Exploring the Determinants of Financial Structure in the Technology Industry: Panel Data Evidence from the New York Stock Exchange Listed Companies
Bibliographic record
Abstract
This paper aims to analyze the influencing factors on the financial structure of 51 companies listed on the New York Stock Exchange, in the technology industry, from 2005–2018. The objective is to see the impact of independent company-specific variables such as company size, tangibility of assets, growth opportunity, effective tax rate, current liquidity, depreciation, stock rotation, financial return, working capital, price to book value, price to earnings ratio, as well as the impact of governance variables and macroeconomic variables such as inflation rate, interest rate, market size, gross domestic product per capita. Using panel data and multiple linear regressions, we analyze the relationship between the independent variables listed above and the dependent variables, namely the total debt ratio, the long-term debt ratio and the short-term debt ratio. The results of the analysis showed that variables such as size, tangibility, liquidity, profitability have a significant influence on the dependent variables in accordance with the theories regarding the capital structure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".