Firms’ Financing Choices and Profitability in the For-Profit Education Industry: An Empirical Survey of Education Companies Listed on the NYSE and NASDAQ Stock Markets
Bibliographic record
Abstract
This study seeks to understand financing choices and its relationships with profitability of for-profit education companies listed on NASDAQ and NYSE. This study tests the hypothesis that knowledge-based firms with higher assets intangibility used less debt in their capital structures than capital-intensive firms with higher assets tangibility. Capital structure theory is a useful framework to explain research findings. Tests were conducted on a sample consisting of education companies and capital-intensive firms listed on NASDAQ and NYSE during the period 2000-2018. Data collected by Zacks Premium database were submitted to regression analysis. Findings suggest that capital-intensive firms have higher debt ratios than education companies. Also, this study finds existence of a strong inverse relationship between profitability and the amount of debt in the capital structure of capital-intensive firms while no such relationship was found between these two variables for the education companies. This study’s findings could provide valuable information to policymakers and investors to identify suitable financing policies in the for-profit education industry. Findings can also be useful to stimulate debate on the role of equity financing to supporting growth policies of for-profit education industry. This study attempts to fill the absence of empirical studies on financial policies of for-profit education firms.Contribution/ Originality: The main contribution of this study is that it will increase knowledge about funding able to sustain for-profit education firm's growth. Furthermore, this study tries to fill the lack of studies who investigate explicitly (empirically or theoretically) funding policies of for-profit education firms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".