Study of Characteristic and Period of Communication and Electronics Industry in Chinese Securities Market
Bibliographic record
Abstract
Purpose: This study aims to analyze the characteristics of communication and electronics industry in Chinese stock market and calculate the average periods of it. Design/methodology/approach: We use R/S analysis method to study the characteristics of communication and electronics industry in Chinese stock market, and use Matlab software and Eviews software to calculate some representative exponents of this industry. Findings: The results show that the probability distribution of the communication and electronics industry in the Chinese stock market is nearly non-normal distribution, but a partial distribution, showing a peak, thick tail, migraine and other features. Hurst exponent calculated shows that the communication and electronics industry in the Chinese stock market has obvious fractal characteristics, and does not follow a random walk assumption, but follows persistent trend. The average big circulation period is about 400 days; the average small circulation period is about 200 days. Research limitations/implications: We use R/S analysis method to study the characteristics including periods and venture.of communication and electronics industry in Chinese stock market. Practical implications: The average periods and related venture can give investors properly suggestions. Originality/Value: We use R/S analysis method and Matlab software and Eviews software to analyze the characteristics of communication and electronics industry in Chinese stock market which has barely been studied. The results unfold the characteristics of this industry and can give investors properly suggestions as well. Key words: Stock market; Fractal; R/S analysis; Hurst exponent; Periods; Communication and electronics
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".