Investment decisions and bank loan contracting
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how a firm’s investment behavior relates to its subsequent bank loan contracting. Design/methodology/approach Using a sample of US firms during the period 1992-2011, the authors examine the association between overinvestment (underinvestment) and three characteristics of bank loan contracts: loan spread, collateral requirement, and loan maturity. Findings The authors find that overinvesting firms obtain loans with higher loan spreads. Additional tests show that the effect of overinvestment on loan spreads is generally more pronounced in firms with lower reputation, weaker shareholder rights, and lower institutional ownership. The effect of overinvestment on collateral requirement is mixed, and investment efficiency has no significant relation to loan maturity. Research limitations/implications The results are subject to the following caveats. First, while the study provides empirical evidence that investment efficiency affects bank loan contracting terms, especially the cost of bank loans, the underlying theory is not well-developed. The authors leave it up to future research to provide a theoretical framework to clearly distinguish the cash flow and credit risk effects of past investment behavior from those of existing agency conflicts. Second, due to data limitation, the sample size is small, especially when the authors control for corporate governance measured by G-index and institutional ownership. Practical implications The finding that overinvestment is costly to corporations suggests that managers should consider the potential trade-offs from such investment decisions carefully. The evidence also alerts shareholders and board members to the importance of monitoring management investment decisions. In addition, the authors find that corporate governance moderates the relationship between investment decisions and cost of bank loans, suggesting that it would be beneficial to design effective governance mechanisms to prevent management from empire building and motivate managers to pursue efficient investment strategies. Originality/value First, the findings enhance understanding of the potential economic consequences of overinvestment decisions in the context of a firm’s private debt contracting. The evidence suggests that lenders perceive higher credit risk from overinvestment than from underinvestment, likely because firms squander cash in the current period by investing in (negative net present value) projects that are likely to result in future cash flow problems. Second, the study contributes to the literature on the determinants of bank loans by identifying an observable empirical proxy for uncertainty in future cash flows that increases credit risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".