Efficiency of Australian banks: its determinants and stock price relevance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of this thesis is to conduct a thorough analysis of the performance of Australian banks over a long period of time, covering a period of various regulatory measures. To achieve this aim, the following four objectives are set in this thesis: first, to investigate economic efficiency (i.e. cost and profit efficiency) of the Australian banks before and after the implementation of the prudential regulation; second, to examine whether the Australian banks operate at the minimum efficient scale; third, to assess whether the efficiencies achieved contribute to wealth maximization of shareholders; fourth, to examine the determinants of Australian bank efficiency. Using a data set covering a period from 1985 through 2008, I first apply the stochastic frontier analysis (SFA) to examine the technical, cost and profit efficiency of Australian banks. A standard data envelopment analysis (DEA), as well as a slack-based DEA model (Tone 2001), is then used to assess the technical and scale efficiency of Australian banks. In addition, a Malmquist index model is used to investigate bank productivity changes over the sample period. The relationship between bank efficiency and bank stock returns is also examined using the market model. Lastly, a mixed two-step approach is used to examine efficiency and the determinants of efficiency using panel data from 1988 to 2008 across three countries, namely, Australia, Canada and the U.K.. In the first stage, a common efficiency frontier for banks in three countries is constructed including the environmental factors. The firm-level determinants of efficiency are then investigated by regressing these efficiencies on firm-specific factors. A key finding of this thesis is that, over the period from 1985 through 2008, the technical, cost and profit efficiency of Australian banks improved. However, scale efficiency showed a declining trend, which was mainly due to the scale inefficiency of the big-four banks over the sample period. Australian banks have a high level of cost and profit efficiency, but have a relatively low level of technical efficiency. Technological improvement is found to be the major driving force behind productivity changes of Australian banks, and also has a positive effect on the profit efficiency frontier. It is also observed that technical, cost and profit efficiency have a positive effect on bank stock returns, suggesting that bank efficiency is properly recognized by market participants. Compared to their regional counterparts, the big-four banks have a lower level of technical efficiency, but a higher level of cost efficiency. The low level of technical efficiency of the big-four banks is attributed to scale inefficiency. In comparison, the regional banks can achieve the same level of profit efficiency as that of the big-four banks by devising a better way of transforming inputs into outputs. Australian banks show a superior performance in terms of technical, cost and profit efficiency compared with that of Canadian and U.K. banks. The factors such as intangible assets, loans to deposits ratio and, loans to assets ratio exert a positive influence on technical efficiency. On the other hand, technical efficiency is inversely affected by size, ratio of loan loss provisions to total loans and debt to equity ratio. The findings of this thesis appear to provide justifications for the deregulatory measures and the prudential regulation framework introduced by the Australian regulatory bodies. Australian banks with increased efficiency levels and relatively high capital adequacy ratios demonstrated resilience to external shocks, such as the Asian financial crisis and the subprime mortgage crisis. An investigation of the determinants of bank efficiency suggests that an Australian bank manager has the choice of tuning up either the capital structure or the asset structure to improve efficiency. However, these findings should be interpreted with caution due to the limitations relating to data unavailability and efficiency evaluation techniques.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it