tries to answer this question).  It doesn’t explain why, if macro factors (post-war environment) are so important, the cross section still changes so much from year to year.
  • Sample-selection bias is an accusation commonly made to papers whose data start after the post-war period.  David, Fama, and French (2000) made a similar criticism of Daniel and Titman’s (1997) argument that firm characteristics matter and risk-factor loadings don’t.  But it is not clear that the post-war decades are relevant for all analyses.
    • Fama’s and French’s three-factor asset pricing model seeks to explain only cross-sectional difference in returns, and is not concerned with how the cross-section changes over time.  High-return stocks in any period have different risk-loadings, but the risk loadings are allowed to change over time in an unspecified manner.  In this case, extending the sample backwards should be safe.
    • But this paper is concerned with how the cross-section varies over time.  Firms’ “wholesale abandonment of conservative capital structure” in the 1950s and 1960s, as they took advantage of (potentially very rare) investment opportunities may not tell us much about how they have managed their debt ratios in the last 40 years.

Input Specificity and the Propagation of Idiosyncratic Shocks in Production Networks

Barrot, Jean-Noel, and Julien Sauvagnat, QJE (2004).


Do firm-level shocks propagate in production networks?


  • Use natural disasters as the exogenous shock to suppliers
  • Compare firms whose suppliers are in disaster areas with firms who do not have any suppliers in disaster areas.
  • Use three measures of asset specificity:
    • Rauch’s (1999) classification of the extent to which goods are traded on markets
    • Supplier’s R&D expenditures
    • Supplier’s number of patents
  • Regress customer change in sales when a specific supplier suffers a shock
  • Regress (non-disaster) supplier change in sales when a customer has another specific supplier that suffers a shock.
  • Control for firm characteristics, number of suppliers, and fiscal-quarter fixed effects.
  • Take appropriate consideration for
    • Customers who are affected by the same disaster as their supplier
      • exclude observations where customer and supplier are less than 300 miles apart
    • the fact that customers may select their suppliers based on natural disaster risk
      • this would bias against finding any results
    • customers lose sales through some other channel – e.g., if their own customer base is in the same area as the supplier
      • find that there are not results when the customer-supplier link is inactive


  • Data on non-financial firms with headquarters in the U.S. between 1978-2013
  • Firm characteristics and location (county) from Compustat
  • Regulation SFAS 131 requires firms to report the names of customers accounting for more than 10% of segment sales (starting in 1978)
    • Supply chain from Compustat Segment data – match customer names to Compustat by string-matching and by hand
  • Disaster date and location data from SHELDUS (Spatial Hazard and Loss Database for the U.S.) at the University of South Carolina
  • Data on supplier specificity
    • Recreate Rauch’s (1999) measure of input specificity based on SIC-code
    • R&D data from Compustat
    • patent data from Kogan, et al. (2012), retrieved in turn from Google patents


  • When a supplier’s location is hit by a natural disaster, customers’ sales growth falls by 2 to 3 percent and their equity value falls by 1%.
    • This effect is not observed when the supplier and customer do not have an active relationship at the time of disaster.
    • This effect is only observed when the supplier supplies a specific input.
    • $1 of lost supplier sales leads to $2.4 of lost customer sales – shocks propagate.
  • Suppliers who are not hit by a disaster, but which have a customer who has another supplier that is in a disaster zone, are also negatively affected.


  • This paper looks at the effect of supplier shocks on customers and on other suppliers.  The authors also look at the impact of customer shocks on suppliers, but not at the effect of customer shocks on specific suppliers, which they should have done in a paper about asset specificity.  The position this as a supply-side story, but they do not fairly consider the demand-side.
  • Something else the authors could do to support their story is to look at inventory.  The basic story is that when a specific supplier is hit by a disaster, the supply of a specific input falls, so the (customer) firm has no choice but to cut production, leading to lost sales.  By effectively using sales as a measure of production, they assume that (1) inventory levels are fixed and (2) the relative sales price of the customer’s final good is fixed.  If, as I would expect, customers deplete inventory to make up for lost production, then the affect of the supplier shock is even bigger than this paper shows.
  • The authors probably want to look at lost sales to see the effect of shocks on firms’ bottom line.  If pre-shock inventories were optimal, than any decrease in inventory has a cost associated with it, as well.  My point is that there may be, and probably are, other consequences beyond lost sales.  How do the lost sales compare in magnitude to the declines in market cap for these companies?

