Is Taiwan a black swan phenomenon for local textile and clothing industry?A robust nonlinear regression-based model for stock exchange prediction
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
Local apparel and textile manufacturing industry in Taiwan is a sector of great importance for sustainable economicgrowth. A stock market is an effective barometer indicating the economic health of a country and Taiwan is a case evenmore special. However, is Taiwan a black swan phenomenon for local apparel and textile manufacturing industryconsidering its economic growth and financial perspectives? In addition to existing literature, this research articleprovides a new robust nonlinear regression-based model for stock exchange prediction for Taiwan stock market. Thefinancial data series used for the econometric analysis include the period from January 2000 to July 2018 for 13 mainstock markets from countries all around the globe, such as: Taiwan, Spain, Poland, Hungary, Romania, Canada, USA,Japan, Germany, France, UK, India, and China. The final multiple regression equation provides a new prediction modelfor Taiwan’s main stock market index. A sustainable economic growth in Taiwan is necessary to achieve major objectivessuch as social justice, poverty alleviation and natural environment protection. The stock market in Taiwan plays anessential role in order to stimulate economic growth and technological progress by attracting foreign investment andforeign capital. In a globalized economy, the inter-linkages between stock markets are complex and can significantlyinfluence Taiwan’s sustainable development.
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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 | 0.001 | 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