Key Indicators Influencing BRICS Countries' Stock Price Volatility through Classification Techniques: A Comparative Study
Bibliographic record
Abstract
The stock market is crucial for a country’s economy. It reflects the economic health and investment status of a country. While it has attracted the interest of many scholars, the volatility of stock prices and the indicators influencing this volatility has not been extensively studied, particularly using classification techniques. This study aims to fill this gap in the literature by identifying an effective classification technique to classify the data of BRICS countries using eight classification techniques via WEKA software from 2000 to 2021. Additionally, the study seeks to explore the common indicators that significantly impact stock price volatility in BRICS countries. Findings reveal that tree algorithm-based techniques performed well in terms of accuracy and reliability, although no single common classification technique was identified. Among the eight techniques, Random Tree classified the data of BRICS countries with high accuracy, except for India, where the J48 technique was more efficient. Furthermore, the study indicates that there are no common indicators affecting stock price volatility, as these indicators vary across countries due to the distinct economic and sociopolitical structures of BRICS countries. These findings provide valuable insights for investors and policymakers to better understand and manage stock market dynamics in BRICS countries.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".