Efficient Capital Markets, Inefficient Firms: A Model of Myopic Corporate Behavior

Stein, Jeremy, “Efficient Capital Markets, Inefficient Firms: A Model of Myopic Corporate Behavior,” The Quarterly Journal of Economics (1989), 655-669.

Claim: Managers will invest myopically, even when they care about stock price and even when markets behave rationally.


  • Stock markets determine company valuation based on current earnings.
  • Managers have an incentive to forgo profitable investments now in order to boost earnings (cash flow) now.
  • Market are rational, so they will expect managers to do this.
  • Managers who care about stock prices will be trapped into behaving myopically, since they will be penalized if they do not boost earnings now.
  • In a steady-state “signal-jamming” model, managers will inflate earnings by “borrowing” from the future (i.e. not investing optimally), and markets will correctly estimate this borrowing.
  • Capital market pressure determines the strength of this phenomenon, and may take one of the following forms:
    • Threats of takeover (when a firm’s stock price is low)
    • Lack of financial slack (the ability to undertake investments without issuing new stock)
    • Distance between the firm and its creditors (creditors who are intimate with a firm will rely less on the stock price to appraise firm value)
    • The degree to which current earnings are a good signal of future earnings.


  • Myopic behavior, where managers underinvest to boost short-term earnings and market rationally expect this and adjust valuations accordingly, is a Nash equilibrium.
  • This positive market reaction to announced investments is not evidence that managers do not underinvest, as argued by Jensen (1986).
    • Underinvesting managers only undertake the best projects, so markets are pleased when myopic managers invest.
  • Startups with high stock prices and high investments are not evidence that managers are not myopic.
    • For startups, current earnings have little correlation with long-run success (most startups have negative earnings). The link between stock prices and underinvestment does not yet exist.
  • Corporate divestitures and breakups are evidence supporting the signal-jamming model.
    • Markets can better interpret the investments and future earnings of stand-alone companies than of conglomerates, so breakups reduce the signal-jamming inefficiencies

Managerial Incentive Problems: A Dynamic Perspective

Holmström, B., “Managerial Incentive Problems: A Dynamic Perspective,” Review of Economic Studies 66 (1999), 169-182.

Purpose: To show how a manager’s desire to build his resume and firm owners’ desire to earn financial returns can conflict or harmonize.


  • Fama (1980) posited that market forces eliminate the agency problem over time.  A manager will choose to maximize shareholder returns because, in the long run, he is concerned about his long-term career prospects.
    • Under some narrow assumptions, Fama is right, but in general, he is not.
  • When the market is uncertain about a manager’s ability, he will work harder to achieve good outcomes and look like a high-ability manager.
    • Ability changes over time and is never fully known, so managers can always substitute effort for ability.
  • Managers’ supply of effort/output in every period is less than the socially optimal level.
  • A risk-averse manager may not want to invest, since investing poorly could reveal low managerial ability, and since it cannot be proved that investments not made would have been successful.

The Basic Model:

  • Effort is unobservable and cannot be contracted, so managers in each period are paid in advance for their efforts.
  • A manager’s ability is revealed over time through the outcomes of his effort.
  • A risk-neutral market pays a manager wages w_t(y^{t-1} = E[\eta | y^{t-1}] + a_t(y^{t-1}).
    • y^{t-1} is the series of historical output through time t-1.
    • w_t is wages paid at the beginning of period t.
    • \eta represents belief about manager ability.
    • a_t \in [0,1] is the manager’s decision rule, or how much effort he gives in period t.
  • The manager maximizes the expected discounted payoff of his current and future wages, minus the current and expected disutility of effort g(a_t), where g(\cdot) is an increasing and convex function.
  • Solving these two equation gives the equilibrium wage and managerial decision rule.
  • When there is uncertainty, there are gains to effort.
    • High effort biases the market’s ability estimating process upward, and lower effort biases it downward.
  • Managerial ability changes over time, and so never becomes fully known.

