Listing and Value: A Cross-Country Analysis in the Energy Sector
Bibliographic record
Abstract
The study aims to fill a gap in the literature on the impact of listing on value. Most of the relevant literature analyzes the impact of listing by focusing on the financial performance of companies. The innovative aspect of this study lies in considering value as a combination of return on equity and risk profile, the latter reflected in the cost of capital. So, in this analysis value is ascertained with the ROE-ke measure. We compare listed companies vis-à-vis their unlisted peers in the energy sector. Data are extracted from Amadeus and covers the period from 2015 to 2017. The empirical investigation considers the following areas: profitability (ROE); cost of equity (ke) and value (ROE-ke). We observe statistically significant differences between listed and unlisted companies. In particular, listed companies show lower cost of equity but they also have lower profitability than unlisted companies. Furthermore, results highlight that listing has a negative impact on shareholder value: listed companies have negative ROE-ke or they register less ROE-ke if compared with unlisted peers. This research has several limitations, for example, having considered a relatively short period of time. Future developments of this work may overcome some limitations by taking into account more recent years and using additional variables such as governance, financial structure, operations in the renewable energy sector, size.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".