Essays on corporate risk and capital structure
Bibliographic record
Abstract
This dissertation consists of two essays and five chapters. The first essay in chapter two addresses the zero-leverage puzzle, the observation that many firms do not issue debt and thus seem to forego sizable debt benefits. Based on the trade-off theory, a firm financed with debt saves on taxes, while it faces the debt costs associated with financial distress. Firms issue debt and net a positive gain by trading off costs and benefits. However, zero-levered firms seemingly ignore significant tax advantages associated with debt financing. I propose that this behavior is due to the value in waiting to issue debt and postponing debt costs. By considering the real option of issuing debt, small and risky firms have incentives to postpone debt issuance, even when standard trade-off theory predicts that these firms should have leverage. Thus, the value of debt-free firms should include an option component whose value is derived from future debt issuance benefits. I present a simple model for a firm's optimal issuance with optimal leverage and default, and find the factors that increase the propensity to remain zero-levered: high volatility, high debt costs, low tax levels, low payout rate, and small size. I verify the factors empirically on a sample of zero-leverage (ZL) firms by estimating a survival and a choice model and an out-of-sample test on levered firms.The second essay in chapter three provides an explanation for the underleverage puzzle by relating it to volatility risk premia. As a stylized fact, many firms have lower leverage compared to what the trade-off theory predicts, in particular based on their low asset volatility. In addition, the underleverage is the highest for Investment-Grade (IG) firms. Without volatility risk, the essay empirically documents that underleverage across firms increases with volatility risk premium at the asset level. The result is the motive to present two models with stochastic asset volatility that feature optimal capital structure. With priced asset volatility risk, the models in standard trade-off settings show that a higher premium implies lower leverage; the assets' Variance Risk Premia (VRP) reduce tax benefits and increase debt costs. Empirically, the models' calibration leaves no significant underleverage patterns in the cross-section of the firms. Thus, seemingly underleveraged firms have high asset volatility risk premia relative to their low physical asset volatility, which explains their apparent underleverage. In particular, the largest proportion of the volatility is systematic for IG firms; and, consequently, VRP are the highest. This in turn leads to a lower implied leverage, close to the IG firms' empirical leverage.Chapter four reviews the literature related to the earlier chapters. Chapter five concludes with the main findings and provides venues for the future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".