Determinant Factor of Indonesia Banking Industry to Issued Bond in 2006-2014
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Bibliographic record
Abstract
<p>Due to global economic crisis which occured in 2008, has caused the volatility of the market and increasing the market risk. Moreover, the banking industry issued Basel III Act as a respond in order to strengthen the stability of the financial sector and prevent the negative effect on the economy from the crisis that may occur in the future. Based on Basel III Act, the banking industry is expected to meet the requirement through internal and external business activity. Furthermore, the aim of this study is to analyze which factor that determined the volume of bond issued based on internal and external factors of the company. The result shows that CAR, NIM, and BI Rate have significant effect on the volume of bond issued. span class="hps"&gt;has always been one of the most important, including, among others, calendar effects. The sell-in-May-and-go-away (also called Halloween) effect is worth considering from the point of view of assessing the portfolio management effectiveness and behavioral finance. This paper tests the sell-in-May-and-go-away strategy and its modifications on the market of 122 equity indices and 39 commodities in the eight approaches, depending on the investment time horizon (October-15<sup>th</sup> May, November-15<sup>th</sup> May, October-1<sup>st</sup> May, November-1<sup>st</sup>May) and types of computed rates of return (accrued rates of return and average daily geometric rates of return). Calculations presented in this paper indicate the presence of the sell-in-May-and-go-away effect on the analyzed markets in the classic time frame, as well as in the different time frames. ation in the country. Markets determine nominal exchange rate should prevail in the economy. The country should regulate its foreign reserve policy by setting a threshold, above which excess deposit should be plough back to the domestic economy inform of investments rather than support excessive importation.</p><p> </p>
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it