Predicting Year-End Financial Performance: Can Quarterly Earnings Be Used as an Indicator?
Bibliographic record
Abstract
The main objective of this study is to analyze the ability of quarterly net income performance to make a prediction of year-end (Q4) net income growth in Indonesia’s banking sector. By adopting the adaptive expectations theory, this study examines the influence of net income growth in the first quarter (NIQ1), second quarter (NIQ2), and third quarter (NIQ3) on the fourth quarter (NIQ4). The data includes 57 banking companies listed on the Indonesia Stock Exchange (IDX) from 2022 to 2023. Multiple linear regression was implemented to conduct the analysis, and is also supported by several classical assumption tests, such as normality, heteroscedasticity, and multicollinearity tests. The outcomes demonstrate that both NIQ1 and NIQ3 have a significant positive impact on NIQ4, while NIQ2, by contrast, does not show a significant relationship. The findings of this research confirm that quarterly growth patterns of banks’ net income is able to be effectively estimated based on previous quarterly performances, particularly from Q1 and Q3. Thus, the findings provide broader insights for investors and company executives in making more informed decisions using available yet accountable historical information.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".