Analysing the chinese stock market using the hurst exponent, fractional brownian motion and variants of a stochastic logistic differential equation
Bibliographic record
Abstract
The Chinese stock market is rapidly developing and is becoming one of the wealth management investment centres. Recent legislation has allowed wealth management products to be invested in the Chinese stock market. By taking data from St. Louis Fed and analysing the Chinese stock market using the Hurst exponent, which was calculated by using two methods, and fractional Brownian motion, it is proved that the Chinese stock market is not efficient. However, further analysis was directed to finding its equilibrium state by using logistic difference and a differential equation. To achieve more precise movement, a stochastic logistic and delayed logistic differential equation have been implemented which are driven by fractional Brownian motion. As there is no explicit solution to a delayed differential equation using the Stratonovich integral, a method of steps has been used. A solution has been obtained that gives the equilibrium state of the Chinese stock market. The following solution is only for a Hurst exponent that is higher than 1/2. If Hurst exponent is close to 1/2, then Brownian motion is an ordinary one and the equilibrium solution is different. Sensitivity analysis in that case has been conducted in order to analyse possible stationary states. The main conclusion is that the Chinese stock market is still not mature enough to be efficient, which represents a hidden risk in wealth management investing. A new approach to valuing the efficiency of the market by using nonlinear dynamics and system analysis has been proposed.
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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".