Evaluation of information in the statements of profit or loss and other comprehensive income of WIG30 companies
Bibliographic record
Abstract
The aim of the article is to analyze and assess the comparability of the structure and content of statements of profit or loss and other comprehensive income of listed companies included in the WIG30 index (excluding financial institutions and foreign companies) and to propose some solutions to increase comparability. 21 financial statements for 2016 were analyzed. Two research methods were used: literature review and anal-ysis of the content of financial statements. All companies presented gross profit subtotal, which classified expenses by function in the statement of profit or loss and other comprehensive income (almost 75% of analyzed companies). However, some differences were observed in the calculation of that subtotal. The majority of the companies also presented other subtotals which are not required by IAS 1 – an operating profit subtotal and profit before tax subtotal. Generally all companies classified expenses by nature in the manner prescribed in the Polish Accounting Act. IASB should consider a requirement that companies pre-sent more additional subtotals in the statement of profit or loss and other comprehensive income, and should also prescribe the way of its calculation because many companies in the world (including Poland) present such subtotals. Moreover, in order to increase comparability of this statement, it might be advisable to introduce more detailed regulations concerning classification of expenses by nature and the place of presen-tation of the item Share of result of associates and joint ventures, because as some research indicates there are inconsistencies at the global scale. The findings of this research can be useful for the IASB work on changes in the structure and content of the statement of profit or loss and other comprehensive income to increase its comparability. Moreover, in Poland, previous research concerning the structure and content of the statement of profit or loss and other comprehensive income mainly focused on other comprehensive income rather than profit or loss.
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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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".