Market effects of SEC regulation of short-term borrowing disclosure
Bibliographic record
Abstract
Purpose The purpose of this paper is to offer an alternative approach to measure the cost-benefit tradeoff, by analyzing stockholders’ reactions to the announcement and vote on the proposed rule. More specifically, the authors use event study methodology to investigate the stock price reaction on two key dates; that is, the announcement date and the voting date of the proposed short-term borrowing disclosure regulation, and argue that positive abnormal stock returns indicate that the expected benefits of the regulation outweigh the compliance costs. A negative reaction would indicate that, in the eyes of investors, the costs of compliance exceed the expected benefits. Design/methodology/approach The authors use event study analysis and apply the market model to equal-weighted portfolios of 2,450 financial and 3,985 non-financial US firms to calculate mean cumulative abnormal stock returns (MCARs, hereafter) on the announcement and voting dates. Then, the authors conduct mean difference tests on firm-level MCARs across three event windows, that is, (−30,−1), (0,+1) and (+2,+30), to confirm if the MCARs of financial firms are different from those of non-financial firms on both the announcement and the voting dates. Finally, robustness tests are performed with alternate benchmark, using value-weighted portfolios, for the market. Findings The authors find that the market reaction is positive and significant at the announcement date and negative and significant at the voting date of the proposed regulation of short-term borrowing disclosure regulation. Overall, the paper documents a positive market reaction, indicating the usefulness of the disclosure from the vantage point of users. Examining and comparing the results for various subsets, including commercial banks and saving institutions, bank holding companies, size quartiles, and exchange listed and OTC registrants, the authors find that a “one-size-fits-all” approach to regulation is undesirable. Originality/value This is first empirical study, to best of the authors’ knowledge, to explore stockholder reaction to a proposed, rather than an enforced, Securities and Exchange Commission (SEC) regulation and may contribute to the SEC’s final decision on the rule. Second, given a dissimilar reaction from investors of different firms, the results suggest that the SEC needs to reconsider its one-size-fit-all approach for the proposed rule. Finally, because the proposed disclosure would affect all SEC registrants, the economic implications of the findings are important not only for stockholders, but also for regulators, as they attempt to manage systematic risk and optimize the level of market intervention.
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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 | 0.003 |
| 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 | 0.001 | 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".