Asset Price Bubble under Behaviroral Finance Theory: Based on Log-Periodic Power Law Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The asset price bubble problem is not only the most concerned topic in the financial circle, but also one of the most important research topics in the financial circle. In history, every time asset prices skyrocketed, bubbles were accumulated, and every asset price crash resulted in a massive shrinking of wealth, bankruptcy of enterprises, and economic recession. This paper is based on the theory of investor behavioral bias in behavioral finance theory, and is based on the log-periodic power law (LPPL) theory of iron ore futures, apple futures, coke futures and stock market indices that is widely used in Chinese financial markets. The market index and bitcoin price are empirically analyzed through the process of bubble accumulation, and the stock market index of China's stock market and the index of China's capital market are predicted and analyzed based on LPPL theory.
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.
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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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 it