Financial Performance and Income Diversity: Does Ownership Matter? Evidence from Vietnamese Commercial Banks
Bibliographic record
Abstract
In the context of the sharp development of the Vietnamese stock market in recent years, financial performance of listed firms is drawing the attention of investors, particularly in banking industry. Moreover, the harmony of income diversity or reducing the relying on traditional activities of commercial banks is thriving in the world and strongly influence on Vietnam’s banking, especially when the outbreak of COVID-19 worldwide may result in the freeze of real estate market, which leads to devaluate collaterals as well as the risk of non-performing loans, so-called “credit shocks”. This paper, therefore, examines the impacts of income diversity on financial performance of Vietnamese commercial banks in the period from 2007 to 2019. To conduct this study, annual data are collected of 26 commercial banks, listed in Ho Chi Minh Stock Exchange (HOSE), Ha Noi Stock Exchange (HNX), UPCoM and OTC. The research develops an exploratory model reflecting financial performance of the banks in relation to their income diversity and analyzes data using panel regressions. The results show that there is no relationship between financial performance and income diversity due to its low proportion in total operating income. However, the state ownership makes stronger this relationship despite the small impacts. The findings are expected to add the gap in the existing literature, lacking of investigating the impacts of market power on bank income diversity, and the moderating role of state ownership in this relation in Vietnamese banking sector, which is ignored or opposite in most recent studies. Thereby, the paper also gives some useful implications for investors, bank managers as well as policy makers to catch up the market fluctuations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".