1 / 20

(New Title)

lucio
Télécharger la présentation

(New Title)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. What Can We Learn About Capital Structure from Bond Credit Spreads?Mark J. FlanneryUniversity of FloridaStanislava (Stas) NikolovaGeorge Mason UniversityÖzde ÖztekinUniversity of FloridaPrepared for presentation at the Sixth Annual Bank Research ConferenceFDIC Center for Financial Research JFSR (New Title)

  2. Two lines of recent research initially seem quite distinct from one another: • Determinants of corporate bonds’ credit spreads • How (if) a firm selects an optimal capital structure.

  3. Bond Credit Spreads • At least some part is due to potential default losses. • Taxes may also be part • Likewise (il-)liquidity • Merton’s (1974) structural model of credit spreads • Assumes current debt level remains unchanged through maturity • Tends to estimate credit spreads that are “too small” • Collin-Dufresne and Goldstein (JF 2001) argue that mean-reverting leverage implies more realistic spreads. I.e., larger.

  4. Bases for mean reversion • Target adjustment model • Borrowing constraints limit leverage (concern for credit rating) • Pecking order does not imply mean reversion, but may imply predictability. Begin our investigation by regressing leverage on lagged CS, controlling for lagged leverage.

  5. Credit spreads correlatedwith subsequent leverage Using OLS, estimate Reverse causation would give negative coefs. ΔCS = 1%  ΔLEV = 7% LEV mean-reverts

  6. So, spreads predict future leverage.How do spreads change contemporaneously? (1) • CSi,t is the ith firm’s credit spread at the end of quarter t, • LEVi,t is the ith firm’s debt-to-assets ratio, and • Zt is a vector of control variables motivated by structural models of credit risk, as in Collin-Dufresne et al. (2001). Re-write equation (1): (2) Difference equation eliminates unobserved, bond-specific features that may affect the credit spread.

  7. How might investors form (Et(Levt+1))? • Trade-off theory • There is a target • Firms strive to reach it • Pecking order theory: Info asymmetry  effect of FINDEF on Et(LEVt+1) • Other perspectives on leverage movements have no obvious prediction about future leverage.

  8. Econometric challenges make direct estimation of capital structure models difficult. • Firm-fixed effects seem important. • Partial adjustment seems important. • How correct dynamic panel bias to adjustment speed? Examine bond credit spreads to infer what investors believe.

  9. ΔEt(LEVt+1) acc. to Trade-off: LEV*i,t+1 = βXt + εt+1 Xi,t includes size, EBIT, M/B, depreciation, fixed assets, R&D expenditures, industry’s median leverage, bond rating (the usual suspects) Re-arranged:

  10. Therefore future leverage is implied by target and current leverage. We need an estimate of LEV* from Alternatively estimate via • Fixed effects, simultaneous estimate of λ • Fixed effects, imposing λ = 1 • OLS, λ = 1

  11. ΔEt(LEVt+1) according to the Pecking Order theory (6) • DIV = cash dividends • I = net investments • ΔW = the change in working capital • C = the firm’s operating cash flow during quarter t

  12. Severe information asymmetries  firms prefer to • sell debt when FINDEFA > 0 • retire debt when FINDEFA < 0 • E.g., a non-growing firm has • (6) • Can predict future leverage by predicting FINDEFA.

  13. Data • Credit spreads: Lehman-Warga Fixed Income Database (ends in March 1998) • Industrial issuers, fixed-rate bonds • Omit call, put, ABS, maturity < 4 years, short series of observations • Firm characteristics: Compustat • “LEV” defined as interest-bearing debt over total (book or “market”) assets. • Sample: • 626 bonds issued by 246 firms, 1986 – 1998. • More than 10,000 bond-quarter observations.

  14. Strategy Estimate: “Pecking-order” Expectations “Trade-off” Expectations

  15. Tests of the Trade-off Theory (Table 4)

  16. Estimating expected FINDEFA (7) (12) Yi,t includes industry indicator, EBIT, and 1 – 4 lags produces similar results.

  17. Tests of the Pecking-order Theory (Table 6)

  18. Tests of trade-off and pecking order (Table 8)

  19. Conclusions • Credit spreads clearly reflect expected changes in future leverage. • Pricing supports both the trade-off and pecking order theories of capital structure. • Still to do: • explore effects by leverage, maturity, and bond rating. • compare results for market vs. book measures of leverage

  20. Triggering Policy Responses with Bond Spreads Goodhart’s law. Discussed in terms of leverage; but it’s a general point to keep in mind.

More Related