Two essays on the impact of automation on firms' operating performance and M&A decisions, and one essay on a comparative analysis in external financing between U.S. and Japan
Bibliographic record
Abstract
This thesis includes three topics on corporate finance: (1) Automation Exposure and Operating Performance, (2) Corporate Cash Shortfalls and External Financing: US vs Japan, and (3) Automation Exposure and M&A. The first essay, "Automation Exposure and Operating Performance," finds empirical evidence that firms with increased automation exposure experience deteriorating operating performance in subsequent years. Further analysis shows that financially unconstrained firms are better positioned to mitigate this negative impact on their operating performance compared to financially constrained firms. However, unconstrained firms are drawn into an automation arms race, escalating their investments in capital, tangible assets, and R&D. This race, while aimed at maintaining a competitive edge, paradoxically leads to a greater deterioration in their financial health compared to their financially constrained counterparts. In the second essay, "Corporate Cash Shortfalls and External Financing: US vs Japan," I find supporting evidence for the funding-horizon theory on external financing in both the US and Japan. I also find that repurchase and subnormal cash holding are important motives for debt issuance, while debt reduction is an important motive for equity issuance. Japanese firms are much less likely to issue debt and equity compared to US firms. The results suggest that this difference is due to the lower leverage used by Japanese firms and the fact that Japanese firms tend to be mature with fewer cash needs for growth. In the third essay, “Automation Exposure and M&A,” I find evidence that firms with high automation exposure are more likely to be involved in M&A transactions. High automation exposure firms with high R&D, profitability, cash holding, market value, and low leverage tend to be acquirers, while firms with the reverse characteristics tend to be targets. I further find that automation facilitates M&A transactions when product similarity is high. Consistent with the findings from my first essay on the automation arms race, acquisitions for automation purposes result in a "winner's curse," where the acquiring firms face greater financial constraints and lower operating profits in the following period.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 source (direct Gemma or distilled Codex), 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".