The Determinant of Financial Performance of Indian Public Sector Banks- A Panel Data Approach
Bibliographic record
Abstract
In the accelerated development of an economy, the role of a vibrant banking system and financial structure is considered as highly indispensable. The banking sector is recognized as an important element to portrait the financial and economic strength of a country. The economic importance of the banking system may be considered in the form of capital formation, inspiring innovation, monetization, and facilitator of monetary policy. The present research work investigates the association between banks' profitability and the banks’ specific factors of Indian Public Sector Banks. The research work is based on secondary data drawn from annual reports of banks from the period of 2015 to 2019. The panel data regression statistical technique has been employed to vindicate the influence of explanatory variables viz. Capital Adequacy, Human Capital, Liquidity, Management Efficiency, Asset Quality, and Earning Quality, which have been employed as independent variables and Return on Equity, as the dependent variable. Panel data regression model results have reported that the regression coefficients are found statistically significant and the high value of adjusted R- square expresses the overall best fit of the fixed effects model. A significant positive relationship has been found between the financial performance of bank (ROE) and human capital, liquidity, management efficiency, and asset quality. Whereas capital adequacy and earning quality of the banks have an insignificant impact on the profitability of banks. Hence, the financial performance evaluation enables the banks to analyze their financial strength and to follow necessary protective initiatives for its sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".