ETF Prediction of Leading Southeast Asian Countries Using Different Machine Learning
Bibliographic record
Abstract
The current health crisis plays a significant role in the stock market.This study aims to investigate the impact of COVID-19 on the Southeast Asia stock market, especially in Singapore, Thailand, India, Indonesia, Malaysia, and the Philippines.For this purpose, this study considered the influence on the Exchange Traded Fund (ETF) from the date the first COVID-19 case was reported in each country and the lookback period.The collected data covered the period between 3 February 2012 and 18 March 2022.Using the method of Long-Short Term Memory RNN (LTSM) to predict ETF trading with three different levels of lookback parameters of 60, 30, and 15.In terms of Singapore and India, 60 days lookback parameters had the best performance for the whole prediction.For the Philippines and Thailand, 60 days lookback parameters predicted the best before the first COVID-19 case was confirmed in each country and 15 days lookback parameters had the best prediction during the COVID-19 period.The results illustrated that most of the six countries mentioned in this study showed that with the increase of the lookback parameters, the model predicted more accurate; however, for the individual country, the lookback parameters had some differences due to the historical stock price and the COVID-19 situation in each country.
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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 | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.008 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".