Bibliographic record
Abstract
The aim of the paper is to identify the relationship between blue chip stock returns and market indices using advanced machine learning models. The key objective of the study is to determine an efficient machine learning (ML) model that produces minimum error which gauging the relationship between market volatility indices, i.e., VIX, ISEE, and NASDAQ-100 and stock returns of blue-chip companies like Apple, Amazon, and IBM. In recent studies, machine learning models are generally used for the prediction of stock price movements of the company’s stocks. However, very few studies attempt to explore the relationship between market volatility indices and stock returns of blue chip or large capitalization companies using ML models. Therefore, the current research fills this research gap by analyzing the relationship between market volatility indices and the returns of blue-chip stocks like Amazon, Apple, and IBM. The study employs support vector machine (SVR), regression tree, and random forest model to assess the desired relationships. The findings suggest that the regression tree model predicts the stock returns of Amazon better, while SVR model is comparatively better in predicting the stock returns of Apple and IBM. However, the main limitation of the research is lack of consideration for additional stocks and machine learning which could have improved the generalizability of the research findings. Thus, future research studies should consider different types of stocks from different sectors and estimate the predictive capacity of various machine learning models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".