Profitable Investment Channels of Vietnamese Commercial Banks (2018–2024)
Bibliographic record
Abstract
The Law on Credit Institutions 2010, amended and supplemented, was applied on 15 January 2018, causing many changes in senior personnel in Vietnamese banking. The period (2018–2014) had many changes. This was also a period of many business difficulties. Four commercial banks had to carry out mandatory transfers at the request of the State Bank to ensure the development of the Vietnamese banking system in 2024. Profitable investment channels of commercial banks sometimes generate income and, at other times, suffer losses. Managers often analyze and make investment decisions by observing developments recorded on graphs and estimating the future fluctuation trends of each profitable investment channel. However, no research has been conducted on how the simultaneous implementation of all information from investment channels affects the final profit results of commercial banks. This study investigates all banking activities, from trading to investing, to consider which investment channel has a stable impact on bank profits over a long period. The S-GMM estimation method is used, due to the consideration of endogenous variables in quarterly panel data of 27 Vietnamese commercial banks from the first quarter of 2018 to the third quarter of 2024. This study provides statistical evidence indicating that all investment channels of commercial banks contribute to increased profits, except for short-term securities trading channels and capital contributions to subsidiaries. This study also reveals that economic growth and systemic risk affect commercial bank profits. Several solutions are proposed for commercial banks to develop future profitable investment channels.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".