Essays on search models of the capital market, and real investment options with financing constraints
Bibliographic record
Abstract
My dissertation consists of three essays in financial economics. In the first essay, I develop a search model of credit markets with suspension penalties for default. It is shown that there exists a unique search equilibrium that achieves a constrained Pareto optimum under informational asymmetry and uncertainty if the opportunity cost of search is not too large. Greater investment availability makes borrowers choosier in accepting projects, thus increasing default risk and forcing banks to raise the interest rate in equilibrium. Moreover, the suspension penalty has a favorable impact on the borrower's investment choice when the optimal interest rate is low and an unfavorable effect when the interest rate is high. The policy implication is that the bank should tighten penalty severity in the former case and loosen it in the latter so as to enhance loan repayments. In the second essay, I utilize a search model to examine how joint liability lending enhances loan repayments compared with unsecured individual liability lending. Joint liability serves as a substitute for collateral in curbing default risk, where the effective cost of borrowing is positively related to group risk type, and where a safe group is more willing than its risky counterpart to trade joint liability commitments for lower interest rates. The efficiency gain under group lending results from a risky borrower's choice of less risky investment due to the joint responsibility for default risk, and from more prudent actions taken by safe borrowers since the effective interest reduction that they obtain outweighs the effective “collateral” imposed on them by joint liability. Moreover, joint liability has a favorable incentive effect if the loan rate is low and an adverse incentive effect otherwise; moral hazard is less severe for joint liability than for loan rates if the group size is not too large. All this makes it possible for joint liability to improve efficiency by lowering loan rates. The third essay is a joint paper with Varouj Aivazian. In this essay, we examine the impact of financing constraints on the dynamics of the firm's investment choices under uncertainty. It analyzes interactions between investment and financing decisions when the firm faces demand uncertainty and has the option of adjusting its capital stock to the resolution of uncertainty. Investment options enable the firm to better tailor investment to future demand and, at the same time, they affect firm's expected tax obligations and potential bankruptcy status. In addition to allowing the firm to better adjust its capital stock to the resolution of uncertainty, these options enable the firm to realize greater tax shields from debt and reduce the potential impact of financial leverage on bankruptcy. The paper demonstrates that financing considerations can have a significant impact on these option values complicating the relationship between economic fundamentals and the dynamics of investment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".