Comparative univariate model for share price prediction / Nur Sabrina Nisa Nor Amri ... [et al.]
Bibliographic record
Abstract
Axiata Group Berhad is a Malaysian incorporated telecommunications company that manages the Axiata Group. Prepaid and postpaid mobile services, television and cable television services, internet services, enterprise solutions, digital marketing and e-commerce services, mobile advertising, and machine-to-machine (M2M) solutions are one of the Axiata Group's products and services. The group also provides telecommunications infrastructure services and operates telecommunications towers on a regional scale. Holding an investment, selling telecommunications equipment and related products is one of the Group's other regular activities. The Covid-19 pandemic has a short-term impact on Axiata Group SDN BHD in profit and loss. In the post-Covidl9 world, winners and losers emerge as changes in consumer behavior drive new conditions while balancing health concerns, work responsibilities and lifestyle needs (Mandy, 2020). Axiata Group's net income for the first quarter of 2021 was 75.56 million ringgits, down 60% from 188.11 million ringgit in the year-ago quarter due to higher depreciation and lower one-time profits (Khalid, 2021). Celcom extends its support via digital online services and additional special relief activities for all customers during the Movement Control Order period, as many Malaysians would be working from home, contributing to social distancing efforts (Ullah, 2021). According to Makridakis (2014), forecasting is important to help organizations anticipate major tuture changes and their implications and better respond to these changes and the opportunities and risks associated with them. Therefore, this study is important forfinding the best model for predicting the share price of the Axiata Group tn November 2021. Monthly opening prices for shares were taken from the investing.com website from October 2010 to October 2021.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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 source (direct Gemma or distilled Codex), 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".