Debt and Seniority: An Analysis of the Role of Hard Claims in Constraining Management

Hart, Oliver, and John Moore, “Debt and Seniority: An Analysis of the Role of Hard Claims in Constraining Management,” The American Economic Review, Vol 85, No 3 (1995), 567-585.

  • Assume that managers will always invest in every project (due to empire-building, prestige, resumé-building, etc.)
    • If managers have too much cash flow, they will invest in bad (negative NPV) projects along with good ones.
    • Debt can fix the agency problem by forcing managers to pay out cash flows before investing.
  • The debt overhang problem
    • If managers have too little cash, they will have to forgo investing in good projects.
    • If managers have too much debt, they will not risk losing their job and, again, will forgo investing in good but risky projects.
  • Long-term debt can outperform complicated contracts in solving agency problems.
    • Contracts that attempt to align manager compensation with shareholders’ interests can’t address every agency problem.
    • Long-term debt restricts managers from borrowing against future cash flows to invest in bad projects.

What do Private Equity Firms Do?

Gompers, Paul, Steven N. Kaplan, and Vladimir Mukharlyomov, “What do Private Equity Firms Do?” The American Finance Association 75th Annual Meeting, Boston (2015).

Purpose:  To describe the investment behavior of private equity firms (not including venture capital), and compare that behavior to academic theory.


  • P/E firms use internal rates of return and multiples of invested capital, rather than discounted cash flows and the CAPM, to value acquisition targets.
  • They usually look for internal rates of return between 20% and 25%,
  • P/E firms use comparable company multiples to calculate exit value, instead of discounted cash flows.
  • P/E firms choose the capital structure of their portfolio companies based on
    • The company’s industry (100% of P/E firms)
    • current interest rates (100%)
    • the tradeoff between the debt tax shield and default risk (67%)
    • the maximum amount of debt the market will buy (67%)
    • the ability of debt to force operational improvements (40%)
  • They plan to improve the operations of their acquisitions, but do so more by increasing growth than by cutting costs.
    • management incentives
      • P/E firms give 8% of company equity to the CEO, and 9% to the remaining managers and employees.
    • Governance
      • P/E firms prefer boards of directors of between 5 and 7 members, with the P/E firm supplying 3 of those.
      • 58% supply their own management teams after the acquisition.
    • Deal Selection
      • Of 100 opportunities, P/E firms deeply investigate 24 and close on 6.
      • Almost half of closed deals are “proprietary,” meaning the P/E executives sourced the deal themselves.
      • Criteria for evaluating investment opportunities, in order of importance, are
        • the business model and competitive position
        • the management team
        • the P/E firm’s ability to add value
        • the target’s valuation
    • Value Creation
      • Increasing revenue is important in 70% of deals.
      • Follow-on acquisitions are important in 50%.
      • Reducing costs is important in 36% of deals.
      • Changing company’s business model or strategy is key in 33%


  • P/E firms in the 1980s focused on cost-cutting and decreasing agency costs through very high leverage.
  • P/E firms today prefer to increase revenue and improve governance, and do not use so much leverage.
  • P/E firms are more industry-focused today than they were in the 1980s.
  • They devote significant resources to improving operations in their portfolio companies.
  • P/E firms have outperformed benchmarks for 30 years, and their executives tend to be educated at the best business schools, so their behavior likely identifies best-practices.
  • They do not use DCF valuation techniques, suggesting that DCF methods are either made redundant by IRR-based valuation, or that they are deficient.
  • P/E firms’ limited partners care more about absolute return than performance relative to a benchmark.

Data is from a survey of 79 private equity firms (64 of whom responded in full, representing $600 billion in assets).

Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure

.Jensen, Michael C. and William H. Meckling, “Theory of the Firm:  Managerial Behavior, Agency Costs and Ownership Structure,” Journal of Financial Economics, Vol 3, No 4 (1976), 305-360.

Purpose:  To show how agency costs influence the firm’s ownership structure.

Agency costs are the sum of:

  • Monitoring costs incurred by the principal
  • Bonding costs incurred by the agent (costs incurred by the manager to guarantee or to prove his fidelity)
  • The residual loss (monitoring and bonding cannot usually prevent all agency-related problems)

This paper takes a positive approach, seeking to explain contracting methods rather than recommend methods.

