Accounting and Macroeconomic Variables Explaining Investment: An Empirical Study with Panel Data in the Portuguese Textile Sector
Bibliographic record
Abstract
This study analyses the variables that influence investment in a sample of small, labour-intensive companies in a sector that is highly dependent on external demand and the world economy. The aim is to test the three traditional theories of investment (neoclassical theory, free cash flow theory and agency theory), as well as consider the existence of other variables endogenous and exogenous to the company, in order to obtain a model that is appropriate to the reality of the companies in the sample, which consists of 3859 companies in the Portuguese textile sector, for the period from 2010 to 2022. Although there are many studies on the subject, the sample of companies used is different from the others, presenting a unique perspective for understanding investment dynamics in this type of company. The methodology used involves estimating panel data models using the GMM method. The results show that there is a statistically significant and negative relationship between liquidity and asset turnover and investment, so the free cash flow and neoclassical theories, respectively, are partially verified. The agency theory is not confirmed. Other variables are significant in explaining investment: the debt structure is statistically negative, while the size of the company, the GDP and the interest rate are statistically positive. Return on assets proved not to be statistically significant in explaining investment. To summarise, the study highlights the need for financial strategies adapted to the unique characteristics of small businesses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".