Research on the Stock Investment Value of Hong Kong Stock Connect based on Factor Analysis
Bibliographic record
Abstract
Based on the Hong Kong Stock Connect, this paper introduces a stock investment pricing method based on factor analysis, and combines it with portfolio model. A statistical method known as factor analysis is used to separate common factors from collections of data. British psychologist Spearman was the one who initially put out the idea. He modified and enhanced the portfolio model after discovering a particular link between several probable common elements., so as to form a complete set of methods for establishing stock alternative asset allocation. This paper will first introduce a method to evaluate and compare the business performance of listed companies in the industry, and apply it to the analysis data. Finally got the investment plan. In order to explain and demonstrate the methods and models discussed in this paper, this paper will take the Hong Kong Stock Connect listed companies as an example, and analyse and summarize the results. This paper will have certain reference value for investors, and promote the theoretical research of related issues more in-depth.
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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 | 0.015 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".