An Empirical Risk and Return Analysis of Select Stocks in NASDAQ 100
Bibliographic record
Abstract
Stock market indices are considered to be a powerful economic indicator. These indices can be classified based on the methodology of weight allocation for each stock and the rules governing the entry, retention and exit criteria of various stocks in the index. This paper presents a descriptive and an exploratory analysis carried out on the daily returns data of NASDAQ 100 (^NDX) index and shortlist of 20 stocks in the index. Random sampling was conducted at the sector level strata of all stocks that make up the index. This approach was followed to avoid selection bias and that stocks from the varied sectors are represented equally for this analysis. R-squared values and correlation coefficients were used to determine the explain-ability and relationship between the stock returns and the index returns respectively. The paper applied descriptive univariate analysis on daily returns at an individual stock level and at an aggregated sector level. Inter-relationship between stocks and the index returns was carried out by computing Pearson’s correlation coefficient across the different combinations of stocks and index return values. Linear regression was carried out identify the explain ability of the variance in the returns of from the index to the returns from the stocks. All analysis was carried out using the python and the stats-models library. As anticipated, the returns of randomly picked 20 stocks were able to explain ~85 % of the variance of the returns of index. One of the primary focus of the paper was to explore whether NASDAQ-100 index can explain the variability of the technology stocks relatively more than the stocks that belong to other sectors in its portfolio owing to the nature of most stocks that make up the index.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".