A “firm” is a nexus of contracts between individuals, rather than a cohesive body with clear boundaries.  Therefore, firm behavior, like market behavior, is an equilibrium outcome.

Agency costs of outside equity:

  • If an owner-manager sells a fraction of the firm, his incentive to maximize firm value declines and he will increase his perquisites, thus reducing firm value.
  • In a rational market, equity purchasers of X% of the firm will only pay X times the value they expect the firm to have once the manager increases his perquisites.
  • Firm value is lower after a sale of equity, and since the outside buyer paid a fair price given the expected reduction in firm value, the loss of wealth is entirely imposed on the owner-manager who sold the equity.
  • The manager may be forced to sell equity to raise money for investment, but his total welfare may still increase if the investment is profitable enough.
  • monitoring and bonding can increase firm value and manager wealth regardless of who (principal or agent) incurs the costs.

Agency costs of debt:

  • Consider two investments requiring the same outlay, but with different payoffs and probabilities of success.
  • The high-variance investment, with a higher expected payoff but a lower probability of success, imposes risk on the bondholders.
  • A manager can increase his wealth by promising to invest in the low-variance project, selling bonds, and subsequently investing in the high-variance project.
  • In a rational market, bondholders will expect the manager to do this, so they will not pay the full (low-variance project) price for the bonds.
  • If the manager could fully self-finance, he would choose the high-variance project.
  • If the manager cannot finance the project and must sell bonds, then the bond buyers will not pay enough to finance the high-variance project.  The agency costs are the loss of value associated with forgoing the higher-expected-value project.
  • Monitoring costs (external audits, bond covenants, etc) can sometimes put a burden on the firm.
  • If the manager can produce information for monitors more cheaply than the monitors themselves (having internally collected data simply certified by external auditors), it will be worth it to do so.  This is referred to as bonding.
  • Bankruptcy costs and reorganization costs may also be incurred if the firm cannot meet its debt obligations.


  • Managers want to spend a lot of money on perks.
  • higher leverage means managers’ equity forms a larger part of the whole, so managers’ incentives are more aligned with shareholders.
  • If all outside financing is debt, or if all is equity, agency costs one way or the other will likely be too high; there is a balancing point.
  • Inside vs. Outside Financing:
    • Inside financing avoids agency costs, but limits the manager’s ability to diversify his personal investments and limits the size of projects he can undertake.
    • Outside financing is often necessary to raise the capital needed for an investment.
    • Outside financing is often preferred by a risk-averse manager who wishes to diversify.

Debt and Taxes

Miller, Merton H., “Debt and Taxes,” The Journal of Finance, Vol 32, No 2 (1977), 261-275.

Purpose:  To argue that, even if debt repayments are tax deductible, a firm’s value is independent of its capital structure.


  • The [potential] direct bankruptcy costs of debt seem too small to be balanced by the tax savings.
    • Corporations do not have as much debt as models suggest they should have – fear of bankruptcy has to be very high for debt levels in the models to match the empirical evidence.
    • The potential indirect costs of bankruptcy are probably not very big, either.
  • Between the 1920s and the 1950s, taxes increased 400% while corporate capital structures changed very little (and change through the end of the 1970s appears unlikely).
  • Investment bankers and corporate financial officers are aware of the tax implications of debt.
  • The only explanation left is that the tax savings of debt are much smaller than researchers generally believe.

Tax Advantages of Debt:

  • If there are no taxes, there are no tax advantages.
  • If there are corporate taxes but no personal income taxes, then the tax advantage of debt is the corporate tax rate multiplied by corporate debt.
  • If there are personal income taxes, and the personal tax on share income is less than the tax on bond income, then investors will prefer that corporations use equity financing over debt financing, thus negating the tax advantages of debt.
    • Corporations holding very little debt and/or paying small dividends will be preferred by shareholders in high tax brackets.
    • Corporations with high leverage and/or high dividends will be preferred by shareholders in low tax brackets.
    • One shareholder class is as good as another, so capital structure is, again, irrelevant.